{"cells":[{"cell_type":"markdown","metadata":{"id":"sKB26q-4-X8r"},"source":["# CS224N: Hugging Face Transformers Tutorial (Winter '26)\n","Updated By: Minsik Oh\n","\n","Original Author: Ben Newman\n","\n","Thanks to Anna Goldie for feedback!\n","\n","This notebook will give an introduction to the Hugging Face Transformers Python library and some common patterns that you can use to take advantage of it. It is most useful for using or fine-tuning pretrained transformer models for your projects.\n","\n","\n","Hugging Face provides access to models (both the code that implements them and their pre-trained weights, including latest LLMs like Llama3, DBRX, etc), model-specific tokenizers, as well as pipelines for common NLP tasks, and datasets and metrics in a separate `datasets` package. It has implementations in PyTorch, Tensorflow, and Flax (though we'll be using the PyTorch versions here!)\n","\n","\n","We're going to go through a few use cases:\n","* Overview of Tokenizers and Models\n","* Finetuning - for your own task. We'll use a sentiment-classification example.\n","\n","\n","Professor spoke about a few main project types in last Thursday's lecture:\n","1. Applying an existing pre-trained model to a new application or task and explore how to approach/solve it\n","2. Implementing a new or complex neural architecture and demonstrate its performance on some data\n","3. Analyzing the behavior of a model: how it represents linguistic knowledge or what kinds of phenomena it can handle or errors that it makes\n","\n","Of these, `transformers` will be the most help for (1) and for (3). (2) involves a bit of learning curve but if you master it, you will find it very convenient to design a model based on existing ones provided by Huggingface. We won't be covering it here and please refer to [this example](https://huggingface.co/docs/transformers/en/custom_models).\n","\n","\n","\n","Here are additional resources introducing the library that were used to make this tutorial:\n","\n","* [Hugging Face Docs](https://huggingface.co/docs/transformers/index)\n","  * Clear documentation\n","  * Tutorials, walk-throughs, and example notebooks\n","  * List of available models\n","* [Hugging Face Course](https://huggingface.co/course/)\n","* [Hugging Face Examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch) You can find very similar code structures accross very different downstream tasks/models using Huggingface.\n","* [Hugging Face O'Reilly Book](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)\n","\n"],"id":"sKB26q-4-X8r"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"9EhWoZef-X8u","outputId":"1a27e5d0-c7c4-486e-b515-22c3bb30753f","executionInfo":{"status":"ok","timestamp":1770365554187,"user_tz":480,"elapsed":28218,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.0.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.3)\n","Requirement already satisfied: huggingface-hub<2.0,>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (1.3.7)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n","Requirement 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torch\n","\n","def print_encoding(model_inputs, indent=4):\n","    indent_str = \" \" * indent\n","    print(\"{\")\n","    for k, v in model_inputs.items():\n","        print(indent_str + k + \":\")\n","        print(indent_str + indent_str + str(v))\n","    print(\"}\")"],"id":"Q9th7mpc-X8v"},{"cell_type":"markdown","metadata":{"id":"qmXezUMg2idv"},"source":["## Part 0: Common Pattern for using Hugging Face Transformers\n","\n","We're going to start off with a common usage pattern for Hugging Face Transformers, using the example of Sentiment Analysis.\n","\n","First, find a model on [the hub](https://huggingface.co/models). Anyone can upload their model for other people to use. (I'm using a sentiment analysis model from [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3489963)).\n","\n","Then, there are two objects that need to be initialized - a **tokenizer**, and a **model**\n","\n","* Tokenizer converts strings to lists of vocabulary ids that the model requires\n","* Model takes the vocabulary ids and produces a prediction"],"id":"qmXezUMg2idv"},{"cell_type":"markdown","metadata":{"id":"ySLmJ0Z-oD35"},"source":["![full_nlp_pipeline.png](https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/full_nlp_pipeline.svg)\n","From [https://huggingface.co/course/chapter2/2?fw=pt](https://huggingface.co/course/chapter2/2?fw=pt)"],"id":"ySLmJ0Z-oD35"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Mcsii_O42Z8Q","executionInfo":{"status":"ok","timestamp":1770365612254,"user_tz":480,"elapsed":51521,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"colab":{"base_uri":"https://localhost:8080/","height":533,"referenced_widgets":["bde2be6d0e314c3dab58af547b23109b","22a1b6d0d8904ed4bcad06465752ef1d","295365a8da9a45c8bb790559284360bc","13c63a2faab649df8d9a0c8464d17f68","588bb055b0bf4cad945a42e36722ff2f","e0f63e18ae73405da99131e30883c1ad","20b7235b498343b3b9c58d4c68ea2867","83a29972511c40b6a41f52e3bbf25bd4","d31c1edb07be4e7c9adb40e6408ba1f3","5cc3269f519240cbad747a315a8349ab","a16997a886c847499b8ba9563db43eeb","fd6f26eea3514e73a84ecad486a23b47","3dbce6025ee94dae84ce8977b67bb5b1","a533b4acc0c844d9ae17a0f5934b5322","61929306fe854f9e927c9427b70b41b8","f7e0b65deabf4592858fb66deb1624cf","457c34146e0f4f94995e494be9e78aeb","2886922ce7d544a6ba4d3da881fc7de7","57418f607a96442784041a697745cd26","9ac8636e197040918eff227ee2280ec9","a8a3ac6eee374fb2bf8b8ad3d099b658","4fe7c6978ac04500a24ce17044384d21","faa2e1d63634430aabca206c7bfbd3ec","a04504ac764d4f72a6f48a164708f440","22363160aa9e4c3aac48e47ff870e104","be287aa960fa4092b521f53e739a848b","35616436fdd94438968988c9acea3492","b43741ceaabc46348f01f9b8bf816640","cc8dd7f23a704610a70e531cb3d652b6","daa20d1849374236938b23d4b402ed78","acab2ee9eca343099a321921dbaa2665","6a31dca8c5204887821f7ada883bb09c","d199cf9ab3ac40c5a7924fcc717f5c04","74cd170e1838451f812f138fff047d52","f64c953925af47b58bf5496b83eb44c1","99235abc31d243aa82e61335ed503e6c","3ee86056bcdb499d873f298f22eef6c6","3d8671babedd4104a460250a3db0db80","a13beb7938b54dfd9ffca0ff8d68fb2c","dd0ccac3b20f44e0a65339d3e2026c04","abe8a459070842d5954f2eed1c63cc7b","033063c8ee1444cdab1f5f9b5fdf7171","d48dca7beaff4251b0c51c08e09227de","a5bafa475cf149578b7b473fd00fbba8","58b749aca69b4298bcefea6747e217b6","612f4cd98b66457fa0f998ce2b15837c","d6b79732db034f958435db3830952df9","3b656808081e49bfa94ccbc796888d1a","c55f9b5083e04f4fa87370713d183159","51cc769ce13a4454b6d39c48072d1a74","bbb8c7875cb34c8fb236f5e275de195b","d41521f142614b12bc3747727758edf9","ea5e42efa9d04b82848fdc0b4642eb76","25c54b2035a246a1930e08da82b3d3a6","7b056f871c434bd58a22339cdfe1b6fe","217f8f27e5884022b9a99abf310ba6c0","095c9edb7c80411c89df69da261fc980","15023700789d4e6eb52710ac395b5410","534ce73059f04c63a53e012f10d40e40","2ef335e74b25491fb8a72f0ac9b61579","e5c2982dede0486489166dabc44ebe73","ddc5cda096ef492388436e493607ccd6","b16297e7ce65422e959079b2692479c3","3c5c7c55358c4d7cb223c142cb509a49","f0b926b6b1504cfa9549a77d07156264","f082a78574544ac4abc35ce460b05267","367220da47a14cc88826ed31ae9cf23a","bc6d04de3e2a4a30aa32dca78ec5b257","e9907776eb6d49d39c3bd5b86cba21f3","16c5741052ba4499bf0757c4c8229d71","773ea941925245e9b659347bf69edc5a","463893fffaa74d3c9104f7213a6809cf","fdf96b4e00414c4c87f58e58b1f7dbb4","d6fe2ea1b26143faa17919cbd2b5deda","c74889a009b947cd84216eb0c7afbda4","dfc93e5753d44891ba68406a0e7302af","bb5d616f0bfc455fa18bba21c39d3346","80560f985dfe4b7da94eebefa88f7f9b","b6b1964e68fd4bf09d122ff382b5c7a4","f81d474bd54c4e3fbbc17374510278e3","dcc873c6e19e46288268a28d21802782","bf5d9b37686f4319b37cb564c22576ee","d720258d906f43bf91e81b36f3bc1e5e","53b37bbcbe034a0d84fa54c54de250b1","abe46e6a166e443097215f49d42e9641","8230e4ac83a44694b4e1094c36f730c5","5331d99cfc254e40a5945376d85b5269","1770f01d0be241e78cf3ff825859e2a8"]},"outputId":"910a8a1f-abd6-4356-c8ba-286350d81c8b"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/687 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bde2be6d0e314c3dab58af547b23109b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/256 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fd6f26eea3514e73a84ecad486a23b47"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"faa2e1d63634430aabca206c7bfbd3ec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"74cd170e1838451f812f138fff047d52"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58b749aca69b4298bcefea6747e217b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"217f8f27e5884022b9a99abf310ba6c0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"367220da47a14cc88826ed31ae9cf23a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/393 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"80560f985dfe4b7da94eebefa88f7f9b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["RobertaForSequenceClassification LOAD REPORT from: siebert/sentiment-roberta-large-english\n","Key                             | Status     |  | \n","--------------------------------+------------+--+-\n","roberta.embeddings.position_ids | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]}],"source":["from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","# Initialize the tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(\"siebert/sentiment-roberta-large-english\")\n","# Initialize the model\n","model = AutoModelForSequenceClassification.from_pretrained(\"siebert/sentiment-roberta-large-english\")"],"id":"Mcsii_O42Z8Q"},{"cell_type":"code","execution_count":null,"metadata":{"id":"kT_zeWRBoD36","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1770365612314,"user_tz":480,"elapsed":54,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"45a0e47f-4e38-45e3-e8e9-d5054ed1e6fd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Input:\n","I'm excited to learn about Hugging Face Transformers!\n","\n","Tokenized Inputs:\n","{\n","    input_ids:\n","        tensor([[    0,   100,   437,  2283,     7,  1532,    59, 30581,  3923, 12346,\n","         34379,   328,     2]])\n","    attention_mask:\n","        tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","}\n","\n","Model Outputs:\n","SequenceClassifierOutput(loss=None, logits=tensor([[-3.7605,  2.9262]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n","\n","The prediction is POSITIVE\n"]}],"source":["inputs = \"I'm excited to learn about Hugging Face Transformers!\"\n","tokenized_inputs = tokenizer(inputs, return_tensors=\"pt\")\n","outputs = model(**tokenized_inputs)\n","\n","labels = ['NEGATIVE', 'POSITIVE']\n","prediction = torch.argmax(outputs.logits)\n","\n","\n","print(\"Input:\")\n","print(inputs)\n","print()\n","print(\"Tokenized Inputs:\")\n","print_encoding(tokenized_inputs)\n","print()\n","print(\"Model Outputs:\")\n","print(outputs)\n","print()\n","print(f\"The prediction is {labels[prediction]}\")"],"id":"kT_zeWRBoD36"},{"cell_type":"markdown","metadata":{"id":"a7jvH9haoD37"},"source":["### 0.1 Tokenizers"],"id":"a7jvH9haoD37"},{"cell_type":"markdown","metadata":{"id":"43FLbwgz-X83"},"source":["Pretrained models are implemented along with **tokenizers** that are used to preprocess their inputs. The tokenizers take raw strings or list of strings and output what are effectively dictionaries that contain the the model inputs.\n","\n","\n","You can access tokenizers either with the Tokenizer class specific to the model you want to use (here DistilBERT), or with the AutoTokenizer class.\n","Fast Tokenizers are written in Rust, while their slow versions are written in Python."],"id":"43FLbwgz-X83"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pu6L0lWG-X83","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":582,"referenced_widgets":["00d592b773394f60899a1b34f3f5368c","44d75fe2a65a45b6837e859e157403db","099e87c07ef94b94911d20b5a75c306c","e00d9906bd474b7e8cc97e838c06f744","2d936a3d0a5644869606362074d1f56a","5887016edf9f4d3e8662c4632643fe4b","df26c1577fa04d7299c1ed33402ec04a","3a29733bec574e2a825de4bf6fa1abc0","b5daf02856af4885aa304e6de39077c7","7c2f448a6a5b406a840217f40172ae8a","8598f3872b8840cdb7095d6f205c10a5","8f1b75c13a2443e08206760134684272","3cf188e643a64e1faa051ec0ce2e923f","58c85a27e8344dd2b53a1f362d78f98c","e3ec0d68fb8f4918be734f2038450b83","157de3a6a8f248d2b54564f1b8e64e8c","fd37bb0d682b46278ee9f42826b9aab3","d61c00142961433cb039072282add884","475961fce5c64dd2864fa6b9bb8a57e0","a2c23f415b0e4d138961afca2f0e0917","16e06fc1dfd44a59b732bc4ed3b60303","9194019df7694ccc92ca759abc3b2d6b","585f676bfaaa4b808784caeae139480a","6bbfcbc0d98a4844b063bddc457ac888","12c172986f92490bbf88743de548b819","f8dcc6b23422405e85c6da2dcf41f7e0","ab20c0f32e8240079add7a9faea99f1f","1b3906ca8e9e48ef906771c99dc0e5b5","c8c794d29eb74688a728bdad74159865","cd7a206d46bf4ad8b4b9e69bada78838","7f523d38f3b143a5820e3be38d3a84d8","93277f55e8d8474db66706b984ce8455","62eaa66665c44084889625edf22b5733","17b2bc912009403089f92fdd6fdd673f","40149185e6a94aa7a536de08600834ff","89f394ee474b4a4b97df49249c38f97d","5ea15326aa5542c9b93d2eaf9ca3eae4","23dcaac7989c41729d8b4ea74867272c","2a567ffc2a7b4788a742b6c349a49fef","54bde16c3ba34e748357d1ea1ff1d661","5ddb7f8249d34471970b6a8bb63122e2","20874c64854e4e93aac9e003e5b59037","306d49a63efe482ebc263a3ee727169f","872fca9509974530a111cb8e0be05778"]},"executionInfo":{"status":"ok","timestamp":1770365614938,"user_tz":480,"elapsed":2621,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"2611cb3c-1076-42c7-a45f-9cf8bbf49884"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"00d592b773394f60899a1b34f3f5368c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f1b75c13a2443e08206760134684272"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"585f676bfaaa4b808784caeae139480a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["DistilBertTokenizer(name_or_path='distilbert/distilbert-base-cased', vocab_size=28996, model_max_length=512, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, added_tokens_decoder={\n","\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","}\n",")\n","DistilBertTokenizer(name_or_path='distilbert/distilbert-base-cased', vocab_size=28996, model_max_length=512, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, added_tokens_decoder={\n","\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","}\n",")\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/465 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17b2bc912009403089f92fdd6fdd673f"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["BertTokenizer(name_or_path='distilbert/distilbert-base-cased', vocab_size=28996, model_max_length=512, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, added_tokens_decoder={\n","\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n","}\n",")\n"]}],"source":["from transformers import DistilBertTokenizer, DistilBertTokenizerFast, AutoTokenizer\n","name = \"distilbert/distilbert-base-cased\"\n","# name = \"user/name\" when loading from\n","# name = local_path when using save_pretrained() method\n","\n","tokenizer = DistilBertTokenizer.from_pretrained(name)      # written in Python\n","print(tokenizer)\n","tokenizer = DistilBertTokenizerFast.from_pretrained(name)  # written in Rust\n","print(tokenizer)\n","tokenizer = AutoTokenizer.from_pretrained(name) # convenient! Defaults to Fast\n","print(tokenizer)"],"id":"Pu6L0lWG-X83"},{"cell_type":"code","source":[],"metadata":{"id":"mUWigfZD1yIQ"},"id":"mUWigfZD1yIQ","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zrPzbBhR-X84","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365615040,"user_tz":480,"elapsed":38,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"42812c16-ca3e-452a-a39e-71460faf14a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Vanilla Tokenization\n","{\n","    input_ids:\n","        [101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n","    token_type_ids:\n","        [0, 0, 0, 0, 0, 0, 0, 0, 0]\n","    attention_mask:\n","        [1, 1, 1, 1, 1, 1, 1, 1, 1]\n","}\n","\n","[101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n","[101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n"]}],"source":["# This is how you call the tokenizer\n","input_str = \"Hugging Face Transformers is great!\"\n","tokenized_inputs = tokenizer(input_str) # https://huggingface.co/learn/nlp-course/en/chapter6/6\n","\n","\n","print(\"Vanilla Tokenization\")\n","print_encoding(tokenized_inputs)\n","print()\n","\n","# Two ways to access:\n","print(tokenized_inputs.input_ids)\n","print(tokenized_inputs[\"input_ids\"])"],"id":"zrPzbBhR-X84"},{"cell_type":"code","execution_count":null,"metadata":{"id":"E_8C6L2G-X85","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365615062,"user_tz":480,"elapsed":34,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"e74cfa9c-bc5a-4d62-c9dc-e2cb841d0c48"},"outputs":[{"output_type":"stream","name":"stdout","text":["start:                 Hugging Face Transformers is great!\n","tokenize:              ['Hu', '##gging', 'Face', 'Transformers', 'is', 'great', '!']\n","convert_tokens_to_ids: [20164, 10932, 10289, 25267, 1110, 1632, 106]\n","add special tokens:    [101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n","--------\n","decode:                [CLS] Hugging Face Transformers is great! [SEP]\n"]}],"source":["cls = [tokenizer.cls_token_id]\n","sep = [tokenizer.sep_token_id]\n","\n","# Tokenization happens in a few steps:\n","input_tokens = tokenizer.tokenize(input_str)\n","input_ids = tokenizer.convert_tokens_to_ids(input_tokens)\n","input_ids_special_tokens = cls + input_ids + sep\n","\n","decoded_str = tokenizer.decode(input_ids_special_tokens)\n","\n","print(\"start:                \", input_str)\n","print(\"tokenize:             \", input_tokens)\n","print(\"convert_tokens_to_ids:\", input_ids)\n","print(\"add special tokens:   \", input_ids_special_tokens)\n","print(\"--------\")\n","print(\"decode:               \", decoded_str)\n","\n","# NOTE that these steps don't create the attention mask or add the special characters"],"id":"E_8C6L2G-X85"},{"cell_type":"code","execution_count":null,"metadata":{"id":"tdoZ3EEU-X86","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365615095,"user_tz":480,"elapsed":31,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"b697d184-9658-42dd-d9cc-95663c2b7375"},"outputs":[{"output_type":"stream","name":"stdout","text":["Hugging Face Transformers is great!\n","-----\n","Number of tokens: 9\n","Ids: [101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n","Tokens: ['[CLS]', 'Hu', '##gging', 'Face', 'Transformers', 'is', 'great', '!', '[SEP]']\n","Special tokens mask: [1, 0, 0, 0, 0, 0, 0, 0, 1]\n","\n","char_to_word gives the wordpiece of a character in the input\n","For example, the 9th character of the string is 'F', and it's part of wordpiece 3, 'Face'\n"]}],"source":["# For Fast Tokenizers, there's another option too:\n","inputs = tokenizer._tokenizer.encode(input_str)\n","\n","print(input_str)\n","print(\"-\"*5)\n","print(f\"Number of tokens: {len(inputs)}\")\n","print(f\"Ids: {inputs.ids}\")\n","print(f\"Tokens: {inputs.tokens}\")\n","print(f\"Special tokens mask: {inputs.special_tokens_mask}\")\n","print()\n","print(\"char_to_word gives the wordpiece of a character in the input\")\n","char_idx = 8\n","print(f\"For example, the {char_idx + 1}th character of the string is '{input_str[char_idx]}',\"+\\\n","      f\" and it's part of wordpiece {inputs.char_to_token(char_idx)}, '{inputs.tokens[inputs.char_to_token(char_idx)]}'\")"],"id":"tdoZ3EEU-X86"},{"cell_type":"code","execution_count":null,"metadata":{"id":"vt5WV-6S-X87","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365615202,"user_tz":480,"elapsed":42,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"45b08ed1-2ee7-45ea-c670-5979ac459966"},"outputs":[{"output_type":"stream","name":"stdout","text":["PyTorch Tensors:\n","{\n","    input_ids:\n","        tensor([[  101, 20164, 10932, 10289, 25267,  1110,  1632,   106,   102]])\n","    token_type_ids:\n","        tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0]])\n","    attention_mask:\n","        tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])\n","}\n"]}],"source":["# Other cool tricks:\n","# The tokenizer can return pytorch tensors\n","model_inputs = tokenizer(\"Hugging Face Transformers is great!\", return_tensors=\"pt\")\n","print(\"PyTorch Tensors:\")\n","print_encoding(model_inputs)"],"id":"vt5WV-6S-X87"},{"cell_type":"code","execution_count":null,"metadata":{"id":"HI3bAzpeoD3_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365615226,"user_tz":480,"elapsed":40,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"d1b54871-de78-40d9-8c5b-7048f1207e03"},"outputs":[{"output_type":"stream","name":"stdout","text":["Pad token: [PAD] | Pad token id: 0\n","Padding:\n","{\n","    input_ids:\n","        tensor([[  101, 20164, 10932, 10289, 25267,  1110,  1632,   106,   102,     0,\n","             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","             0,     0,     0,     0,     0,     0,     0,     0,     0],\n","        [  101,  1109,  3613,  3058, 17594, 15457,  1166,  1103, 16688,  3676,\n","           119,  1599,  1103,  3676,  1400,  1146,  1105,  1868,  1283,  1272,\n","          1131,  1238,   112,   189,  1176, 17594,  1279,   119,   102]])\n","    token_type_ids:\n","        tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","         0, 0, 0, 0, 0],\n","        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","         0, 0, 0, 0, 0]])\n","    attention_mask:\n","        tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","         0, 0, 0, 0, 0],\n","        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1]])\n","}\n"]}],"source":["# You can pass multiple strings into the tokenizer and pad them as you need\n","model_inputs = tokenizer([\"Hugging Face Transformers is great!\",\n","                         \"The quick brown fox jumps over the lazy dog.\" +\\\n","                         \"Then the dog got up and ran away because she didn't like foxes.\",\n","                         ],\n","                         return_tensors=\"pt\",\n","                         padding=True,\n","                         truncation=True)\n","print(f\"Pad token: {tokenizer.pad_token} | Pad token id: {tokenizer.pad_token_id}\")\n","print(\"Padding:\")\n","print_encoding(model_inputs)"],"id":"HI3bAzpeoD3_"},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":false,"id":"iSZat-nkoD3_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365615235,"user_tz":480,"elapsed":29,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"effa6ac6-b7f3-4fea-b3fb-b3c58fad0b2b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Batch Decode:\n","['[CLS] Hugging Face Transformers is great! [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]', \"[CLS] The quick brown fox jumps over the lazy dog. Then the dog got up and ran away because she didn ' t like foxes. [SEP]\"]\n","\n","Batch Decode: (no special characters)\n","['Hugging Face Transformers is great!', \"The quick brown fox jumps over the lazy dog. Then the dog got up and ran away because she didn ' t like foxes.\"]\n"]}],"source":["# You can also decode a whole batch at once:\n","print(\"Batch Decode:\")\n","print(tokenizer.batch_decode(model_inputs.input_ids))\n","print()\n","print(\"Batch Decode: (no special characters)\")\n","print(tokenizer.batch_decode(model_inputs.input_ids, skip_special_tokens=True))"],"id":"iSZat-nkoD3_"},{"cell_type":"markdown","metadata":{"id":"JmZNLz_noD4A"},"source":["For more information about tokenizers, you can look at:\n","[Hugging Face Transformers Docs](https://huggingface.co/docs/transformers/main_classes/tokenizer) and the [Hugging Face Tokenizers Library](https://huggingface.co/docs/tokenizers/python/latest/quicktour.html) (For the Fast Tokenizers). The Tokenizers Library even lets you train your own tokenizers!"],"id":"JmZNLz_noD4A"},{"cell_type":"markdown","metadata":{"id":"6juLjnNt-X87"},"source":["### 0.2 Models\n","\n","\n","\n","\n","Initializing models is very similar to initializing tokenizers. You can either use the model class specific to your model or you can use an AutoModel class. I tend to prefer AutoModel, especially when I want to compare models, because it's easy to specify the models as strings.\n","\n","While most of the pretrained transformers have similar architecture, if you there are additional weights, called \"heads\" that you have to train if you're doing sequence classification, question answering, or some other task. Hugging Face automatically sets up the architecture you need when you specify the model class. For example, we are doing sentiment analysis, so we are going to use `DistilBertForSequenceClassification`. If we were going to continue training DistilBERT on its masked-language modeling training objective, we would use `DistilBertForMaskedLM`, and if we just wanted the model's representations, maybe for our own downstream task, we could just use `DistilBertModel`.\n","\n","\n","Here's a stylized picture of a model recreated from one found here: [https://huggingface.co/course/chapter2/2?fw=pt](https://huggingface.co/course/chapter2/2?fw=pt).\n","![model_illustration.png](https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/transformer_and_head.svg)\n","\n","\n","Here are some examples.\n","```\n","*Model\n","*ForMaskedLM\n","*ForSequenceClassification\n","*ForTokenClassification\n","*ForQuestionAnswering\n","*ForMultipleChoice\n","...\n","```\n","where `*` can be `AutoModel` or a specific pretrained model (e.g. `DistilBert`)\n","\n","\n","There are three types of models:\n","* Encoders (e.g. BERT)\n","* Decoders (e.g. GPT2)\n","* Encoder-Decoder models (e.g. BART or T5)\n","\n","The task-specific classes you have available depend on what type of model you're dealing with.\n","\n","\n","A full list of choices are available in the [docs](https://huggingface.co/docs/transformers/model_doc/auto). Note that not all models are compatible with all model architectures, for example DistilBERT is not compatible with the Seq2Seq models because it only consists of an encoder.\n"],"id":"6juLjnNt-X87"},{"cell_type":"code","execution_count":null,"metadata":{"id":"RXm1K2sF-X88","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":958,"referenced_widgets":["0ca11de3ea02439dacec05d03b7a9f62","f8a8df4387ba4dbf8498430825e950d9","7fd68e1745324e1ebe992f35b71d99c0","f55787e2aead415681142b0a83d36e94","c9333d7283eb4310842288a867c1dc90","1cadbdca2b5d4d8cab849016ebd62ebd","09472337df824a6497596b7f10f2aef1","d4558c04636043119395a330129d8e6a","bfe5f29268fb4121ab5aae996278868f","c145503929d94afe8d322df6353c036b","598fd0f2905441e2b6770c7109358b67","75bdbe0a51f0431896ea87026bb9f1be","72c873d3017e418e85070935284725a6","c50e80dfa16344fb99f1c14cb453dfaf","a6280188a8514a29b6a452102cf32d40","7d10df8e54a54ed899377bac6423349c","d46b86dd5663413ba1e3efd92da00a2a","907a32321c36465bbbf35276caf3a955","c526adbfe3a042a990c26f4ea0a1202f","da73c6461a0041e8bd8e89e315c9837d","69ba8748c61b4a8fb82e8b190c602801","1b96f0e0c0284ec584baf0a7be4b1296","c158d01be204490e880eb077e4cc21e8","ed7a12ac51b743ea892f5e0ce5a9c51a","550c78650afb47c2ac136ac0e2517a23","1f473681733146f5be4f278b47a1a327","3d4a40d434f342b38ab5fdbab6af628f","d775563fd4b74a8c91af20a8da1b7853","bf5fb7c8cb0d4bd0a707bb4265be43d8","0c19f1a3024645f6b99c6b0357974570","8956a50f76384854aa0166aee3512422","db4c09f6791540a4926ab98c4fc420c3","d4535aa2b87f444b8c320700c5d92a11","f2537934b213450f8daa1b17c7185dd9","6a5ac60798ed455cbd9cd305df57b0a4","80ec1b2f913442809a3f15bb191c10f0","6dff4ff21a654f93901b26c51e590083","2ea5a0c79d08421eac719be0ee5ff511","b1bb69bf45c0405ab6b9191c963a3d2a","f097e11c6a694bed8106d735315ae730","b2d9098481a44ba2907cf6d6c263b446","f1631b3bec3849debf02507f055e17bc","c4d144b1274c4c11bd44f38ba64b03cd","7ac423e53a284eb6aa0e75ff6e3fba3a","e5fca8723e074589a7a0f1021d63ecb0","cb58d41c5be64a8ba8a86bf1e85d06a7","5ae35f2274644d54a9824be95fd3891d","89e226d6fd5347a58069ebac5af1549e","e8be0818de42444e8fb3b82178061ae0","56d5773762fa4585bac46d8e16c55a99","f425aacf062a4b39a51a18587a4e2ead","928fd358c08f4d48a1d994b5a4398172","713208716b4443399fdbb7248205b494","fe80b047a56a43fdae6cc01fe056029f","791e97d9373c45719d1e27a90c22080d"]},"executionInfo":{"status":"ok","timestamp":1770365635728,"user_tz":480,"elapsed":20499,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"6de87995-3949-4492-8ece-a3926d1846e2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loading base model\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/465 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0ca11de3ea02439dacec05d03b7a9f62"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/263M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"75bdbe0a51f0431896ea87026bb9f1be"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c158d01be204490e880eb077e4cc21e8"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertModel LOAD REPORT from: distilbert-base-cased\n","Key                     | Status     |  | \n","------------------------+------------+--+-\n","vocab_transform.bias    | UNEXPECTED |  | \n","vocab_projector.bias    | UNEXPECTED |  | \n","vocab_layer_norm.bias   | UNEXPECTED |  | \n","vocab_transform.weight  | UNEXPECTED |  | \n","vocab_layer_norm.weight | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]},{"output_type":"stream","name":"stdout","text":["Loading classification model from base model's checkpoint\n"]},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f2537934b213450f8daa1b17c7185dd9"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-cased\n","Key                     | Status     | \n","------------------------+------------+-\n","vocab_transform.bias    | UNEXPECTED | \n","vocab_projector.bias    | UNEXPECTED | \n","vocab_layer_norm.bias   | UNEXPECTED | \n","vocab_transform.weight  | UNEXPECTED | \n","vocab_layer_norm.weight | UNEXPECTED | \n","pre_classifier.bias     | MISSING    | \n","classifier.bias         | MISSING    | \n","classifier.weight       | MISSING    | \n","pre_classifier.weight   | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5fca8723e074589a7a0f1021d63ecb0"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-cased\n","Key                     | Status     | \n","------------------------+------------+-\n","vocab_transform.bias    | UNEXPECTED | \n","vocab_projector.bias    | UNEXPECTED | \n","vocab_layer_norm.bias   | UNEXPECTED | \n","vocab_transform.weight  | UNEXPECTED | \n","vocab_layer_norm.weight | UNEXPECTED | \n","pre_classifier.bias     | MISSING    | \n","classifier.bias         | MISSING    | \n","classifier.weight       | MISSING    | \n","pre_classifier.weight   | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]}],"source":["from transformers import AutoModelForSequenceClassification, DistilBertForSequenceClassification, DistilBertModel\n","print('Loading base model')\n","base_model = DistilBertModel.from_pretrained('distilbert-base-cased')\n","print(\"Loading classification model from base model's checkpoint\")\n","model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n","model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n"],"id":"RXm1K2sF-X88"},{"cell_type":"markdown","source":["You can also initialize with random weights"],"metadata":{"id":"v_5IMvUvgEWU"},"id":"v_5IMvUvgEWU"},{"cell_type":"code","source":["from transformers import DistilBertConfig, DistilBertModel\n","\n","# Initializing a DistilBERT configuration\n","configuration = DistilBertConfig()\n","configuration.num_labels=2\n","# Initializing a model (with random weights) from the configuration\n","model = DistilBertForSequenceClassification(configuration)\n","\n","# Accessing the model configuration\n","configuration = model.config"],"metadata":{"id":"IkqSkIhKgIrZ"},"id":"IkqSkIhKgIrZ","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1opFV7Vi-X88"},"source":["We get a warning here because the sequence classification parameters haven't been trained yet.\n","\n","Passing inputs to the model is super easy. They take inputs as keyword arguments"],"id":"1opFV7Vi-X88"},{"cell_type":"code","execution_count":null,"metadata":{"id":"TDZ72k-U-X89","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365637164,"user_tz":480,"elapsed":734,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"843c5b16-38d4-4446-9b6f-bed613e328e9"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': tensor([[  101, 20164, 10932, 10289, 25267,  1110,  1632,   106,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n","\n","SequenceClassifierOutput(loss=None, logits=tensor([[ 0.1700, -0.2388]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n","\n","Distribution over labels: tensor([[0.6008, 0.3992]], grad_fn=<SoftmaxBackward0>)\n"]}],"source":["model_inputs = tokenizer(input_str, return_tensors=\"pt\")\n","\n","# Option 1\n","model_outputs = model(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask)\n","\n","# Option 2 - the keys of the dictionary the tokenizer returns are the same as the keyword arguments\n","#            the model expects\n","\n","# f({k1: v1, k2: v2}) = f(k1=v1, k2=v2)\n","\n","model_outputs = model(**model_inputs)\n","\n","print(model_inputs)\n","print()\n","print(model_outputs)\n","print()\n","print(f\"Distribution over labels: {torch.softmax(model_outputs.logits, dim=1)}\")"],"id":"TDZ72k-U-X89"},{"cell_type":"markdown","metadata":{"id":"oRRqhVUloD4C"},"source":["If you notice, it's a bit weird that we have two classes for a binary classification task - you could easily have a single class and just choose a threshold. It's like this because of how huggingface models calculate the loss. This will increase the number of parameters we have, but shouldn't otherwise affect performance."],"id":"oRRqhVUloD4C"},{"cell_type":"markdown","metadata":{"id":"rvie4gYD-X8-"},"source":["These models are just Pytorch Modules! You can can calculate the loss with your `loss_func` and call `loss.backward`. You can use any of the optimizers or learning rate schedulers that you used"],"id":"rvie4gYD-X8-"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Irxo7sDboD4C","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365639023,"user_tz":480,"elapsed":1469,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"0f0d2bc0-c3d9-440f-d77d-d854496e21c4"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(0.9183, grad_fn=<NllLossBackward0>)\n"]},{"output_type":"execute_result","data":{"text/plain":["('distilbert.embeddings.word_embeddings.weight',\n"," Parameter containing:\n"," tensor([[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n","         [-0.0091, -0.0034,  0.0093,  ...,  0.0092, -0.0051,  0.0443],\n","         [-0.0156,  0.0267,  0.0101,  ..., -0.0134, -0.0209, -0.0073],\n","         ...,\n","         [ 0.0332,  0.0378,  0.0233,  ...,  0.0012,  0.0141,  0.0296],\n","         [ 0.0048,  0.0266,  0.0072,  ...,  0.0391, -0.0212, -0.0311],\n","         [-0.0079, -0.0023,  0.0109,  ...,  0.0114,  0.0381, -0.0083]],\n","        requires_grad=True))"]},"metadata":{},"execution_count":15}],"source":["# You can calculate the loss like normal\n","label = torch.tensor([1])\n","loss = torch.nn.functional.cross_entropy(model_outputs.logits, label)\n","print(loss)\n","loss.backward()\n","\n","# You can get the parameters\n","list(model.named_parameters())[0]"],"id":"Irxo7sDboD4C"},{"cell_type":"markdown","metadata":{"id":"mpHeG1zDoD4D"},"source":["Hugging Face provides an additional easy way to calculate the loss as well:"],"id":"mpHeG1zDoD4D"},{"cell_type":"code","execution_count":null,"metadata":{"id":"S148gCyG-X8-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365639842,"user_tz":480,"elapsed":785,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"0966ee79-fe9a-4ead-a0fc-4aedb61dcfa8"},"outputs":[{"output_type":"stream","name":"stdout","text":["SequenceClassifierOutput(loss=tensor(1.0351, grad_fn=<NllLossBackward0>), logits=tensor([[ 0.1922, -0.4041]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n","\n","Model predictions: NEGATIVE\n"]}],"source":["# To calculate the loss, we need to pass in a label:\n","model_inputs = tokenizer(input_str, return_tensors=\"pt\")\n","\n","labels = ['NEGATIVE', 'POSITIVE']\n","model_inputs['labels'] = torch.tensor([1])\n","\n","model_outputs = model(**model_inputs)\n","\n","\n","print(model_outputs)\n","print()\n","print(f\"Model predictions: {labels[model_outputs.logits.argmax()]}\")"],"id":"S148gCyG-X8-"},{"cell_type":"markdown","metadata":{"id":"7Y6E3IxzoD4E"},"source":["One final note - you can get the hidden states and attention weights from the models really easily. This is particularly helpful if you're working on an analysis project. (For example, see [What does BERT look at?](https://arxiv.org/abs/1906.04341))."],"id":"7Y6E3IxzoD4E"},{"cell_type":"code","execution_count":null,"metadata":{"id":"5WzqhpquoD4E","colab":{"base_uri":"https://localhost:8080/","height":275,"referenced_widgets":["754ff08ad517475b8f741c7e00270854","d2e52c355bbb4702b310967a85ad464d","b24974e539f64a2c8edc8f7de50f1f57","64c5d86d09f74c899ae7e1561a0d1a9b","e89923edc22247f5921ac4333a51080f","e7868d486878456395643fe6820e1773","acd4c312777244baaa799f4903e36a14","90aeec50f738404eb31d631f4fabea93","920c70ce57af4309b6380ab4678e5afe","e7198cfd324b422ab965e7adfb731703","df9c93b9f86b4000a97d1a6b14fae318"]},"executionInfo":{"status":"ok","timestamp":1770365644402,"user_tz":480,"elapsed":4196,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"efa65aff-94fc-47ff-a13d-580b57ef1971"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"754ff08ad517475b8f741c7e00270854"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertModel LOAD REPORT from: distilbert-base-cased\n","Key                     | Status     |  | \n","------------------------+------------+--+-\n","vocab_transform.bias    | UNEXPECTED |  | \n","vocab_projector.bias    | UNEXPECTED |  | \n","vocab_layer_norm.bias   | UNEXPECTED |  | \n","vocab_transform.weight  | UNEXPECTED |  | \n","vocab_layer_norm.weight | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]},{"output_type":"stream","name":"stdout","text":["Hidden state size (per layer):   torch.Size([1, 9, 768])\n","Attention head size (per layer): torch.Size([1, 12, 9, 9])\n"]}],"source":["from transformers import AutoModel\n","\n","model = AutoModel.from_pretrained(\"distilbert-base-cased\", output_attentions=True, output_hidden_states=True)\n","model.eval()\n","\n","model_inputs = tokenizer(input_str, return_tensors=\"pt\")\n","with torch.no_grad():\n","    model_output = model(**model_inputs)\n","\n","\n","print(\"Hidden state size (per layer):  \", model_output.hidden_states[0].shape)\n","print(\"Attention head size (per layer):\", model_output.attentions[0].shape)     # (layer, batch, query_word_idx, key_word_idxs)\n","                                                                               # y-axis is query, x-axis is key\n","# print(model_output)"],"id":"5WzqhpquoD4E"},{"cell_type":"code","execution_count":null,"metadata":{"id":"SH_MAK-soD4F","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1770365646820,"user_tz":480,"elapsed":2402,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"6d4cb583-3213-4e21-cfc7-c55ba848f9ab"},"outputs":[{"output_type":"stream","name":"stdout","text":["['[CLS]', 'Hu', '##gging', 'Face', 'Transformers', 'is', 'great', '!', '[SEP]']\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 3700x2100 with 72 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LzzZq7j2zN3uzZdel53iY7tlPLbsuPbuuN3um5dg1973ZdTnWdl16tkVz75m92Zstuy49x4NjbfZmL/ZMy7Fr7nuz69Kz69KzLZp7zxxr07ItmnvLrsve7Lr0bAt6bp49e71l16XneDT3njnW9mbPTdVy0Nxp9LDN1q1b1a5du+jrkydP1qmnnhp9fdCgQSouLo5p5sSJE6P/77quhg8frieeeEKdOnVq1Fpra2s1ZswYffjhh9qwYYMikUi9t8fz26vnnXeeTj/9dF1xxRX1Ln/llVc0btw4vfPOOzHN++GavKpXr15auHChunTpokMOOUSPP/64unTposcee0wdOnRI9vIUDoeVnp5e77LU1FTV1NQkaUXe56eWJe/37JeWJXq2kZ969nrLkn969nrLEj3Hw3TPzXlvlujZFFqOnZ/2Zsn7PfulZYmebeSnnr3esuSfnr3eskTP8eBYm73Ziz3Tcuz8tDdL9GwSPdvHTz17vWXJPz3Tsn381LLk/Z790rJEzzai5+bZs9dblug5Hn7q2estS/RsSlO1zMnv2Kt27dpp+fLlKigoUHV1tWbMmKFRo0ZF3759+3alpqbGNPO4446r93pKSoqOPPJIFRUVNWqtV155pcaMGaMf//jH6tOnjxzHadQ8Sfr88881evTo3S4fOnSobr755kbNrqys3C1wr7jyyiu1du1aSdJtt92mU045RS+88ILS0tI0ZsyY5C5OdZvqiBEjFAqFopdVVlbqt7/9bb0/NTJ27NhkLM+T/NSy5K+evdyyRM828lPPfmpZ8nbPXm9Zoud4mO6ZvXkXeo4fLcfOT3uz5K+evdyyRM828lPPfmpZ8nbPXm9Zoud4cKzN3uzFnmk5dn7amyV6Nome7eOnnv3UsuTtnmnZPn5qWfJXz15uWaJnG9Fz8+zZ6y1L9BwPP/Xsp5Ylem6MpmqZk9+xV8OHD9ef/vQn/fWvf9Ubb7yhzMxMDRkyJPr2OXPmqFu3bklc4S4vvfSSXnnlFQ0fPtzYzKqqKoXD4d0ur6mp0Y4dO2KeV1tbq7/85S967LHHtH79ei1atEhFRUW65ZZb1KVLF/3qV78ysexGu/DCC6P/P3DgQK1cuVILFixQ586d1bp16ySurM7FF1+822XfXzN256eWJe/37JeWJXq2kZ969nrLkn969nrLEj3Hg57p2Ys903Ls/NSy5P2e/dKyRM828lPPXm9Z8k/PXm9Zoud40DN7sxd7puXY+alliZ5Nomf7+Klnr7cs+adnWraPn1qWvN+zX1qW6NlG9Nw8e/Z6yxI9x8NPPXu9ZYmeTWmyll1gLzZu3OgOGTLEdRzHzcnJcceOHVvv7SeccIJ70003NerfyM7OdpcuXdqoGa7ruh06dHAXLlzY6DnfN3ToUHfkyJG7XX7FFVe4xxxzTMzzRo0a5RYVFbnPP/+8m5GREf28X3rpJffII49s9HpNq6qqchcsWODW1NQkeyloJD+17Lre79lvLbsuPdvETz17vWXX9V/PtGyXRPfcnPZm16VnJI+f9mbX9X7PfmvZdenZJn7q2estu67/eqZlu3Cszd5Mz3bw097suvScCPRsDz/17PWWXdd/PdOyPfzUsut6v2e/tey69GwTem7ePdOyXfzUs9dbdl169htOfsd+lZSUuOFweLfLN2/e7FZXVzdqdnZ2trts2bJGzXBd173vvvvcK664wo1EIo2etdPUqVPd9PR0d8iQIe7tt9/u3n777e6QIUPc9PR09+OPP455Xrdu3dwJEya4rlt/U/jmm2/cvLw8Y+turPLycveSSy5xU1JS3JSUlOg6R44c6d5zzz1JXl3DrF+/PtlL8CQ/tOy63u/ZLy27Lj3bzA89e71l1/VPzza07Lr0vDeJ6rk57c2uS89NiZb3zA97s+t6v2e/tOy69GwzP/Ts9ZZd1z8929Cy69Lz3nCszd7suv7qmZb3zA97s+vSs0n0bC8/9Oz1ll3XPz3Tsr380LLrer9nv7TsuvRsM3puXj3b0LLr0vPe+KFnr7fsuvTclEy0HDT/XPKwTYsWLfZ4+YYNG3TkkUdq0aJFDZ519tln13u9srJSv/3tb5WVlVXv8rFjx8a0xqlTp2rixIl699131bt3b6WmpjZqniQNHjxY06ZN09///ne98sorysjIUL9+/fTkk0+qR48eMc9bvXq1unfvvtvlkUhENTU1Mc9LlBtvvFGzZ8/WpEmTdMopp0QvHzZsmG6//Xb96U9/SuLqpMzMTK1cuVJt2rSRJP34xz/WE088oQ4dOkiS1q9fr44dO6q2tjaZy/QkP7Qseb9nv7Qs0bPN/NCz11uW/NOz11uW6LkxTPXcnPdmiZ5NoeX4+WFvlrzfs19alujZZn7o2estS/7p2estS/TcGBxrszdL3umZluPnh71ZomeT6NlefujZ6y1L/umZlu3lh5Yl7/fsl5YlerYZPTevnr3eskTPjeGHnr3eskTPpjRVy5z8jrhVVVVp6dKlMX3MD29oL7zwQiNrycvL009+8hMjs76vf//+euGFF4zMOvjggzVlyhQVFhbWu/zVV1/VgAEDjPwbJrzxxht6+eWXdeSRR8pxnOjlvXv3jvn7nQiVlZVyXTf6+scff6wdO3bUe5/vvx3756WWJe/37JeWJXpujrzUs9dblvzTs9dblug5EWLtuTnvzRI9m0LL5nlpb5a837NfWpbouTnyUs9eb1nyT89eb1mi50TgWLvh/NKy5P2eadk8L+3NEj2bRM/Nj5d69nrLkn96puXmx0stS97v2S8tS/TcHNFzbPzSs9dblug5EbzUs9dblujZlKZqmZPf0aSefvppX83dqbKyUtXV1fUuy83NjWnGrbfeqosvvlirV69WJBLR2LFjtXDhQj377LN66623TC63UTZu3Ki2bdvudnl5eXm9G0sv88s6/SyRzXm9Z7+0LNEzGoa92fs929CyRM+J1pz3Zomem5Jf1ulnzblnv7Qs0TMahmNt7/dsQ8sSPScae7P3W5bs6Nkv6/QzeqbnpuKXdfoZx9re75mW0RDszd5vWaJnNAw9e79nG1qW6LkpcKxNz03BxDoDBtYBWKmiokIjR45U27ZtlZWVpfz8/HovsTrzzDP15ptvasKECcrKytKtt96qb775Rm+++aZOOumkBHwG8TnssMP09ttvR1/feUPzxBNP6KijjkrWsoBGMdmzX1qW6Bn2YW+uQ8uwAT3XoWfYgGPtOvQMv2NvrkPLsAF7cx16hg3ouQ49w+841q5Dy7ABe3MdeoYNmmPPtAwbcaxdp7n2zDO/w7cOPfRQffjhh8rPz9eAAQP2+dsgM2bMiHn+ddddp4kTJ+rRRx/VL37xCz388MNavXq1Hn/8cd17771xrXnIkCEaP358XB/bVP7yl7/o1FNP1fz58xUOh/Xggw9q/vz5+vTTTzV58uRkL0+O49T7Xv/wdfiT33r2Q8sSPaPp+a1lyR89e71liZ5tRM+J4fWeadlOfuvZDy1L9Iym57eWJX/07PWWJXq2kd969kPLkvd7pmU70XNi0DOamt9alvzRMy0jGfzWsx9alugZyUHP5nm9ZYmebeS3liV6NqGpWubkd+xVfn7+Pq904XC4CVezuzPPPFOhUEiSdNZZZxmf/+abb+rZZ5/V0KFD9ctf/lJDhgxR9+7dVVhYqBdeeEEXXHBB3LPLysoUiUTqXRbrn9lIlGOOOUazZ8/WPffco759++qDDz7QoYceqmnTpqlv377JXp5c11XPnj2j182ysjINGDBAgUAg+nbU5/WWJf/27OWWJXq2kdd79mvLkrd79nrLEj3Hg57p2Ys903LsvN6y5N+evdyyRM828nrPfm1Z8nbPXm9Zoud40DN7sxd7puXYeb1liZ4ThZ7t4/We/dqy5O2eadk+Xm9Z8m/PXm5Zomcb0XPz7NnrLUv0HA+v9+zXliV6boymatlxuVXAXjzzzDMNer+LL744wStJjuzsbM2fP1+dO3fWAQccoLFjx+rwww/X8uXL1bdvX5WVlcU0b/ny5Ro5cqQmTZqkysrK6OWu68pxHNXW1pr+FGJWU1Ojyy67TLfccou6du2a7OXsUXO/XsaDr5nZnv3QskTPtmruXzP2Zm+2LHHdjEdz/5rRszd7bu7Xy3jwNeNYm57t0dy/ZuzN3mxZ4roZj+b+NWNv9mbPzf16GQ++ZvRMz/Zo7l8zjrVp2RZ8zdib6dkefM2aX89+aFniuhmP5v4141jbmz031fWSZ37HXtl6o9dQRUVFWr58uTp37qwDDzxQr7zyig4//HC9+eabysvLi3nehRdeKNd19dRTT6ldu3ae/LMsqampeu2113TLLbckeyl71dyvl/Hga2a2Zz+0LNGzrZr714y92bua+3UzHs39a0bP3tTcr5fx4GvGsbZXcd2MXXP/mrE3e1dzv27Go7l/zdibvam5Xy/jwdeMnr2K62bsmvvXjGNtb2ru18t48DVjb/Yqrpux42vW/Hr2Q8sS1814NPevGcfa3tRk10sX2IstW7a4Dz30kLtt27bd3lZSUrLXtyVDXl6em5+fv9tLy5Yt3Y4dO7rHHnus+9RTTzVo1tKlS93a2lp39OjR7oMPPui6ruuOHz/eTU9Pd0OhkBsIBNx//OMfMa8xKyvLXbBgQcwf19Quuugid/To0cleRkx27Njhjhkzxn344YfdRYsWJXs5nuOnll3X+z37pWXXpWcb+alnr7fsuv7p2Y8tuy497w8907Nf0PK++all1/V+z35p2XXp2UZ+6tnrLbuuf3r2Y8uuS8/7Q8/szX5By/vmp5Zdl55Nomf7+Klnr7fsuv7pmZbt46eWXdf7PfulZdelZxvRc/Ps2Y8tuy4974+fevZ6y65Lz4mUiJZ55nfs1b/+9S/NmTNHv//973d7W4sWLTRlyhSVlpbq5ptvTsLq6rv11lt1991369RTT9Xhhx8uSfriiy/03nvv6Xe/+52WL1+uyy+/XOFwWJdeeuk+Z/Xo0UNr167VH//4R0nSueeeq4ceekgLFizQV199pe7du6tfv34xr3HQoEEqLi5Wr169Yv8Em1CPHj10xx136JNPPtHAgQOVlZVV7+1/+MMfkrSyOldffbVqamr0z3/+U5JUXV2to446SvPmzVNmZqauv/56jR8/XkcddVRS1+klfmpZ8n7PfmlZomcb+alnr7cs+adnr7cs0XM86JmevdgzLcfOTy1L3u/ZLy1L9GwjP/Xs9ZYl//Ts9ZYleo4HPbM3e7FnWo6dn1qW6NkkeraPn3r2esuSf3qmZfv4qWXJ+z37pWWJnm1Ez82zZ6+3LNFzPPzUs9dblujZlCZr2cgp9LDSIYcc4k6YMGGvb58wYYLbv3//JlzR3p199tnuo48+utvljz32mHv22We7ruu6Dz30kNunT5/9znIcx12/fn309ezsbHfp0qWNXuOSJUvcYcOGuWPGjHGnT5/uzp49u96LV3Tp0mWvL127dk328tzevXu7//vf/6KvP/XUU25+fr67YsUKNxKJuCNGjHCHDx+exBV6j59adl3v9+yXll2Xnm3kp5693rLr+qdnr7fsuvQcD3quQ8/e6pmWY+enll3X+z37pWXXpWcb+alnr7fsuv7p2estuy49x4Oe67A3e6tnWo6dn1p2XXo2iZ7t46eevd6y6/qnZ1q2j59adl3v9+yXll2Xnm1Ez82zZ6+37Lr0HA8/9ez1ll2Xnk1pqpY5+R17lZ2d7a5cuXKvb1+5cqWbk5PThCvau6ysLHfx4sW7Xb548WI3KyvLdd26G6fMzMz9zkrUjeO0adPcrl27uo7jRF8CgUD0v2iYnJycet/r8847z7300kujr8+cOdPt0KFDMpbmWX5q2XW93zMtm0PPsfNTz15v2XXp2SR6jh0916Fnb6Hl2PmpZdf1fs+0bA49x85PPXu9ZdelZ5PoOXb0XIe92VtoOXZ+atl16bk5oefY+alnr7fsuvRsCi3Hzk8tu673e6Zlc+g5dvRMz15Fz7HzU89eb9l16dmUpmo5aOBZ6mGplJQUrVmzRp07d97j29esWaNAINDEq9qzli1b6s0334z+KYud3nzzTbVs2VKSVF5erpycnP3OchxHjuPsdlljXXLJJRowYIBefPFFtWvXzsjMRLj66qv3eLnjOEpPT1f37t115plnRr+uTS0QCMh13ejrn332mW655Zbo63l5edq6dWsyluZZfmpZ8n7PfmlZomcb+alnr7cs+adnr7cs0XM86Ln+ZY1Fz2bQcuz81LLk/Z790rJEzzbyU89eb1nyT89eb1mi53jQc/3LGsMvLUve75mWY+enliV6Nome7eOnnr3esuSfnmnZPn5qWfJ+z35pWaJnG9Fz8+zZ6y1L9BwPP/Xs9ZYlejalqVrm5Hfs1YABA/TGG2/oyCOP3OPbX3/9dQ0YMKCJV7Vnt9xyiy6//HJNnDhRhx9+uCTpyy+/1DvvvKPHHntMkjR+/Hgdd9xx+53luq5GjBihUCgkSaqsrNRvf/tbZWVl1Xu/sWPHxrTGlStXaty4cerevXtMH9fUZs6cqRkzZqi2tla9evWSJC1atEgpKSk68MAD9cgjj+iaa67R1KlTdfDBBzf5+g466CC9+eabuvrqqzVv3jytWrVKxx9/fPTtK1euVLt27Zp8XV7mp5Yl7/fsl5YleraRn3r2esuSf3r2essSPceDnunZiz3Tcuz81LLk/Z790rJEzzbyU89eb1nyT89eb1mi53jQM3uzF3um5dj5qWWJnk2iZ/v4qWevtyz5p2dato+fWpa837NfWpbo2Ub03Dx79nrLEj3Hw089e71liZ5NaaqWOfkdezVy5Eidd955OuCAA3T55ZcrJSVFklRbW6tHHnlEDzzwgP7zn/8keZV1Lr30Uh188MH617/+Fb3R6tWrlyZPnqyjjz5aknTNNdc0aNbFF19c7/ULL7zQyBpPOOEEzZ492/M3jjt/6+fpp59Wbm6uJGnbtm369a9/rWOOOUaXXnqpzj//fP3xj3/U+++/3+Tru/7663Xeeefp7bff1rx58zR8+HB17do1+vZ33nknukGijp9alrzfs19alujZRn7q2estS/7p2estS/QcD3quQ8/e6pmWY+enliXv9+yXliV6tpGfevZ6y5J/evZ6yxI9x4Oe67A3e6tnWo6dn1qW6NkkeraPn3r2esuSf3qmZfv4qWXJ+z37pWWJnm1Ez7s0p5693rJEz/HwU89eb1miZ1OaqmXH/f7zywM/cPPNN+uee+5RTk6OioqKJEnLli1TWVmZrrvuOt17771JXqF//Pvf/9Zdd92lSy65RH379lVqamq9t59xxhlJWll9nTp10vjx43f7rZ958+bpRz/6kVavXq0ZM2boRz/6kTZt2pSUNX744Yd666231L59e/3+979XZmZm9G2jRo3Scccdp6FDhyZlbV5Fy+b4pWWJnm1Fz+b4pWc/tCzRczzo2Rx6NoeWY0fL5vilZYmebUXP5vilZz+0LNFzPOjZDL+0LPmjZ1qOHS2bQ89m0XPs6Nkcv/RMy3aiZXP80rJEz7aiZ3P80rMfWpboOR70bA49m9MULXPyO/briy++0AsvvKAlS5bIdV317NlT559/vqd+k6q0tHSPlzuOo1AopLS0tCZe0e4CgcBe3+Y4jmpra5twNXuXnZ2tt956a7cbl0mTJun000/X9u3btWzZMvXv33+vX/dEmTNnjvr06bPPr+X3zZs3T7169VIwyB+5kPzRsuT9nv3SskTPNvNDz15vWfJPz15uWaLnxqJnM+i58Wi5cfzQsuT9nv3SskTPNvNDz15vWfJPz15uWaLnxqLnxvNLy5K3e6blxvFDyxI9m0TP9vJDz15vWfJPz7RsLz+0LHm/Z7+0LNGzzejZDL/07OWWJXpuLD/07PWWJXo2oUlbdoE9mD17tltbW9vg9587d65bU1OTwBXtm+M4biAQ2OtL586d3VtvvTWmz6m5Ov/8892uXbu6Y8eOdYuLi93i4mJ37NixblFRkXvhhRe6ruu6L774ojtw4MAmX1sgEHA3bNjQ4PfPyclxly5dmsAVeZ/fWnZdejaJnu3it55p2Rwvt+y69BwPem6+vNwzLcfOby27Lj2bRM928VvPtGyOl1t2XXqOBz03X17umZZj57eWXZeeTaJnu/itZ1o2h5bt4reWXZeeTaJnu9Bz8+Xlll2XnuPht55p2Rwv99yULfOrL9ijAQMGaN26dWrTpk2D3v+oo47SrFmzon86o6mNGTNGN998s0aMGBH9jaUvvvhCzzzzjP785z9r48aNuu+++xQKhXTTTTc1+fpqamqUkZGhWbNmqU+fPk3+78fi8ccf1x//+Eedd955CofDkqRgMKiLL75YDzzwgCTpwAMP1BNPPNHka3NdV7fccku9P4OxL9XV1Qlekff5rWXJ2z37qWWJnm3jt5693LLkr5693LJEz/GgZ7Po2Qxajp3fWpa83bOfWpbo2TZ+69nLLUv+6tnLLUv0HA96NsdPLUve7pmWY+e3liV6Nome7eK3nr3csuSvnmnZLn5rWfJ2z35qWaJn29CzWX7q2cstS/QcD7/17OWWJXo2pSlbdlzXdeP+aFgrEAjoN7/5TYOvhI888ojmz5+ftBvHE088UZdddpnOOeecepe/8sorevzxx/Xhhx/queee0913360FCxYkZY1FRUV6/fXXdcghhyTl349VWVmZli1bJqlu7dnZ2UlekTR06FA5jhPTx/znP/9Rhw4dErQi7/Nby5L3e/ZbyxI928JvPXu9Zcl/PXuxZYme40HP5tFz49Fy7PzWsuT9nv3WskTPtvBbz15vWfJfz15sWaLneNCzWX5rWfJmz7QcO7+1LNFzItCzHfzWs9dblvzXMy3bwW8tS97v2W8tS/RsC3o2z289e7FliZ7j4beevd6yRM8mNGXLnPyOPfLbhpKRkaE5c+aoR48e9S5fvHixDjnkEFVUVGj58uXq3bu3KioqkrLGJ598UmPHjtVzzz2nli1bJmUNaH781rLk/Z5pGcnit5693rJEz0geejaPnpEMfmtZ8n7PtIxk8VvPXm9ZomckDz2bRctIFr+1LNEzsDd+69nrLUv0jOTwW8uS93umZSQLPZtHz0gWv/Xs9ZYlevabYLIXAG+aNGlSspcQk4KCAj355JO69957613+5JNPqqCgQJK0efNm5efnJ2N5kqR//etfWrJkiTp27KjCwkJlZWXVe/uMGTOStDLYzG8tS97vmZaRLH7r2estS/SM5KFn8+gZyeC3liXv90zLSBa/9ez1liV6RvLQs1m0jGTxW8sSPQN747eevd6yRM9IDr+1LHm/Z1pGstCzefSMZPFbz15vWaJnv+Hkd1jhvvvu089//nO9++67GjRokCRp+vTpWrBggV599VVJ0pdffqlzzz03aWs866yzkvZvA37i9Z5pGWgYr7cs0TPQUPQM2MPrPdMy0DBeb1miZ6ChvN4zLQMNR8+AHbzeskTPQEN5vWdaBhqOngE7eL1liZ79xnFd1032IgATli9frn//+99auHChJKlXr1667LLL1KVLl+QuDEDM6BmwAy0D9qBnwB70DNiBlgF70DNgD3oG7EDLgD3oGbAHPQN2oGWYxMnvQBP76quv9M0330iSevfurQEDBiR5RQDiQcuAPegZsAc9A3agZcAe9AzYgZYBe9AzYA96BuxAy4A96BmwBz37QzDZCwBMeemll3TGGWcoMzMz2UvZow0bNui8887TpEmTlJeXJ0kqKSnR8ccfr5deeklt2rRJ7gIBD/Fyz7QMNJyXW5boGYgFPQP28HLPtAw0nJdblugZiIWXe6ZlIDb0DNjByy1L9AzEwss90zIQG3oG7ODlliV69ptAshcAmHLFFVdo06ZNu/2/V/z+97/X9u3bNW/ePG3ZskVbtmzR3LlzVVpaqj/84Q/JXh7gKV7umZaBhvNyyxI9A7GgZ8AeXu6ZloGG83LLEj0DsfByz7QMxIaeATt4uWWJnoFYeLlnWgZiQ8+AHbzcskTPfuO4rusmexFAvLp166ZjjjlGgwcP1rXXXqt58+apoKBAubm5mjVrloqKipK9xKgWLVpowoQJGjRoUL3Lv/jiC/3oRz9SSUlJchYGeIRfeqZlYN/80rJEz8D+0DNgD7/0TMvAvvmlZYmegf3xS8+0DOwfPQN28EvLEj0D++OXnmkZ2D96Buzgl5YlevabYLIXADTGc889p6lTp+qtt95SRUWFjjrqKA0fPlw1NTXaunVrspdXTyQSUWpq6m6Xp6amKhKJJGFFgLf4pWdaBvbNLy1L9AzsDz0D9vBLz7QM7JtfWpboGdgfv/RMy8D+0TNgB7+0LNEzsD9+6ZmWgf2jZ8AOfmlZome/4ZnfYY38/Hw9++yzmjNnjkaNGiXHcVRUVKRjjz1Wjz/+eLKXpzPPPFMlJSV68cUX1bFjR0nS6tWrdcEFFyg/P1+vv/56klcIeIeXe6ZloOG83LJEz0As6Bmwh5d7pmWg4bzcskTPQCy83DMtA7GhZ8AOXm5ZomcgFl7umZaB2NAzYAcvtyzRs9/wzO/wtcGDB2vIkCE6+uijFYlE1L9/f51++um699579fnnn2vTpk36+OOPk71MSdK//vUvnXHGGerSpYsKCgokScXFxerTp4+ef/75JK8OSD6/9EzLwL75pWWJnoH9oWfAHn7pmZaBffNLyxI9A/vjl55pGdg/egbs4JeWJXoG9scvPdMysH/0DNjBLy1L9Ow3PPM7fO3VV1/VtGnT9Omnn+qLL75Q7969deaZZ+r+++/X7Nmz1aNHj2QvsR7XdTVhwgQtWLBAknTQQQdp2LBhSV4V4A1+6pmWgb3zU8sSPQP7Qs+APfzUMy0De+enliV6BvbFTz3TMrBv9AzYwU8tS/QM7IufeqZlYN/oGbCDn1qW6NlPOPkd1sjPz9d9992nGTNm6N///rdSU1N1xBFH6Nhjj9WoUaOSsqaWLVtq0aJFat26tS655BI9+OCDysnJScpaAD/xWs+0DMTHay1L9AzEi54Be3itZ1oG4uO1liV6BuLltZ5pGYgfPQN28FrLEj0D8fJaz7QMxI+eATt4rWWJnv0skOwFACaddNJJevjhh5Wenq7x48frsssu04YNG5K2nurqapWWlkqSnnnmGVVWViZtLYDfeKlnWgbi56WWJXoGGoOeAXt4qWdaBuLnpZYlegYaw0s90zLQOPQM2MFLLUv0DDSGl3qmZaBx6Bmwg5dalujZz4LJXgBgyiOPPKLWrVtHX+/QoYMGDx6s8847L2lrOuqoo3TWWWdp4MCBcl1Xf/jDH5SRkbHH933qqaeaeHWAd3mtZ1oG4uO1liV6BuJFz4A9vNYzLQPx8VrLEj0D8fJaz7QMxI+eATt4rWWJnoF4ea1nWgbiR8+AHbzWskTPfsbJ77DG//t//y/6//Pnz1fHjh2TuJo6zz//vB544AEtXbpUjuNo27Zt/HYQ0ABe65mWgfh4rWWJnoF40TNgD6/1TMtAfLzWskTPQLy81jMtA/GjZ8AOXmtZomcgXl7rmZaB+NEzYAevtSzRs585ruu6yV4EYMLIkSN1xx13qGXLlsleyh517dpV06dPV6tWrZK9FMDzvNwzLQMN5+WWJXoGYkHPgD283DMtAw3n5ZYlegZi4eWeaRmIDT0DdvByyxI9A7Hwcs+0DMSGngE7eLlliZ79JpDsBQCN8e2330b//z//+Y/KysokSX379lVxcXGylrVHy5cv3+2GsaSkJDmLATzILz3TMrBvfmlZomdgf+gZsIdfeqZlYN/80rJEz8D++KVnWgb2j54BO/ilZYmegf3xS8+0DOwfPQN28EvLEj37DSe/w9cOPPBAFRYW6vzzz1dlZWX0BnHFihWqqalJ8urq++tf/6qXX345+vo555yjli1bqlOnTpo9e3YSVwZ4g196pmVg3/zSskTPwP7QM2APv/RMy8C++aVliZ6B/fFLz7QM7B89A3bwS8sSPQP745eeaRnYP3oG7OCXliV69htOfoevlZSU6L///a8GDhyoSCSi4cOHq2fPnqqqqtL777+v9evXJ3uJUY899pgKCgokSePHj9f48eP13nvv6dRTT9V1112X5NUByeeXnmkZ2De/tCzRM7A/9AzYwy890zKwb35pWaJnYH/80jMtA/tHz4Ad/NKyRM/A/vilZ1oG9o+eATv4pWWJnv3GcV3XTfYigHjt2LFDGRkZkqT8/Hx99dVXWrt2rYYNG6Y+ffpo3rx5Kigo0MKFC5O8UikjI0OLFi1SQUGBrrzySlVWVurxxx/XokWLdMQRR2jr1q3JXiKQVH7pmZaBffNLyxI9A/tDz4A9/NIzLQP75peWJXoG9scvPdMysH/0DNjBLy1L9Azsj196pmVg/+gZsINfWpbo2W+CyV4A0Bh5eXnq37+/Bg8erOrqau3YsUODBw9WMBjUyy+/rE6dOunLL79M9jIl1d14FxcXq6CgQO+9957uuusuSZLruqqtrU3y6oDk80vPtAzsm19alugZ2B96Buzhl55pGdg3v7Qs0TOwP37pmZaB/aNnwA5+aVmiZ2B//NIzLQP7R8+AHfzSskTPfsPJ7/C11atXa9q0afr0008VDoc1cOBADRo0SNXV1ZoxY4YOOOAAHXPMMclepiTp7LPP1vnnn68ePXpo8+bNOvXUUyVJM2fOVPfu3ZO8OiD5/NIzLQP75peWJXoG9oeeAXv4pWdaBvbNLy1L9Azsj196pmVg/+gZsINfWpboGdgfv/RMy8D+0TNgB7+0LNGz3ziu67rJXgRgQn5+vj7++GN98803uuiii9S+fXutX79ehx9+uCZPnpzs5ammpkYPPvigiouLNWLECA0YMECS9MADDygnJ0e//vWvk7xCwDu83DMtAw3n5ZYlegZiQc+APbzcMy0DDeflliV6BmLh5Z5pGYgNPQN28HLLEj0DsfByz7QMxIaeATt4uWWJnv2Gk99hjfz8fM2ePVudO3dWTk6OZs+erczMTE2ePFnnnntuspcHIAb0DNiBlgF70DNgD3oG7EDLgD3oGbAHPQN2oGXAHvQM2IOeATvQMkzi5HdYo7i4WJ06dVIgEFCfPn307rvvqqCgINnLqmfx4sWaOHGiNmzYoEgkUu9tt956a5JWBXiP13umZaBhvN6yRM9AQ9EzYA+v90zLQMN4vWWJnoGG8nrPtAw0HD0DdvB6yxI9Aw3l9Z5pGWg4egbs4PWWJXr2E05+B5rI//3f/+nyyy9X69at1b59ezmOE32b4ziaMWNGElcHoKFoGbAHPQP2oGfADrQM2IOeATvQMmAPegbsQc+AHWgZsAc9A/agZ3/h5HegiRQWFuqKK67QDTfckOylAGgEWgbsQc+APegZsAMtA/agZ8AOtAzYg54Be9AzYAdaBuxBz4A96NlfOPkdaCK5ubmaNWuWioqKkr0UAI1Ay4A96BmwBz0DdqBlwB70DNiBlgF70DNgD3oG7EDLgD3oGbAHPftLINkLAJqLn//85/rggw+SvQwAjUTLgD3oGbAHPQN2oGXAHvQM2IGWAXvQM2APegbsQMuAPegZsAc9+0sw2QsAmovu3bvrlltu0Weffaa+ffsqNTW13tv/8Ic/JGllAGJBy4A96BmwBz0DdqBlwB70DNiBlgF70DNgD3oG7EDLgD3oGbAHPfuL47qum+xFAM1B165d9/o2x3G0bNmyJlwNgHjRMmAPegbsQc+AHWgZsAc9A3agZcAe9AzYg54BO9AyYA96BuxBz/7Cye8AAAAAAAAAAAAAAAAAAAAAAM8LJHsBAAAAAAAAAAAAAAAAAAAAAADsTzDZCwCak2+//Vbjxo3TqlWrVF1dXe9to0ePTtKqAMSKlgF70DNgD3oG7EDLgD3oGbADLQP2oGfAHvQM2IGWAXvQM2APevYPTn4HmsiHH36oM844Q0VFRVqwYIH69OmjFStWyHVdHXroocleHoAGomXAHvQM2IOeATvQMmAPegbsQMuAPegZsAc9A3agZcAe9AzYg579JZDsBQDNxY033qhrr71WX3/9tdLT0/Xaa6+puLhYxx13nH7+858ne3kAGoiWAXvQM2APegbsQMuAPegZsAMtA/agZ8Ae9AzYgZYBe9AzYA969hfHdV032YsAmoOcnBzNmjVL3bp1U35+vqZOnarevXtr9uzZOvPMM7VixYpkLxFAA9AyYA96BuxBz4AdaBmwBz0DdqBlwB70DNiDngE70DJgD3oG7EHP/sIzvwNNJCsrS9XV1ZKkDh06aOnSpdG3bdq0KVnLAhAjWgbsQc+APegZsAMtA/agZ8AOtAzYg54Be9AzYAdaBuxBz4A96NlfgsleANBcHHnkkZo6daoOOuggDR8+XNdcc42+/vprjR07VkceeWSylweggWgZsAc9A/agZ8AOtAzYg54BO9AyYA96BuxBz4AdaBmwBz0D9qBnf3Fc13WTvQigOVi2bJnKysrUr18/lZeX65prrtGnn36qHj16aPTo0SosLEz2EgE0AC0D9qBnwB70DNiBlgF70DNgB1oG7EHPgD3oGbADLQP2oGfAHvTsL5z8DkUiEa1Zs0Y5OTlyHCfZy7FSbW2tPvvsM/Xu3Vt5eXnJXk6z5bqutm/fro4dOyoQCCR7OQlBz4lFy95he8+0nHj07B30jMaiZ2+gZTQWLXsHPaOx6Nk76BmNQcveQctoLHr2DnpGY9GzN9jeskTPiUbL3mF7z7ScePTsHfSMxqJnb4il5WATrQketmbNGhUUFCR7GUCTKS4u1gEHHJDsZSQEPaO5sbVnWkZzRM+AHWgZsAc9A/agZ8AOtAzYg54BO9jaskTPaH5s7ZmW0RzRM2CHhrTMye9QTk6OJOnYzJ8p6KQamxsprzA2K6H88NtQfvkDDQn4Wj4xf5qxWdvLIhowaGP0Om+jnZ/bcS3OVdBJMza3dtt2Y7N2CnbuZHxmeNVq4zOdlBSj80rPHmB0niSllkaMz3QScLuTNW+dsVnhSLUmrXnS2p53fl7HOKcb3ZsVqTU3C+YlYB8NZKQbn/narC+Mzisti6jw0BXW9zwk7SdGe3arqozN2imQlWl8ZmRHAtaZZvZurJNp/vN2Ug3edu8UMj8z/LC5/T5cUa2p59q/Nw9tO0LBgLnj7PC69cZm7ZTSto3xmaqsND7Sra42Oi/QoZ3ReZIU2bDJ+MzwgO7GZ6462ex+H6ms1Kq777S+5yEpZ5jdm8NhY7PQfL2+6Guj85rLsfZxef/P6ONgke1lxmbt5ITMrW+nSMUO4zONPw6diMfeE/CYVbBrZ+MzD3pulbFZ1eU1evzkd6xv+RgNV1Dm9mYnFDI2ayfTx7CSlJKTbXymk9/C6Lzwym+NzkuYBNzmjJrzpdF55WURDT9qrf09m35cOwECWRnmh9bUGB/p1prd99yw+TXKMf/MqqZ/NidJv/5ivrFZFWW1unTIAmtblr53DknI7OPagXYJeNyqJgH3x9PM34a5W0qMznNyzV//ajduNj7TSTF/G7HobwcamxXZUak1191rbc/Rx7UP+LXZx7WL1xibtZMTTEB3idj3DAu2a2t8ZqRtvvGZK39s9j6GJLWdbvb7Ew5X6suJ91jfs/HzOxPw+FIgMwHH2rXeP9fFDSdgjQk41lbfbsZHuqO2GZsVy8+cOfkd0T+FEXRSzf6QwPH+QYQkf5z8ruZ78ntOTgIeMPHF9zw+u3pOM9qzk4AHKYMB8z98UALW6ThmH2BLSTV/Imow1R8nvyfie25rz/X3ZoPX60QcGMOcRJz8bnAv2Ck3AXuzRM+xch3zt/2JuL5EErJOs/u9Y/AB310zE/AD3gSsU1nm93vrWw6kGf0hQSKOX1MScV1JQMuu4atKIAHHmpEE3C4qaP7+QCDd/EypGfRsfG+28+uFpsWxdmwS9ThYJCGPLyXiWDsRv3Tjg5PfE/BYeSIeswplJ+J6ZHnLMrs3J+Ixbdcxf/1LScDtg2P6Ou3xk5ijEtBINntzTBL2uHYCJOJxsERcB00/Bmj6vrikxJz8bvhnc5KUmWN+pq0tS4k7hyQRj98oYP57qwQ8vusavt0xvt8rMcdPieg5EU/8ZGvP9R/XNnidSch1JRHdGR9pnNGfN3wnkmL+9iElAY9BB1MTcPutZtCz8fM7zT++lJhjbR+c/J6Ix+oScY5Pivme3SzzT6LVkJY5AwoAAAAAAAAAAAAAAAAAAAAA4Hmc/A4AAAAAAAAAAAAAAAAAAAAA8DxOfjds6NChchxHjuNo1qxZTfpvd+nSJfpvl5SUNOm/DdiIngE70DJgD3oG7EHPgB1oGbAHPQP2oGfADrQM2IOeAXvQM2AHWgbswMnvCXDppZdq7dq16tOnT/Sy1157TUOHDlWLFi2UnZ2tfv366Y477tCWLVskSWPGjFFeXt5eZ27cuFGXX365OnfurFAopPbt2+vkk0/WJ598En2fL7/8Uq+99lrCPi+gOaJnwA60DNiDngF70DNgB1oG7EHPgD3oGbADLQP2oGfAHvQM2IGWAf/j5PcEyMzMVPv27RUMBiVJN998s84991wNGjRI7777rubOnav7779fs2fP1nPPPdegmT/96U81c+ZMPfPMM1q0aJHGjRunoUOHavPmzdH3adOmjVq2bJmQzwlorugZsAMtA/agZ8Ae9AzYgZYBe9AzYA96BuxAy4A96BmwBz0DdqBlwP+CyV6A7b744gv95S9/0T/+8Q9deeWV0cu7dOmik046qUF/vqKkpERTpkzRpEmTdNxxx0mSCgsLdfjhhydq2QD2gJ4BO9AyYA96BuxBz4AdaBmwBz0D9qBnwA60DNiDngF70DNgB1oG/Ilnfk+wF154QdnZ2briiiv2+PZ9/SmMnbKzs5Wdna033nhDVVVVjV5TVVWVSktL670A2D96BuxAy4A96Bmwh9d6pmUgPl5rWaJnIF70DNjDaz3TMhAfr7Us0TMQL3oG7OG1nmkZiI/XWpboGWgITn5PsMWLF6uoqEipqalxzwgGgxozZoyeeeYZ5eXlafDgwbrppps0Z86cuObdc889atGiRfSloKAg7rUBzQk9A3agZcAe9AzYw2s90zIQH6+1LNEzEC96BuzhtZ5pGYiP11qW6BmIFz0D9vBaz7QMxMdrLUv0DDQEJ78nmOu6Rub89Kc/1Zo1azRu3DidcsopmjRpkg499FCNGTMm5lk33nijtm3bFn0pLi42skbAdvQM2IGWAXvQM2APr/VMy0B8vNayRM9AvOgZsIfXeqZlID5ea1miZyBe9AzYw2s90zIQH6+1LNEz0BCc/J5gPXv21LJly1RTU9PoWenp6TrppJN0yy236NNPP9WIESN02223xTwnFAopNze33guA/aNnwA60DNiDngF7eK1nWgbi47WWJXoG4kXPgD281jMtA/HxWssSPQPxomfAHl7rmZaB+HitZYmegYbg5PcEO//881VWVqZHHnlkj28vKSmJe/bBBx+s8vLyuD8eQGzoGbADLQP2oGfAHvQM2IGWAXvQM2APegbsQMuAPegZsAc9A3agZcCfgslegO2OOOIIXX/99brmmmu0evVq/eQnP1HHjh21ZMkSPfbYYzrmmGN05ZVXSpJqa2s1a9aseh8fCoXUtm1b/fznP9cll1yifv36KScnR9OnT9ff/vY3nXnmmUn4rIDmiZ4BO9AyYA96BuxBz4AdaBmwBz0D9qBnwA60DNiDngF70DNgB1oG/ImT35vAX//6Vw0cOFAPP/ywHnvsMUUiEXXr1k0/+9nPdPHFF0ffr6ysTAMGDKj3sd26ddO8efN0xBFH6IEHHtDSpUtVU1OjgoICXXrppbrpppua+tMBmjV6BuxAy4A96BmwBz0DdqBlwB70DNiDngE70DJgD3oG7EHPgB1oGfAfTn5vIuecc47OOeecvb59xIgRGjFixF7ffs899+iee+5JwMqA/8/efYdHVSXuA3/vnd7Se0ghJoTe0RUbCIq9rG1/VnRtq35d17Whq+K6LOoq6hbb7lqwK2JZRRQUELAhXXpCSQIJ6T1T7/39MWQwEhDCuWRy8n6eh+fJTCZvbkjee88998wMHSr2mUgO7DKRPNhnInmwz0RyYJeJ5ME+E8mDfSaSA7tMJA/2mUge7DORHNhlop5F7e4NkNEzzzwDt9uNtWvXHtHvO2jQIJx++ulH9HsSyY59JpIDu0wkD/aZSB7sM5Ec2GUiebDPRPJgn4nkwC4TyYN9JpIH+0wkB3aZqOfjK78L9vrrr6OtrQ0AkJ2dfUS/95w5cxAIBAAAMTExR/R7E8mIfSaSA7tMJA/2mUge7DORHNhlInmwz0TyYJ+J5MAuE8mDfSaSB/tMJAd2mUgOXPwuWGZmZrd975ycnG773kQyYp+J5MAuE8mDfSaSB/tMJAd2mUge7DORPNhnIjmwy0TyYJ+J5ME+E8mBXSaSAxe/U4TSJx2KySYsz1RRLSyrXaiuTnimERSzRWieHvALzetJXmoYKizL2xwEsFtYXlTTdEDRhcWpgwqFZUW0tAmPNB2VKzxTd9mF5sXP2SA0DwB0f0B4phFKbh4uLCvk8wJPC4uLWiX3joJqF/c3mDdtpbCsdprPJzzTELq4faJhDNhGrbVVeGbfudcKzdPavACmCs2MRnogCF1RhOWpLpewrHaKW3ymyWoVnqm3iR1DhGp7xjmGOS1VeKbHGhSWFegl5yxaYyM0RfzftUhaTa34UJNJeKRiFjslFYoVvw9DSZnwSGup+H1OzACxf5Oh1h4yvjtMitkERRH4d2hAT9S5icIz9bMahGdqLS3CM3urcddeJzQvGPACeFBoZjTSg0Hoiiou0IA+G3Fu1iP0hHNxAKGd5cIzz4xdJSyrRdXwD2Fp0avk/qPFzoNNXyMsq50eEHcO1S48LyKWWlsvNM+UmiI0DwBClVXCM1WbuGuc7X4z8w9C80JeL4B7hWZGI3OfNJhVcb+P0C7x1/aUdPF/197ceOGZjs2VQvO0GvHnpFpLzxjn3DNzsrCskK93dBkA9JAOXdG6ezMOqGlMH+GZrrkGjCNCYv8f9aYmoXnhUPHjdyPOCGwx4uauQubeMQ+m1TUIndc2YtylxIp/BWzFIXatBwDoote5qALnM9ojaxqFZ9pq44RnmttCYgODgvOilJKeKnR9p7KtVFhWOz0o/txZNeA6NgReuwcAvalZaJ5R1OKdwjOrvUnCskK+g98vit+DEhEREREREREREREREREREREREREREREJxsXvRERERERERERERERERERERERERERERBT1uPidiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKIeF78TERERERERERERERERERERERERERERUdTj4nciIiIiIiIiIiIiIiIiIiIiIiIiIiIiinpc/E5EREREREREREREREREREREREREREREUY+L36PU5MmTcd555+1z/8KFC6EoCurr64/4NhFR17DPRPJgn4nkwC4TyYN9JpIH+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwT4TdR8uficiIiIiIiIiIiIiIiIiIiIiIiIiIiKiqMfF7z3Y1KlTMXz48A73PfXUU8jNze2W7SGirmOfieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJg30mMoa5uzeAjjyfzwefzxe53djY2I1bQ0SHg30mkgO7TCQP9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJg30mkgO7TCQP9pnol3HxexT7+OOP4Xa7O9wXCoUOO3f69Ol46KGHDjuHiA4e+0wkDyP6zC4THXk8NhPJg8dmInmwz0Ry4FibSB48NhPJg30mkgPH2kTy4LGZSB7sM1H3ULt7A2j/xo8fj1WrVnX495///Oewc6dMmYKGhobIv9LSUgFbS0QHwj4TycOIPrPLREcej81E8uCxmUge7DORHDjWJpIHj81E8mCfieTAsTaRPHhsJpIH+0zUPfjK71HM5XIhPz+/w31lZWWRj1VVha7rHT4fCAR+Mddms8Fms4nZSCI6KOwzkTyM6DO7THTk8dhMJA8em4nkwT4TyYFjbSJ58NhMJA/2mUgOHGsTyYPHZiJ5sM9E3YOv/N6DJScno6KiosPOcdWqVd23QUTUZewzkTzYZyI5sMtE8mCfieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZyJ5sM9ExuDi9x5s3LhxqKqqwmOPPYbi4mL861//wqefftrdm0VEXcA+E8mDfSaSA7tMJA/2mUge7DORHNhlInmwz0TyYJ+J5MAuE8mDfSaSB/tMZAwufu/BBgwYgGeeeQb/+te/MGzYMHz//fe44447unuziKgL2GciebDPRHJgl4nkwT4TyYN9JpIDu0wkD/aZSB7sM5Ec2GUiebDPRPJgn4mMoeg/fT8F6pUaGxsRGxuLCYW3w2yyiQuuqBaXtUeork54JhRFfKTZIjRPD/iF5hnGgP/LiWsbhWV5m4OYeswXaGhoQExMjLDcaBLpc8zlMCtWYbl6doawrHZqS5vwTN0k/jldussuNE/ZsUtoHgDo/oDwTCOU3TxcWFbI58Wmp++Vts/tXc770zSodnF/g3nTVgrLaqf5fMIzDcEhrzCbXxwtNE9r86LspqnS93mc+muYFXFjRNUh9vgEAIrbJTwTBhyj9DaxY4iesh8zp6UKz3S8ExSWFWjx4+NJL0rf5ZOdvxE6ztZaW4VltVPMZuGZMJmER4reTr1frtA8AMDaTcIjTdl9hGdWPC3ubxIAQq0+rL34Cfn7bL9Y7HmzAeNNdW6i8Ez9rAbhmVpLi/DM3sp3xhihecGAF998/qD8ffZcJrbPfvFzsUZkGqKXnjsrNoHXRfa4d8N3wrJamjScNXSr9F3Ou1/wPNj0NcKy2mltXuGZigFjbeFzBgbMQYQqq4RnqgZ0eduUkULzQl4vtv5V/nntidk3wayK+32Edu0WltVO7ZslPNObGy8807G5UmieViP+WrvWYsDchgH7xpJ7xM1rh3xeFD0qb5eBvX0eb7lI6Ly2KTNNWFa7puHiM11zxY8j9JAmNi9owPXhHnI+sOOdIcKyQq1ebL1qurR9Nuq8GQHxf39KrPj/f8WAcawueJ2LYhW7vgwAoIpfN7Pz/GzhmUlrxJ5fBYNeLF78Z+n7PCH/NqHrO7VtpcKyIlTx6wdVI65jC17nqDU1C80ziup0Cs+sfi1JWFao1YdVF844qC7zld+JiIiIiIiIiIiIiIiIiIiIiIiIiIiIKOpx8TsRERERERERERERERERERERERERERERRT0uficiIiIiIiIiIiIiIiIiIiIiIiIiIiKiqGfu7g2g6KE0NENR/cLygnV1wrLamQryhGeGtmwVnqkHA8IzewRdFx7pFPg3qahBYVnRTonxQFFt4vKqxfcZZgMOQXXNwiOVerHbqXg8QvMAAM0twiOV+FjhmUGnuKxQL3n6nr1KgcmmCMtrO3mIsKx2rtU7hWeGdlcKz9SDgo8BirjfS4QBx1EjxK6yCs0L+TShedHKFOuBSRH3f6e4BO5U99BbWoVnak1NwjN7BEX8gcqI34/dJG47TabeMdZW7HYoqsD9YKv436tiwDhbN+AYJfrYbKpuEJoHACHhiYDe0Cg8szBB7O8nYPNjrdDE6KRYrVAEHpu1RvG/29D4XcIza68+Vnhmwivfiw3UjGhfz9B4o9i/o1CrD/hcaGRUUhPjoQqcBwuVlQvLoh4iJH6/UxpIFJbVJnpOI0r1md8Gs1ncuEb4XBAARTVgPsiATOHj9ybx8+5GzIPpIfFzTBPPWC40z98cwNa/Co2MSlp1LTSBY23oBswfVlQJjzSlxwjPrDopU2he0rdi53YBQN1RJjwTqvi5tfiN4o73wUDvOWdRY2OgCpwLKz9N7N80ACS/IPicFICamiw8U6urF5qnmMSdA7XTvF7hmardLjxT3+IWF+btHcvmVKdTaJdDNeLXjyht4v/+QvXi54xFnz+quVlC8wCgYWSq8EzNgKrYisWuCTBpPqF50UppaoGiiluXqBswJ4KgAeP3kPjjieIWeDwBoPrFrxfVDNg36n5xazHbJTnFrVsL6gff5V6ydIyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIejIuficiIiIiIiIiIiIiIiIiIiIiIiIiIiKiqMfF70REREREREREREREREREREREREREREQU9bj4nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiiHhe/ExEREREREREREREREREREREREREREVHU4+L3KDJ16lQMHz68uzeDiA4Tu0wkD/aZSA7sMpE82GciebDPRHJgl4nkwT4TyYN9JpIDu0wkD/aZSB7sM1H06LWL36uqqmC1WtHS0oJAIACXy4WSkpJOH/vuu+9i7NixAICvv/4aeXl5hmzTHXfcgS+++MKQbCJZNdX68YehC+FrDSEU0HDHyEWo3eXt9LFr5ldFPmaXiaJPsKUZm/98JzS/D3oohC3T7kGgvq7TxzZvXBv5mH0mii7B1mb8+I87oQXCXf7xX/fA39h5lxu2rMK2958DAHz33XfsMlGU8da14e0TXkSwLQAtqOGd8S+jpaK508eWLdoe+ZjHZqLo461rw7sn/jfS51knv8Q+E/VAwYYWrDl/OkJeP/RgCGsvfAz+yoZOH1u/ZAOK738DAMfaRNGosTaAqwd9D19rCMGAhmuHLUP1Ll+nj10+b+85NY/NRNGnrc6L53/1JgJtQYSCGv59/Ftoqmjp9LHbFpZGPmafiaJLwNuM72bdg1DQD00L4fv37oWvpfN57dqdP0Y+ZpeJok+ouRlF998VueZc/OCU/V9zXs9rzkTRzB9qxWclf0dQC0DTQ5hX+k+0BRs7fezu1uLIx+wzUe9g7u4N6C7ffPMNhg0bBpfLhe+++w4JCQnIzs7e72OPO+44AMDixYsjH4vmdrvhdrsNySaS1fZVjcgsdMPmNGH76gY4Yy1IyLB3+tgda5siH7PLRNHHW7odtrQMqFYb2sp2wORwwhIX3/ljd+59whr7TBRdWiu2w56UAdViQ2vFDpjtTlhjOu9yS/kOONNy0La7BF9//TW7TBRlqn+sRFxBAswOC6rXVcIWY4MrrfMu1azf+0RTHpuJok/Nj5WIzQ/3uWZdJawH6HPtBvaZKFq1bNwJe24qTHYrWjbthMljhzUldj+PLYOrMANtm3dxrE0UhYpWNiO70Amb04Ti1c1wx5qRlGHr9LHFa/Y+YY3HZqLoU7GmGon94mBxmLH7x2rYYm3wpLk6fezudTWRj9lnoujSVLMDzth0mMxWNNWUwGx1wubaz7x2zd4nsrDLRNHnp9ecvaU7YHI69nvN2VfKPhNFs3pfOTzWZJhVC+p95bCodjjMMZ0+tsFfEfmYfSbqHXrtK7//dMJ/yZIlB9zhHcxjP/roIxQUFMBut2P8+PF45ZVXoCgK6uvrI4/597//jaysLDidTpx//vmYMWMG4uLiIp//+dtiTJ48Geeddx4ef/xxpKenIzExETfffDMCgUDkMeXl5TjzzDPhcDjQt29fvPHGG8jNzcVTTz3Vtf8Yoh5m28oG9B0Rvsi3dUUD8kZ0fsEPAHas2fvsP3aZKPq0lW6HIys3/HHJtsjHnfnp4nf2mSi6tO7aDmdGLgCgZde2yMedPrZ8GxxpOQCAb7/9ll0mijLVa3cjaUgqAKBqdQWShqbu97E16/YuluWxmSj6VK/djaShaQD29HnIgfpcGfmYfSaKLi0byuAa2Cf88fpSuAZk7fexrRvL4CzMBMCxNlE02rKiCQWjPACAzT80oWDk/i+cb1299xWkeWwmij6711QhbVgyAKB8VRXShybv97GVP1ZHPmafiaJLc80OeJJyAQBN1dsiH3f62FpeoyKKZm07tsOekxv+ePs22LP77vex3jL2mSia1fnLEW9LD3/s24U4W8Z+H1v/k8Xv7DNR79CrXvm9pKQEQ4cOBQC0trbCZDLh5ZdfRltbGxRFQVxcHC699FI888wzeOONN3DTTTcBABobG3HFFVfAZDKhqakJCxYswD333INnnnkGl156KbZt24YLL7wQv//973Httddi5cqVuOOOOzp876VLl+LGG2/Eo48+inPOOQfz58/H/fff/4vbvGDBAqSnp2PBggUoKirCJZdcguHDh+O6664DAFx55ZWorq7GwoULYbFYcPvtt6OysvKAmT6fDz7f3rfPbGzs/O1AiKJV7S4vHj1vGQDA7w1BVRV8/0EF/N4QFEXB3UcvxqgzU3Dxg4X44ePdePfPmwEA3uYgACA7O1uKLgPsM/V8gfo67Hj2bwAALRCAoipoXLUMejAAQEHR9HvhGTISqWddiMY1y1H5yXvhx+75u5elz+wy9XT+xjpseT3cZT0YABQFdeuXQQ+Fu7zu2XsRVzgSmSdfiPqNy7FzwZ4u+33YVfMOAODzzz/H4sWLe3SXAfaZer6WimZ8esVsAEDIG4SiKtg2ZwtCviCgKJh1ykzknHoUxtx5HLZ/VoQfHv8aABBo8QPgsZkomrRUNOOzK8PH3PY+b5+zOdLn2ae+guxTjsLoO4/Hjs+LsPzxpQDYZ6Jo469swKZb/w0A0H0BQFVR+8Ua6L4goABrf/M44k8chD43nY66hT+i7Lm5AACt1Yeyf84BwLE2UbSo3uXDn85eCwDweTWoKrBkdhX8Xg2KAtw46gf86uxETJ7aF1//rxozp24HALQ1hwDw2EwUTZoqWvDObz4BAAS9ISgmBZv+txVBXwiKAvx33DsoOC0XJ95zNDbP3YbFj+y5ptUSXgDDPhNFB19LHdZ8PgMAoIXC89pV238IfwwFy96/H0nZI9B31K9RvWMltq3YM2cWkOsaFcA+U88XqK9D6d8fB7DnmrOioGnFssjHW/98H9zDRiLl3AvQtGoFqj7ac53KK1ef2WWSQVuwEUvLXwMAhPQgFEXBzub1COkhKADmlz6DdFd/DEo4GbtaNmJ97ZcAgKDOeW2i3qZXLX7PyMjAqlWr0NjYiNGjR+O7776Dy+XC8OHD8cknnyA7OzvythTnnHMOxo4di/nz5+Opp57Cxx9/jDVr1uDGG2/E11+HL+4nJSUBAJ5//nkUFhbib38LL/gpLCzEjz/+iGnTpkW+9z/+8Q+cfvrpkZ1mv3798PXXX+Pjjz8+4DbHx8fjn//8J0wmE/r3748zzzwTX3zxBa677jps3LgR8+fPx7JlyzB69GgAwH/+8x8UFBQcMHP69Ol46KGHuvA/SBQdYlOsuOv90fA2h/D4RT/g9rdHweow4bHzl+GG54YiPsMGm9MEABgyPhF9R4zGpq/rsODlUuze2ornnnsOt99+e4/vMsA+U89n9sQg58Y7oPm82PHCk8i+9jYoVit2PPcEMi+9FpbYeKhWKwDAXTgYjqy+aN26GbXfLESgulKaPrPL1NNZ3DEouOwOhHxeFL/1JI665DaoFiuK3ngCuedeC4snHqol3GVP3mAUZPRFc8lmVK/6ClmTrkDRG39DSkoKvvnmGwA9t8sA+0w9nyPJidNmno9Aix+fX/0hTvnvOTDbLZh71fs46fFT4Uxzw+KwAAAyT8hB0tBU7F62Exvf/BGN2+t5bCaKIo4kJya98msEWvyYd80HmPifc2G2W/DZ5Nk44W+T4Epzw7ynzxnH5yBpSCoqlu3E5rfWss9EUcSS6EHh09ci1OrDlttfRMHjk6Hardj8+/+g7wOXwJocA9UeHmvHHNMPhQP6oGnVNlR/tAzZd56Hzf/3b461iaJEfIoVD384BG3NITx4wY948N3BsDlV3H/uWtz+QiES022wO8Nv2Dzy5HgUjPBg3dcNmPtSOXYVe3lsJooiriQHLnrjDARaAph1xVxc8PIkmB1mvHvpHJzx1Di401ywOMOX4fue2AdpQ5NR9n0FVr+2AfXbG9lnoihhdcRgyKl/QCjgxY/z/47BE/4PJrMVaz5/Cv1PuAZWZxxMZhsAID5jINxJOWjcvQW7Nn0Fb5M816gA9pl6PrMnBln/90doXi9Kn3kKfX73e6hWK0r+MQMZV/0W5ri915xdAwbBnpOL1qLNqF+yCIEqefrMLpMMbCY3xqZfhpDmx9cVb+LY1N/ApFiwtOJ1jEo+F3azB2Yl3OcURx7i0tNR4y3F9sblaAnWsc9EvYja3RtwJJnNZuTm5mLjxo0YM2YMhg4dioqKCqSmpuLEE09Ebm5uZIfndruRm5uLFStW4Nxzz0Vubi7Wrl2LM844A7m5ucjNzY0slN+0aRPGjBnT4XsdffTRHW5v2rRpn/t+frszgwYNgslkitxOT0+PPPNn06ZNMJvNGDlyZOTz+fn5iI+PP2DmlClT0NDQEPlXWlr6i9tBFE1MZhWJmQ7s3tqK7MEeZBa60VTtgyfJivwxcUjMdMAdHx7o2FxmJGY6ULa+CQNPSgQArFu3ToouA+wz9XyKyQRLfAL81ZWwZ2bBlpaBUHMTzC43nLlHwRKfAJMr3FHVZoMlPgHe8jK4CgYCkKfP7DL1dIpqgjUmAb66SjhSs+BIzkCwtQlmpxuuzKNgjUmA2RHup8lqgzUmAW2VZYjJGwxrTLgfp5xySo/vMsA+U8+nmlW40z1o2t6AhAFJiM9PhLemFfZ4B1JGpMOd7oEtzg4AsDgtcKd7ULupBhljswDw2EwUTVSzCle6B4076hE/IBlx+Yloq93bZ9fP+uxK96BuUzXSj2WfiaKJYlJhTY2Dr6wGjoIMOPqmIljXDHOcC+7B2bCmxsEc6wQAmBxWWFPj0FZcgZhjCmBNiQXAsTZRtDCZFST3saF8axvyhriQ3d+JhqoAYhIt6D8mBsl9bPAkhJ+YZneZkNzHhu3rWzDspDgAPDYTRRPVrCImw4267Y1IGZiAxIJ4tFa3wZFgR8bIVMRkuOH4yVg7JsON6o21yDk+EwD7TBQtFNUEuysB3qYquOL7wBWXgYC3CRa7GzHJebC7EmCxuQAAJosNdlcCWup2Ii69PwB5ugywz9TzRa45V+255pyegWBzE8xuNxx9O7/m7Nu1E67CAQDk6TO7TDJQFRVOcyyaA3WItabCY02GT2uFzeREgr0PnOZYWE0OAIBZtcJpjkWjvxLJjr4A2Gei3qRXvfL7oEGDsGPHDgQCAWiaBrfbjWAwiGAwCLfbjZycHKxbtw4lJSUYODC8qM7r9cJsNuPpp5+Gz+eDqqp46623cPnll+O5554zfJstFkuH24qiQNO0w8q02Wyw2WyHlUHUnf561neoLfchFNCg68Ado76CFtShhXTcMeorJKTbcO/Hx6B2lxd/Pft7AEDQp0HdM8545JFHpOgywD5Tz7f9X48iUF8HaCHouo4t0+4BNA26pmHLtHtgiYtH7s13I1Bfh+3/ehQAoAeDUNTw8/dk6TO7TD3d5lcfRaCpDvqeLq975h7oe7q87pl7YPHEo98Vd8PfWIctr4W7rO3pcvXKrwAAb7/9NmbPnt2juwywz9TzfXLpLLRWNEMLatA1He+e/DK0kA49pOHdk1+GM82NM9+4EC0VzZhz6SwAQMgfgqIqAHhsJoomn172boc+vzfhpUif35vwEpxpbpz++kVoqWjG3MveBcA+E0WjjTc9j0BVA/SgBl3Xsfaix6CHNOghDWsvegyW5Fj0f+YG+CsbsOnm5wEAmj8IxaSi+qNlADjWJooWU85Yg+pdPoQCOnQduG74MmhBHaGQjuuGL0NShg3T5wxF9S4fppyxBgAQ8GlQTTw2E0Wbty7+GE3lLdCCGqDp+PcJb0MPadBC4Y896S785p2z0FTRgrcuCr9KZMgfgsqxNlFUWT33cfhaw/Pa0HV8P/u+8Ly2ruH72ffB5ozHsNPugK+lDqs/exwAoIWCUBS5rlEB7DP1fCVPPYZAfR30ULjPxVOnRK45F0+dAktcPLJvuwuB+jqUPPUYgD3XnCXrM7tMMlhSPhNtwSZougZAx7zSf0HXNejQMK/0X3CYPTg+/Uq0BRuxpPxVAICmB6GAfSbqbXrV4vc5c+YgEAhgwoQJeOyxxzBq1Cj85je/weTJk3HaaadFdkQZGRlYtWoVKioqMHHiRKxatQqhUAjDhw/H4sWLkZCQgJiYmEhuYWEh5syZ0+F7LVu2rMPtwsLCfe77+e1DVVhYiGAwiJUrV2LUqFEAgKKiItTV1R1WLlG0u/H5YQgFNfzz6lU4946jkDXIg5f/uB7HnJ+GAccnwGQOD2hiU6y4e/ZoNFb78a9rVuP3r4/AExctBwB2mShKZF52HfRQCGUzn0PyKWfBlt4H5bNeRczwMXDl94ey59mxZk8Mcm78I0LNTSib+Rz6XHMLSv/9FAD2mSga5J57HXQthG2zn0Pa8WfBkdIHpZ++iriBY+DJ6Q9lzzPQLO4Y5F/6RwRbmrDt/eeQf+kfEfJ5sfWdpzF37lxkZWWxy0TdbNwTk6AFNXx566cYfvMYJBQm4esHFqDvGQVI/1UfqHvG2o4kJ0575Xx4a9vw5a2fYuJzZ+Hzaz4EwGMzUbQ48fHToIU0LPy/ORh289GI75+Ebx74En3P6Ie0X/WBatrb51Nf+TW8NW1Y+Ps5mPDs2Zj32w8AsM9E0SBv6m+gB0Mo/tPrSL96ApxHpWHH395HwoSh8Iw8Cop5z1g70YN+T1+LQF0Ltt7/Ovo9fS1CrT4U3fEyx9pEUeKP/y5EKKjjkas24JI7s9F3sAv/+kMRTjg/CUNPjIPJHF4UG59ixV8+HIL6qgAem7wBf3pzIB68YB0AHpuJosWZT4+DFtTx0e/m49hbRyB5QCLm3bsEhWflIXtsBtQ9fXYlOXDxG2egtaYN/7vpC5z371Px3lVzAbDPRNGg/wm/ha6FsH7R88geeibc8X2w5dvXkZw7GnFphZF5basjBkNP+QP83iZsWPQCBp58E9bN/zsAdpkoWqRfdS10TcOu/z6HxNPOhC0zC7vfehWekWPg7Nc/8sJqZk8Msv7vjwg1NWLXi88j8/pbUPbMUwDYZ6JoMSr5PGi6hmWV76Ew7njEWFOxumYOMl0DkWTPhbrnSSs2kxtj0y6DP9SCZZWzcUzqRfhm91sA2Gei3kLt7g04knJycuB2u7F7926ce+65yMrKwrp163DBBRcgPz8fOTk5AACz2Yz8/HyUlZXhmGOOQf/+/VFTU4O8vDwcffTRyM/PR0pKSiT3hhtuwMaNG3H33Xdj8+bNeOedd/Dyyy8DCD+TBwD+7//+D3PmzMGMGTOwZcsWPP/88/j0008jn++K/v37Y+LEibj++uvx/fffY+XKlbj++uvhcDgOK5co2iVk2mFzmtBUE8CQCUmIS7ejoqgFw05JRnKOEwmZ4beSNJlVJOc4UV/hQ85QD1Jyw28BnZubyy4TRQlLXAJUqw2h5ia4CgfDHBsPf1UFPAOHwZqYDEtcAoDwW9VZE5MRaGyAvU82rInh7rLPRNHBGpMA1WJDsLUJMXmDYXHHw1tTgdj8YbDFJcMas6fLqgm2uGQEmhvgTMuGPSEVIW8rAGDUqFHsMlEUcKV7YHZa4K1tQ58TcuBMdaNhax2yxveFJysWrnQPgPBbvHuyYtFa2YLEgcmIyY4FwGMzUTRxpXtgdljgrWtD5gk5cKa40bi1Dn3G9YWnz8/63CcWbXv67GGfiaKKNSUWqsOKYH0LYo/pB0tyDLwl1Ygd2x+2jARYU8KdVUwqbBkJCNQ0wtkvE/asJISa2gBwrE0ULZIybbA5VTRWBzByYjwS0q3YuaUVYyYlIDXHjqTM8Cu6mcwKUnPsqKvwI2+YG2l9w/PdPDYTRQ9PuhsWhxlttV7knpQFd6oTtVsbkDchG7FZHnjS3QDCY+3YLA+ad7ciZXAS4nLCi2/YZ6LoYHPFQzVbEfA2IyFzEKzOOLQ2ViChzxDYPUmwueIBhOe17Z4k+Nsa4E7MhsOTDIBdJoomlvgEqFZr+JrzgMGwxMbBv7sC7kFDYU1MgiX+p9eckxBsaICtTzasSewzUbRxmGNgVi3wh1qR4jwKDrMbzYEapDoL4LLEwWEOj6lVRYXLEgdvqBmxtjS4LOHjNvtM1Hv0qsXvALBw4UKMGTMGdrsd33//Pfr06YP09PT9PvbEE08EACxatCjy8c/17dsXs2bNwuzZszF06FA8++yzuO+++wAg8vYTxx13HJ577jnMmDEDw4YNw9y5c/GHP/wBdrv9sH6emTNnIjU1FSeeeCLOP/98XHfddfB4PIedSxTttnxfj+whHlhsJpSsaURcqg2xKZ2/3UvRsnrkj46L3B47dmynj2OXibpH6/Zi2DOzoFos8O4sgTkmDmZPTKePbdteBEfOUZHb7DNR9GgpK4YjNQuq2YK23SWwuONgcXXe5ZayIjgzw11uLd+230x2mah7VK4oR+KAJJhsZtSsr4IjxQVHknO/j00Znha5zWMzUXSpWlGOhD19rl1feeA+r9yF5OF758jYZ6Lo0bx2B5wFGVCtZrRu3gVLogeWBE+nj21ZuwOuwVnhjzeU7TeTXSbqHhu/a0LfIW5YbSq2rm5GQpoVcSnWTh+74ftGFI7Z23Uem4miy87lu5E8MBFmmwm719XAneKEK8nR6WN3Ld+NjBF7F96wz0TRo7FqK9wJWVBNFjTXlsDmiIPV0fm8dmNlMWKS+kZus8tE0aVtazFs7decS0tgjo2DOWY/15y3FcPRNy9ym30mii613jLE2lJhUsyo9+2G3eSB3eTq/LG+MiTYMiO32Wei3kPRdV3v7o2Q0bRp0/Dcc8+htLR0v4+57rrrsHHjRixevFjY9y0rK0NWVhbmz5+PCRMmHNTXNDY2IjY2FhPTrodZ7XyStSuC5RXCstqZCvJ++UGHKLRlq/BMiH5mVi+u6Tnra4RltTUHcfeYxWhoaOjw1jYH0pO6DPykz31+B7Pa+ZMBuiQQEJfVzmwWHqk3NwvPFL2diqPzye/DoTe3CM9U4mOFZ269IkNYVsjrRfEj90rb5/YuD7zhrzDZxJ3wxBWL77Jr9U7hmaHdlcIz9WBQbKARz8LuIcf73bd2PmHQVSGfF+ufO/g+96QuA3v7PCH+KpgVcWNtxdX5wsXDobe0Cs/UmpqEZ4qmh0LiQxXxzzM3uTuf1DscCXPFbWegxY9ZE2dKf2yekDBZ6HlzqKZWWFY71YDJ0p4wdWRKSRaeGTJgXkONPbh+HIq4j8T+fgItfsw+5RX5+xxzudBjc6ixUViWkWqvPlZ4ZsIr34sN1Aw4NvcQVR8VCs0Ltfrw4yWPSz/Wnph7i9B5sFBZubCsdnrQgLk1I/SAY74RFAPmKa9Yt/8neh+qtuYgbhi5XPpj84nH/glms7ixrGnZBmFZEUacP5pMwiMVq7gxDgBDfm6tVfwchGIR/HMDyP9a7BygvzmA/457R/o+n+z8jdCxtu73C8tqpzrFz60FRhz1yw86RA25Ys/xk76tEpoHAPqO/T/hsstU8XNrjWcOEZYVDHjxw/v3S9tl4Cfnzkm/FToXVnFBvrCsdskvCD4nBWBOFT/PpNXVC88UTfN6hWcaMVe59f4RwrI0rxdb/3yftH2OnDenXid4XrtOWFY71SH+b0Xz+YRnih4bq7lZQvMAoGFkqvDMpj7ij81Z75YIzQtqPswve5Z9PkTBymphWRG6JjzSZMC1FcXtFpqnN4q/Lq61iT82K1aL+MyPxa0xC7b48MWZzx9Ul8XP6PVSzzzzDMaMGYPExEQsXboUf/vb33DLLbd0eMzjjz+OU045BS6XC59++ileeeUVPPPMM4f1fb/88ks0NzdjyJAhKC8vx1133YXc3Nz9vko9ER0Yu0wkD/aZSA7sMpE82GciebDPRHJgl4nkwT4TyYN9JpIDu0wkD/aZSB7sM5FcuPhdkC1btuAvf/kLamtrkZ2djT/+8Y+YMmVKh8d8//33eOyxx9DU1IS8vDz8/e9/x7XXXntY3zcQCODee+/F1q1b4fF4MHbsWLz++uuwWMQ/Q4OoN2CXieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJg30mkoui94T3riZDRd4WI+16sW+LYcDbg5sK8oRnhrZsFZ4JRezbIPbWt5sFgHPW1wjLamsO4u4xiw/pLW56mkif+/xO6Ns9I2DAWzMb8HbCenOz8EzR26k4HELzAEBvbhGeqcSLe0uadluvyBCWFfJ6UfzIvdL2ub3LA2/4K0w2cW/xFlcsvsuu1TuFZ4Z2VwrP1INBsYGij/VAjzne7751rNC8kM+L9c/J3+cJ8VcJfbtnxSX+rZn1FvFvOa41iX97N9F0I962XhH/9o8mt0t4ZsJccdsZaPFj1sSZ8nc5YbLgt4etFZbVzoi3Eu4JU0emFPFvRx0yYF5DNeCtOeM+Evv7CbT4MfuUV+Tvc8zlQo/NocZGYVlGqr36WOGZCa8Ifot5zYBjcw9R9VGh0LxQqw8/XvK49H2emHuL0HmwUFm5sKx2etCAuTUj9IBjvhEUA+Ypr1i3TVhWW3MQN4xcLn2XTzz2TzCbxY1lTcs2CMuKMOL80WQSHqlYxY1xABjyc2ut4ucgFIvgnxtA/tdi5wD9zQH8d9w70vf5ZOdvhI61db9fWFY71Sl+bi0w4ijhmQ25Ys/xk76tEpoHAPqOMuGZUMXPrTWeOURYVjDgxQ/v3y9tl4GfnDsn/VboXFjFBfnCstolvyD4nBSAOVX8PJNWVy88UzTN6xWeacRc5db7RwjL0rxebP3zfdL2OXLenHqd4HntOmFZ7VSH+L8VzecTnil6bKzmZgnNA4CGkanCM5v6iD82Z71bIjQvqPkwv+xZ9vkQBSurhWVF6JrwSJMB11YUt1tont4o/rq41ib+2KxYxT/pQvlY3BqzYIsPX5z5/EF1ma/8TnupiiEnkiIZsVBdsQlcILyHEZMwvdUYh7jfeUtQ/ME1Wmm1ddBETiwaMLktfAIegO4Vf/KiCl5oFqoUP7GoGrEfqxa/EGvMaeIyAy1+FD8iLC5qpb+zSexFgpx0YVnt1v+pj/DMgdPELywPlolfpN9biX4SRdCIJ1hFIa25FZoi7mfVG8QvsDMlJgjPNGIRuGISnGnANhoxdjJiYcDoWHETWl5TELOEpUWxYNCQvxmRjFiorgcEP4kMgCJ4kY+WaMBkdoX4J+QpHrGTqQCwtUHsuVWoxYCLQlEo1NQMRel9r6yT/N464Zlls/sJzcu4cIvQPMCAJ8MaxPSB4PGYX/zFkajk9QMCD89qjPh9NTTxx2cjnmiqC95OxWLAi18YMPduxDWCz2oHCcsKtPgBLBeWF60a8xwwWcUteEn4zoAncxiwUN2IsbbwhUNp4hcAKtvELnQBjLmA/8lycV0G2hcuvCM0MxrpIQ26Im5uRPTxySimb9cLz0ys6Ss0r3FwotA8AIhtaROeGaoSvwirrr+4fXjIJ/54EK20+npoAs+d094Qf06qZKQJzzRkobrgcYRuxKJeA174yYjFxxecsVRYlq85gKf+LCwuetmtgMgXT1QNeJEwA87LFCPWuYh+Ap0B5xixq8UfR00+8dcQ644Tu84gGPCiN1yo0nU96l+wyOTxCM8M1TcIz1REv9CoEdcPjdjfGvD3MzZR3PpOry2ALw7ysdF9xZaIiIiIiIiIiIiIiIiIiIiIiIiIiIiICFz8TkREREREREREREREREREREREREREREQ9ABe/ExEREREREREREREREREREREREREREVHU4+J3IiIiIiIiIiIiIiIiIiIiIiIiIiIiIop6XPxORERERERERERERERERERERERERERERFGPi9+JiIiIiIiIiIiIiIiIiIiIiIiIiIiIKOpx8fsRMnnyZCiKss+/oqKi7t40IjpE7DORHNhlInmwz0TyYJ+J5MAuE8mDfSaSB/tMJAd2mUge7DORHNhlInmwz0Q9i7m7N6A3Oe200/DSSy91uC85ObmbtoaIDgf7TCQHdplIHuwzkTzYZyI5sMtE8mCfieTBPhPJgV0mkgf7TCQHdplIHuwzUc9xSK/8HggEcNdddyE/Px9HH300XnzxxQ6f3717N0wmk9ANlInNZkNaWlqHf08//TSGDBkCl8uFrKws3HTTTWhubu7wdUuXLsW4cePgdDoRHx+PSZMmoa6uDgCgaRqmT5+Ovn37wuFwYNiwYZg1a1Z3/HhEvQr7TCQHdplIHuwzkTzYZyI5sMtE8mCfieTBPhPJgV0mkgf7TCQHdplIHuwzUc9xSIvfp02bhpkzZ+LGG2/Eqaeeittvvx033HBDh8foui50A2Wnqir+/ve/Y926dXjllVfw5Zdf4q677op8ftWqVZgwYQIGDhyIb775BkuWLMHZZ5+NUCgEAJg+fTpmzpyJ5557DuvWrcMf/vAHXH755Vi0aNF+v6fP50NjY2OHf0R0+NhnIjmwy0TyYJ+J5HGk+8wuExmDx2YiebDPRPLgWJtIDjw2E8mDfSaSA7tMJA/2mSg6KfohrFYvKCjAk08+ibPOOgsAUFRUhNNPPx3HH388XnzxRVRWViIjIyNSXNpr8uTJeO2112C32yP3nX766Xj33Xc7PG7WrFm48cYbUV1dDQC49NJLUVJSgiVLluyT6fP5kJCQgPnz5+PYY4+N3H/ttdeitbUVb7zxRqfbMnXqVDz00EP73D8x4waYVVuXfr7OBHfuEpZlJMUm7mdup/v9ggN775NKHt62TFhWS5OGiUNK0dDQgJiYmC7n9IQ+n+z8DcyKtUs/X2d0A/brilXc9rXTvT7hmarbJTRPa20VmgcAqgH7MSMkfSYuK9DixzsTXjusPveELk+Iv0psl3PShWW123iDW3jmwGk7hWcGywRnKorYPKDHHO99Z44RmhcMePHNZw9K3+fxlotgVixd+vk6Y8Sx2ZSYIDxTq28QnqmYDuk53L9ID2lC88KZBoydVPH7ndNWVwvL8jYHcd/RC6UZa+/32BxzudBjc8iAyUpDznEDQeGZiuB3DlQG5QvNAwB9XZHwTFNmmvDMmmfEnluFWnxYceGT0h+bxynnCT0295SxnOkw9tH7UzYzU2hexoVbhOYBgB4Uvx8zQu01x/7ygw5ByO/F6lfvk77PE9Ouh1kVeO4sem4XADTx+witqUl4pi54OxWLWWgeYMzvR3U6hWcmzxc31hExDwZET5/31+Xhl02DyWrf5/6uSnh9ubCsCAPOy4wYa5tiBM/XJSeKzQOgbSsRnmnEudDGJwYJzdPavCi77QHpj83jbReLnQczoieCr/0AgOYTf41KKegrNK+pMFZoHgDEflcmPDNUJW7Oql3JXaOEZYV8XhQ9eq80x2bgAH02XyC0z0aMu5QYj/BMra5eeKZougH7HCPmtY0waoW47fQ1B/DU8f+T/tg8MedmoevBQuW7hWW1Uw2Ys9INWJshfJ1LivixthGa+4u/hhh0iL3eFwx4sXzWn6Tv84SUa4XOg4WqaoRltTNirG3I9TSz4HkrRezfNABD5iFEX58DgOO+rRWW5W0OYPqxnx1Ulw/pN7hz504MHjw4cjs/Px8LFy7EySefjCuuuAKPPfZY17a4lxg/fjyeffbZyG2Xy4X58+dj+vTp2LhxIxobGxEMBuH1etHa2gqn04lVq1bhoosu6jSvqKgIra2tOOWUUzrc7/f7MWLEiP1ux5QpU3D77bdHbjc2NiIrK+swfzqi3oV9JpIDu0wkD/aZSB7R0Gd2mejwRUOXAfaZSAT2mUge0dBndpno8EVDlwH2mUgE9plIDuwykTzYZ6Ke45AWv6elpaG4uBi5ubmR+zIzM7FgwQKMHz8ekydPFrx5cnG5XMjP3/tKZNu3b8dZZ52F3/3ud5g2bRoSEhKwZMkS/Pa3v4Xf74fT6YTD4dhvXnNzMwDgk08+QWZmx1dYsh3g1Q1sNtsBP09Ev4x9JpIDu0wkD/aZSB7R0Gd2mejwRUOX2z/HPhMdHvaZSB7R0Gd2mejwRUOX2z/HPhMdHvaZSA7sMpE82GeinuOQFr+ffPLJeOONNzBhwoQO92dkZODLL7/EuHHjRG6b9JYvXw5N0/DEE09AVcNve/DOO+90eMzQoUPxxRdfdPo2FgMHDoTNZkNJSQlOOumkI7LNRNQ59plIDuwykTzYZyJ5sM9EcmCXieTBPhPJg30mkgO7TCQP9plIDuwykTzYZ6LodUiL3++//35s3Lix089lZmZi0aJFmDdvnpAN6w3y8/MRCATwj3/8A2effTaWLl2K5557rsNjpkyZgiFDhuCmm27CjTfeCKvVigULFuCiiy5CUlIS7rjjDvzhD3+Apmk4/vjj0dDQgKVLlyImJgZXXXVVN/1kRL0P+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwT4TyYFdJpIH+0wkB3aZSB7sM1H0Ug/lwTk5OZg0adJ+P5+RkcFCHoJhw4ZhxowZePTRRzF48GC8/vrrmD59eofH9OvXD59//jlWr16No48+Gsceeyw+/PBDmM3h5y08/PDDuP/++zF9+nQMGDAAp512Gj755BP07du3O34kol6LfSaSA7tMJA/2mUge7DORHNhlInmwz0TyYJ+J5MAuE8mDfSaSA7tMJA/2mSh6Kbqu6135wrKyMmRkZEBV1Q4fU8/T2NiI2NhYTMy4AWbVJiw3uHOXsCwjKTZxP3M73e8XHNilmkrh4W3LhGW1NGmYOKQUDQ0NiImJEZYbTdr7fLLzNzArVmG5eigkLKudYhW3fe10r094pup2Cc3TWluF5gGAasB+zAhJn4nLCrT48c6E16Ttc3uXJ8RfJbbLOenCstptvMEtPHPgtJ3CM4NlgjMVRWwe0GOO974zxwjNCwa8+OazB6Xv83jLRTArFmG5RhybTYkJwjO1+gbhmYpJ7HmnHtKE5oUzDRg7qeL3O6etrhaW5W0O4r6jF0rf5Qkxlws9NocaG4VltTPkHDcQFJ6pmExi8wblC80DAH1dkfBMU2aa8MyaZ8SeW4VafFhx4ZPS93mccp7QY3NPGcuZDPidls3MFJqXceEWoXkAoAfF78eMUHvNsULzQn4vVr96n/R9nph2PcyqwHNn0XO7AKCJ30doTU3CM3XB26lYDunNhw+KEb8f1ekUnpk8X9xYp7fMgw2/bBpMVruw3ITXlwvLijDgvMyIsbYpRvB8XXKi2DwA2rYS4ZlGnAttfGKQ0DytzYuy2x6Qvs/jbReLnQczoieCr/0AgOYTf41KKRC7IKqpMFZoHgDEflcmPDNUJW7Oql3JXaOEZYV8XhQ9eq+0XQZ+0mfzBUL7bMS4S4nxCM/U6uqFZ4qmG7DPMWJe2wijVojbTl9zAE8d/z9p+xw5b865Weh6sFD5bmFZ7VQD/v91A9ZmCF/nkiJ+rG2E5v7iryEGHWKv9wUDXiyf9Sfp+zwh5Vqh82ChqhphWe2MGGsbcj3NLHjeSjFg7bQB8xCir88BwHHf1grL8jYHMP3Yzw6qy13+Hx84cCC2b9++z8dERERERERERERERERERERERERERERERKJ1efH7T18wvosvHk9EREREREREREREREREREREREREREREdFAMeK19IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKxzN29ARRFNB2A1t1bcWCKIjxS9/uFZ6pOp9A8raVFaJ5RFLP4XUptyC0sqzUUEpYV7VSPB6pqFZanNTULy2qnOB3CM2HA71ix24TmGfGsM8Um7ne9N1T8/nbx6mxhWVqbV1hWNNNa26ApQWF5+uoNwrLaDfxbjvDMHZeJz+zzVLXQPD0g7veyN9SA45QBXS4bJ/Z4r3nNwGdCI6OSHgpBV6L8uceaAcdRq0V4ph4U2z89GBCaBwAw4HdtxFjbooj7nQcFZkUz3e+HLn7XKpYR5z26+LkCPSg2UykuFZoHhPfdwjMNGLuflF4kNM/XHMAKoYkUTUKNjcIzMy8T25U+S8WehwNAyTEGjN8N4N4ldjuDRpy3RCNVAdQoH2urBgwgTCbhkQrE9lkxYj7fiHciNuDvp9Evbu4z4Bf/u45K+p5/ouKMmCs24GRAMaDLoiltPuGZuia+y6rDLjwTFsHnQoLPg6KVoog9BugGzFlpra3CM404NutbtgnN86Cv0DwACKXGCc9UA+Ln63yFbcKytNbecY0KCO+vdUXcPtuI7qkW8fOmoeEFwjPN63cIzdN94o/PMGSsLX7fuKy2j7CsYIsB/4/RSNfF/n57yNob0deTwqFie6LUi5+r05MTxGeaxJ8LeePEZob80X7xRpBgUOg8hmLAcRQGZCoW8ddWRF8jVqwGzNUZMf9nwDXnXb44YVl+38Gv5Y3yGWEiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi5+JyIiIiIiIiIiIiIiIiIiIiIiIiIiIqIegIvfiYiIiIiIiIiIiIiIiIiIiIiIiIiIiCjqcfE7EREREREREREREREREREREREREREREUW9Li9+v/fee5GQkLDPx0REREREREREREREREREREREREREREREopm7+oVTpkzp9OOerqKiAldccQW+/vprWCwW1NfXd/cmEVEX1FYF8MQfd2LDilaYzQreWTWguzeJiLoo1NCEmpfegq94O2AyIeupP3f3JhFRFwQbG1H15hvwbd8BxaQi5y/TunuTiKiLGqt8ePXuDdi2qgEms4pHvz+huzeJiLqopdqLOfd9j52ra2Ayq/i/Jed29yYRURf4fU3YuPotNNbvgKKYcNwpD3X3JhFRF3lrWrBy2nzU/VgBxazi9DnXdfcmEVEXhRqaUPPvt+Er2hGe1/7X1O7eJCLqgmB9E6qffRe+LSWAyYScf9/f3ZtERF3kq23B+ulz0bBuF1STCSf+76bu3iQi6qJAayNKvngDrRU7AFXFkN/yujNRb3TIi98DgQAcDgdWrVqFwYMHd/kbK4pywM8/+OCDmDp1apfzu+rJJ59EeXk5Vq1ahdjY2CP+/Yl6mvn6rL03Avt+Pk8dhKNMQ47cBu3xwYs1qKsM4h//Owouj+mIf3+inmhu+b8O+Pmj3GNQEHPMEdqavRrnL0aooRFpf7oNqsNxxL8/UU/T4dis7/v5vhiIo9RBR26D9mj86iuEGpuQefvtUO32I/79iXqi+aG3995o2/fzeeYhyLcMO3IbtMeCV8rQWOXHXbPHwOHp8nPqiXqVSJ83dv75oxKPQ37ykX8iyQ+vbUZztRdXvT0RVo/liH9/op6mw1i7E30xAEcpR36sXbZtMfy+Jow67jaYzBxrEx2MDn1u3PfzedbhyLePOHIbtMfWd1bDV9OKE/97CSxu6xH//kQ9UWSsXdX5549yjkK+a/SR26A9Gj9fglBDE9Ie+j1UB4/PRL9k3g8HfgLnUa7RyPccfYS2Zq/GT5ciVN+EjL/ewi4THaQOY+3Qvp/vqwzCUWrX15t1VemsFfDXtODoFy6H2WU74t+fqCf6fO1fwh+s7fzzfQacguyBpx65DdqjavVXCLY0od9Ft0O18vhM1Fsd8lVqi8WC7OxshEKdjFAOQXl5eeTjt99+Gw888AA2bdoUuc/tdkc+1nUdoVAIZrPxF9WLi4sxatQoFBQUdDnD7/fDaj1yk5KBQAAWCy9QUvc4AWdFPq5Ud6JY+xFjzWdE7jP9ZDej6zp06FAV1fDtqigJIH+wHZl9u37Swi5TbzM+ZXLk43JvMYqavsMJKZdF7jMpe/8+j2Sfg9U1sGb3gSU1ucsZ7DP1Jj89Nu9WylCs/4ixyumR+7rr2ByoqYGtTx9YktllooN1gnpO5ONK0y4UB1bjOPve+7qrz9Wlbcga5EFKrrPLGewz9TbtfTblZaOicQOKqpfg+Ly9r+RqUvf24Uj2ub6sBakD4hGf4+lyBvtMvUmHsTZKUYx1GIvTIvd117HZ21oDd2wmHK6kLmewy9TbdJjXtlWi2LcSx7l/Hbmvu+bBWnc1IrZfMtxZcV3OYJ+pt4mMteNjUeEtRlHrDzg+4ZLI57ttXruyBtacTFhSeXwmOhgnDvtj5OPKrd+iqHkZjk+6NHJft3V5dy2sfTNhSWOXiQ5Wx+tUO8PXqdTuv07Vtqsenn4pcPaJ73IG+0y9zUn9bwMAtBzlQXXpapSu/xwjJt0Z+bzJvHdNlq7rgK5BUY1/gVJ/Yw0cyX1gi+N1Z6LerEujh/vuuw/33nsvamtru/yN09LSIv9iY2OhKErk9saNG+HxePDpp59i1KhRsNlsWLJkCYqLi3HuueciNTUVbrcbY8aMwfz58zvk5ubm4q9//SuuueYaeDweZGdn44UXXoh83u/345ZbbkF6ejrsdjtycnIwffr0yNe+9957mDlzJhRFweTJkwEAJSUlOPfcc+F2uxETE4OLL74Yu3fvjmROnToVw4cPx3/+8x/07dsX9j2vZKkoCp5//nmcddZZcDqdGDBgAL755hsUFRVh3LhxcLlcGDt2LIqLizv8DB9++CFGjhwJu92OvLw8PPTQQwgGg5HPK4qCZ599Fueccw5cLhemTZuGuro6XHbZZUhOTobD4UBBQQFeeumlLv9+iA6WTbFH/pn3TDrYFAdsigMteiMWBN9DtbYL3wY+wxfBd1GvV6FVb8Kq4GIsCryPLwOz8F3wc9RoFR1yFwc+wrbQOjx1905cOHQDJh+/GZ++uXefE/BreHZqOS7/1SacN2A9Jp+wGe88G35Zj6tP3IylcxvxxfsNOPOodZhx504AQFV5+KXpMzIy2GWiTthMrsg/857FN+23m4N1mF/xAqq8O/B11dv4vPxZ1Pl3oTXYgBW1n+DLiv9iXvnz+LrqHVT7SjvkLtz9CoqbfkDNK++g9NY/Yec9f0XzV99GPq8Hg6h98wOU3fkwSm6+Fzun/BUNn34JANh573S0rViLlm+Xo+SGu1DzcvhVfIJ19QDYZ6LOdDg2wwIFSuR2CxqxUH8f1Xo5vtPm4Uv9PdSjGq16M1ZpS/GV9hEWaLPxvTYfNfruDrlLtE+wTd+Aqrffwvb77kXJXx5G47ffRD6vB4Oonj0bJQ9NxfZ77kbJX/6C+i++AACUTvsLWteuQfPyH7Dtjj+i6q03AQDBujpUvvYqAKBPnz7sMtHPtI+rbYoDZvxsrK014Evv26gK7cQ33jmY730T9VolWrUmrPQtxMK2Wfii7S18652DmlB5h9yvvO9ja+BHvH7fBtw56is8ePLXWPrOrsjng34N7z68GX86YSluH7YID578NT5/YQcAYOqEb7D68yp8/2EFbh2wAK9N2QAAqKvwAuCxmWh/In02u2FWwxcEbGY3bGY3Wnw1+GLzDFQ1F+ObbS9h3qa/ob61FK3+Oqwom4UFW/6O+ZuewDfbX0ZNy/YOuYuKnsG3/9mAuQ/+gKfHfoDnT/sEq2dtjXw+FNAwf/pKPDvxYzx59Gw8f/ocfPff8MvPv3D6HGyZvxPrP96Bx4fPwqf3LwMANFW0AmCfiTrzi2NtfBAea+vz8SVm7x1r60vxlf4/LNDfx/f6F/uOtfU52KZvwKY172Lp5/fjuwV/RXnJd5HPa1oQRes+wLdf/gWLP7sP3y2YjpLiBQCA7xY+gurdP6Jy5wp89end2LTmHQCAt60OG1a9AYBjbaLOdJzXtgJQYFOdsKnO8Fi76TVUBcrwTfNHmN80E/Wh3WjVGrGydT4WNr2JLxpfxbfN/0NNcFeH3K+a3sVW32qseuQLzJn0AuZd+Ap2fLQu8nktEMLaJ7/C5+e9hE8mPof5F72CLa8tBwDMv3gmyhcVo+yzTfjfif/Cyr+Gz6nbKpsB8NhMtD+Rsbbq3Duv3d7nYD2+qH4RVb4SfFP3HuZV/wf1gQq0hhqwomEuFlTPxPyq/+Kbutmo8Zd1yF1U8zoaPl6AmhffRenvHsDOO6ajeeHe47MeDKL2tQ9R9odpKLn+T9h55yNo+CR8fN555yNoW/4jWr5egZJr7kHNf8PH52BtPQD2magzNos78s+s7DlvNjlhMznREqzDF5X/RpVvB76pfhfzdj+Pen95+BpV3RwsqHwJ83e/gG+q30XNz65RLap8FVubl6P6hfew47cPofTWx9D05feRz+vBIGpe/gilN0/H9skPovTWv6H+w0UAgNLf/w2ty9ahZfFKbL/sPlQ9F34162BNAwB2mWh/9j13/sm8NpqwUJsdPncOfY4vtVl7z51Di/FV6AMsCL2H70Ofo0bvuIZkSeh/2Katx4bHPseiM/+Jpb/5D3Z+vCbyeS0Qwqanv8SSC5/Hwkl/x9Lf/Afb3wj3/ev/919UfVWEis834MuTn8T6Rz8DAHgrmwCwz0T7035sttpjYLbYAQWw2mNgtcegrakK3334J9RVbMTqL57Ct+9PQWPNdnibq7Hh65ew7OOH8O0H92H1l0+jfvfmDrnLP/0rdi+fj5IFb2Htf+7F+lcfRs36vdedtVAQZYtnY90rU7Hmhbux/tW/YPeK8Dny+tf+goata1C3+QesfvaPKPkyfN3Z31wPgH0m6k269FLq//znP1FUVISMjAzk5OTA5XJ1+PyKFSuEbNw999yDxx9/HHl5eYiPj0dpaSnOOOMMTJs2DTabDTNnzsTZZ5+NTZs2ITs7O/J1TzzxBB5++GHce++9mDVrFn73u9/hpJNOQmFhIf7+97/jo48+wjvvvIPs7GyUlpaitDR8ArRs2TJceeWViImJwdNPPw2HwwFN0yIL3xctWoRgMIibb74Zl1xyCRYuXBj5nkVFRXjvvfcwe/ZsmEx7n8H08MMPY8aMGZgxYwbuvvtuXHrppcjLy8OUKVOQnZ2Na665Brfccgs+/fRTAMDixYtx5ZVX4u9//ztOOOEEFBcX4/rrrwcAPPjgg5HcqVOn4pFHHsFTTz0Fs9mM+++/H+vXr8enn36KpKQkFBUVoa2trdP/V5/PB5/PF7nd2NjJe3oSCbQltAb9TMPhUFywwAovWpGkpCPfNAQKTCjXtmFVaDHGKmfAoezdn+zQNuGUIYm4+HdJWDK3Ec88UI4hx7jQJ8+Gj16pxXfzm3DPP/ogJcOCqvIAqnaFBxFPvZ+HJ+7YCadbxfUPpMFmV6FpOh67LTxx+cknn8But/f4LgPsMx15mxu/RmHMcXCaY2FWbfCGmpFky0GB51dQFRN2tW7EipqPcULK5XCY97565PbmlXDnnI6Y009G64q1qH3jfdj65cGSloKmL5eibfV6JF1/OcwJcQjW1SO05yJA2pT/Q81Lb0O12xB/yblQrGbomoaa/4Qv4MvSZ3aZjrQifS0KlGFw4KfH5jTkYzBUqCjXd2C1vgRjcTrsyt5Xdi7RNyOmz1mImzARLWtWo+a992DPOwrWlBQ0LlmM1vXrkHLFlTDFxSHUUI9gfT0AIOP3t6HqzTeh2m1IOPc8qBYLdE3D7pdehLLnGe4ffPAB7rrrrh7dZYB9piNvS2AlCi2j4FDcsChWePVWJJkykG8ZHu5zaCtW+hfiONs5cKg/GWsH12PEoFycen0OVn1ehXce2oT8MXFI7evEotfK8OOCalz95CDEp9tRV+FFfUX47/qP747Ca/dsgN1lxgX35sNiN0HTdMz843oAPDYTHY7NVQtRmHIynJY4mE12eAONSHYdhYLkk8Jj7YYfsaJsFo7Puw4OS2zk6354dQuOu2kQjvltf2yeX4b5f12BrNHJSMj1YMUbW1C8aBfOfuwYeNKcaNrdFlncfvnrEzDn/u9hc1lw8l3DYbaZoGs6Pp4SvijIPhN1TRF+RAGGdhxrI33vWBs7sBpLMVY/reNYG1uQE3sGso8aj6qKtdiy7n3EJuTB6U7Gzu1LUVO5HgOGXwabIw6+tnr4vOEFNyOOvQWb1rwNs9mOowaeA1U1Q9c1rFs+E6opPP3PsTZR12zx/YBC+xg4VA8sig1erQVJ5j7It40K9zlQjJWt83Gc+9dwqHvfzXiHfx36Fx6DgstHYdfCYqyZsQiJwzPgzo7HtllrULF0G0Y9NAmOVDfaKpvh3bO4/YQXLsLKafNhcVox6NbjYbKZoWs6Vjz0OQAem4kOx+aW71DoPhZOkwfmPX1OtmajwHU0VJiwy7cZKxrm4viES+Aw7Z3XbvpsMWLPPwUxZ45H6w8/ovbVD2ArzIMlPRlN879G26r1SPrdpeF57dqGvfPa99+Cmv+8A9VhR/ylZ0PZMxdW8/xbANhnoq7a3PQtCj1j4TTFRK5RJdtyUOA+Jnze3LYJK+rm4PjkSzt0eXvLKnj6norYc8ah5fsfUfPiR7D37wtLRjIaP/sGrSs2Ivn//h/MibEI1jbsXdz+8E2ofvZdKA47Eq48E6o13OWqf4VfqEmWLgPsMx15RdpqFKjD4YAbFlj2riFRhkCFCeX6dqzWlmCsejrsP1lDUqJvQt/C45Fz2dGoXLQZm576EnFD+8CVnYCy2StR/c1WDH7gTNhSYuCraoosbh/97P/D+kc+g9lpRcEt42Cyhsfa66aFuyZLn9ll6g47fpyD3CFnwe5KgMnqhL+1HvFp/ZE96HSoqglVJcux8euXMGLSXbA5977zQtXqRUg7+jSkjpyI+uLVKPvqPbjSj4I9PgXVaxejcfs65JxyJayeOPib6xHYs7i93wW3oeSLN6Fabcg8/jyoJgt0XUPJvNcAsM9EvUmXFr+fd955gjejc3/+859xyimnRG4nJCRg2LBhkdsPP/ww3n//fXz00Ue45ZZbIvefccYZuOmmmwAAd999N5588kksWLAAhYWFKCkpQUFBAY4//ngoioKcnJzI1yUnJ8Nms8HhcCAtLQ0AMG/ePKxduxbbtm1DVlYWAGDmzJkYNGgQli1bhjFjxgAIv6L8zJkzkZzc8e00rr76alx88cWRbTn22GNx//33Y9KkSQCA3//+97j66qsjj3/ooYdwzz334KqrrgIA5OXl4eGHH8Zdd93VYed46aWXdvi6kpISjBgxAqNHjwYQfhX7/Zk+fToeeuih/X6eSLSjTIORqKZFbltgg8e0d0CTbxqKSm0nqrSdyDb1i9yfpKTjrMvDFw0uuiEJH75YgzXftqBPng1VuwLIyLVi0GgnFEVBSubet6KJTTTDYlVgtatISA4vqFu5pBklReFBwYgRIxATE9Pjuwywz3Tk5XuOQZJ97xPOrKodMZa9b/VYEPMr7PZuRaVvG3LMQyP3J9tzYR43FgAQM2kcmuYvhndTMSxpKQjW1sOckgRbfi4URYE5ce/+weRxQzGboVgtMMWGJyrb1m9GoLwSgDx9ZpfpSMtTBiFRSY3ctsAKD+Iit49SBoePzdiFLORH7k9EGhzHHQcAiB1/Mhq++greoiJYU1IQrK+HJSkJtr59oSgKLAkJka8zud1QzCYoFgvMMTEAgLbNm+CvqEDm7X/Ezsf/htGjR/f4LgPsMx15+ZZhSDSlR25bFBs86k/G2upw7A6VokorQ7ZaGLk/yZSJEy7NBABMvDYbC18pxZbv6pDa14m6ci+ScxzIGxV+l7aETHvk6zwJVpitKix2FTHJ4Vfh2ri0FhXFLQB4bCY6HAVJJyDJ1Tdy22pyIMa+93hdkHwiKps2o7K5CDnxoyL35x2fhhGXHAUAOPrqQix/bQtKllUiIdeDxoo2xGe7kTkiCYqiIDZj78VCZ4INZosJZpsJrqRwz7d/sxs1W8OT6OwzUdfk4RfG2hiMSn1Xp2PtjJxjAQBZeeOwc/sS1NcWw+lOhs9bD4crCTHx4fNmu2Pvsd5qc0NVzVBNFlht4fPmuurNaGmuwKjjbsPyJTM41ibqonzbCCSaMyO3LSYbPKa957r5ppHYHdyBqmAJsq0DI/cnmfsg9/wh4cdcNhJb312N6pU74c6OR1tlE9x9YpEwNB2KosCZFhP5OlucA6rFBNVmhj0xfMyuWlaK5u3hd0TlsZmo6wpcY5Bk7RO5bVXtiDEn7v28eQwqfdtQ6d+BHMfgyP32oYXwnBw+PseccRKa5i2Bd2MxLOnJCNbUw5yaBFvBnnntpJ/Ma8e4oVjMUKzmvfPa67YgsCv8ypPsM1HXFLiPRpItK3J7n2tUnmPCXfZuR45rSOT+JFsObKf8CgAQe/aJaPx0KdrWb4UlIxnB6gZY0hJhK8wJdzn5p112AXu6bI7b0+W1RQjslOsaFcA+05GXpw5BovKzNSTK3v4dpQxBZagMVfouZCkFkfsTlXT0OTe8bizn/41B6XsrUb+qFK7sBHgrm+DMjEPskEwoigLHT8ba1jhnZKxtSwiPtWt/2IGW7TUA5Okzu0zdIXvgqYhL3bvWy2J1whWXsffzg05Dza4fUbtrPdLzj4vcH5MzAEmDw7dTRpyM6jVfoXlXEezxKQg018MWmwRXevi6s9Wz91zc7HBDMZmgmi2wOMM9byrdBG8dx9pEvU2XFr//tKRGai96u+bmZkydOhWffPIJysvLEQwG0dbWhpKSkg6PGzp072I7RVGQlpaGysrwCcjkyZNxyimnoLCwEKeddhrOOussnHrqqfvdhg0bNiArKyuy8B0ABg4ciLi4OGzYsCGyc8zJydlnx/jzbUlNDV/0GDJkSIf7vF4vGhsbERMTg9WrV2Pp0qWYNm1a5DGhUAherxetra1wOp2d/t/87ne/wwUXXIAVK1bg1FNPxXnnnYexY8d2+jNNmTIFt99+e+R2Y2Njh5+PSLQYJaHD7aAewFbtR1Rr5fChDTp0hBCCF60dHudW4gCEX81dURTEJ5tRXxO+PfGCOPzpqh24fmIRRp3oxtEnezDyBDf2p7TIh8RUC6p2BSL39fQuA+wzHXmx1pQOt4OaH0VN36PKtwO+UEu4z3oQ3mBTh8d5LIlof76qoigwxXqgNYVf1co9dhQqn/oPyh/4G+yDCuEYOgCOgf2wP8GKSpjiYiKvogP0/D6zy3SkxeDnx+YgturrUI1y+OGFDi18bNZbAWXv49xKHEJ7PlYUBWaPB6HmPV0ePQYVLzyPskcfgbN/fzgGDISzsBD7499dCXNsHMxxcZH7enqXAfaZjrwYNbHD7aAeQHFwDapDO+HTfzLW1ls6PC481g5TFAWeJCuaavwAgGPOS8e/frsKfzn9Oww4IQGDxiVhwHEd9xs/tXtrK2JTbajbtfcVKHp6n9ll6g4x9vQOt4OaH8XVS1DVXAxfsBm6roXH2oGOr/CSVLD3VeAVRYEryY7W2nAfB5+Tg3dvXIwXz/0Mucel4agT0pA7Ng37U7utEZ4UBxrL956fs89EhyYG8R1uB/UgtmIdqlHRcaz983kwdOyy1eZBwBcea6dmjsbaZf/BD189jvjkfkhIHoCE5P2fN7c2V8Fmj4XNvjezp3cZYJ/pyIsxJXW4HdQDKPatRHWwDD6tbW+ftZ+NtX/yZFRFUWBPcMJfF54Z63PaAHz7xw+x4LLXkXJMNlKOzUXK0dnYn6YdtbAnu9C2uzlyX0/vM7tM3SHG3LEfQT2A4pYfUOUvgU9rDY+1EYI31NzhcdY+e8fOiqLAFOPeO699/ChUPv5flN/7BOyD+8ExrD8cgw8wr72rEqb4WIRq6iP3sc9EhybG8rMuawEUN++5RqW1ho/Neghebd9rVP49HyuKAlOcB1pj+PjtPnEEdj/yEnbe8SQcQ/vBOaIQjqEF2J/ATvm6DLDPdOTte+4cCF+n0nftOXfezxqSnzy5XFEU2OKd8NeHx9rpkwZi5V2z8e1VLyNxTC4Sf5WHxDE52J+WklrYktzwVe7dZ/T0PrPL1B1c8R3/xkJBH0rXf466io3wexuhaxq0UAC+troOj7Mn7J0PVxQFZqcHwbbwWDu+cAy2/u95bHzzEcRk90dMzkB4svZ/3dlbVwmLKxaB5r3fg30mkl+XFr8DQH19PWbNmoXi4mLceeedSEhIwIoVK5CamorMzMxfDjgILperw+077rgD8+bNw+OPP478/Hw4HA5ceOGF8Pv9HR5nsVg63FYUBZqmAQBGjhyJbdu24dNPP8X8+fNx8cUXY+LEiZg1a5bQbe1sWxRF2e997dvX3NyMhx56CL/+9a/3ybLb977a3s+/3+mnn44dO3Zgzpw5mDdvHiZMmICbb74Zjz/++D45NpsNNpvtYH80osNm+tmuZrO2CrVaBfqZRsChuGGCCauDS6FD6/A4BWrHIEWBvuch+YMdeHFhAX5Y1IxVS5vxyP+VYvhxbtz7r8M70PekLgPsMx15JqXjMXZT41JU+0rRP+Y4OM2xUBUzVtXOhfZLfQag6zoAwJrdBxnT7kHbjxvh3ViE6hdeg31AAZJvuOKwtrUn9ZldpiPNBFOH21v01ajFbhQow+CEGypMWKN/vU+XVSiRxe8AAEUB9nTZ1qcPsu69D60bN6BtyxZUvToT9oJ+SN3zjPeu6kldBthnOvL2GWsHVqBGK0c/y0g4FQ9MMGO1/yto+s/6rHQ8NiuK0l5nZA3y4MH5x2LDVzXY9E0dXv7DOvQ7Nh6/fXowDkdP6jO7TN3BpP5srF35JWpatqMwZTyclnioqgWrd74PTQ91/DpzJ2NtLVzo1AHxuO6T07FtaQV2fFuJ/931HbJ/lYJzHz/2sLaVfSbav33G2lgTHmtj6N6xNr7pZKzdSZcR7rInNhNHn3Q3aqs3ob56Czaseh3xifkYOLL3nDcD7DMdeSblZ2Nt7zLUBHehn30MnKoHJsWM1a0LfrnPyt5jc1xhMia8fSUqv92B6uVlWD71MySPysLoh087rG3tSX1ml6k7/LzPm5q/QY1/Jwrdv4LTFANVMWN1wzxoHWe+AFPH43r4OtWeee2cTGQ8dhfa1m6Cd30Rqp99A/aB+Ui++fLD2lb2mWj/9rlG1bQUNf4yFHrGwmkKX6NaXT93n3mwA12jsvXNRJ8n70Db6s1o+7EYlf94C45BRyHltksPa1t7UpcB9pmOvJ/Pa2/RV6NWr0CBOnzvubP29T7HZvWnr9gEdDg2e/qlYuzr16Dm++2oXV6CdX/+BPGjsjBk6tmHta09qc/sMnUHk8na4fb2NR+jvnIzcoecBbs7CarJgk3fzoSudeyzov5srI29152dyX0w4PL70FiyAc1lW7D985nw9OmH3Em957oz+0z0y7q0+H3NmjWYOHEiYmNjsX37dlx33XVISEjA7NmzUVJSgpkzZ4reTgDA0qVLMXnyZJx//vkAwjuS7du3H3JOTEwMLrnkElxyySW48MILcdppp6G2thYJCfu+kt2AAQNQWlqK0tLSyLNn1q9fj/r6egwcOHCfxx+ukSNHYtOmTcjPz//lB/9McnIyrrrqKlx11VU44YQTcOedd+735IWoOzVo1chQ+yJFDb/FZFAPwIuWX/iqfTk9Jpx4VixOPCsWx50eiweu3oGm+iA8cfvu2rLybajZHehwH7tMdPjq/OXIdA5AquMoAOFXp2wLNQI4tCfCqQ47XGOGwzVmOJwjh6Dq7/9FqKUVJpdzn8ea01IQqu/4apfsM9HhqUc10pVcpCjh7gb14D6vpnEwVLsd7uEj4B4+Aq1Dh2L3v/+NUGsrTM59u2xNTUGwoR7B+vrIfewy0eGr16qQYcpDqin86pFBPYA2vRnxSD2kHIfbjJFnpGLkGakYPikZz163Bi31AbjiLPs8NjXPiYbdvg73sc9Eh6++bScyY4cg1RN+RZug5kdboOFnr4v1y2xuC/pPykL/SVnod0om3rtpCdoa/HDEWvd5bELfGDRVtnW4j30mOjz1qEY6cg57rG222JGSPgwp6cOQlDYEP/7wIgL+Vlis+461ne5k+LwN8HkbIvexy0SHrz60GxnWfKRawq8eGdQDaNOaD/nYbHFZkTmhAJkTCpA+7ih8d8f/4G/0whpj3+exnpwEeKs6zp2zz0SHrz6wG5n2fki19QXQ3ucmxCP9F76yI9Vhh+voYXAdPQzO0UNQNeNFhJpbYXJ3Mq+dkYJQXUOH+9hnosNTH6hApqM/Uu15AMKvBN8WajrkY7PqtMN17NDwv2MGY/ejL++3y5ZMdpnICPV6NdKVvkhRfr6GZN9XaD4Qs8uG1PGFSB1fiJSTCrD67vcRaPTC0slY25WdAF91x3emkf6vAAEAAElEQVR9YZ+JDl9TzXak5IxGYmb4ldNDQR98rXW/8FX7MlntiM8fgfj8EYjNG4ptn/wbQW8rzPZ9j8/2+BQEWnh8JupturT4/fbbb8fkyZPx2GOPwePxRO4/44wzcOmlh/cM2AMpKCjA7NmzcfbZZ0NRFNx///2RZ9QcrBkzZiA9PR0jRoyAqqp49913kZaWhri4uE4fP3HiRAwZMgSXXXYZnnrqKQSDQdx000046aST9nlrChEeeOABnHXWWcjOzsaFF14IVVWxevVq/Pjjj/jLX/5ywK8bNWoUBg0aBJ/Ph48//hgDBgwQvn1EIjgVDyq1MiSr4Yt+RaG1kVeyOljv/7ca8ckWHDXIDlUFlnzagPhkM1wxP39mYNjw41zIzrdh+yYfVq1aBbvdzi4TCeAyx2F3WzFS7LkAFGxp/PaQ+9w47yuYYj2wZmcCioLW5WugxnigOvadhAAA+4ACWNJTENhZwT4TCeKEB5X6TiQjAwBQrP94yF1uWLQIphgPrJmZUBQVLatXw+TxQLXvp8sF/WBNS0P1u+8CAJYvX44777yTXSY6TE7Fg8pQKZJNfaBAQVFg1SG2Gfjy5RLEJtvQZ4AbiqJg5dwqxCRZ4YjpfAqhcGw80o5yYdfmFh6biQRyWuKxu2kTkt3hCfSi6sWHfHz+4dXNcCXZkdI/DoqqYPO8MriS7LB79n0iCwDk/CoFiXkxqN7SwD4TCeKEG5XYhWR9z1gb6w65y2XbvoLVFgN3TAagKKiuWAurzQOzpfOxdlxiAVzuNGz58T0AHGsTieJUY1AZ2IFkc1Z4rO1bcch9Ln57FeyJTsQUJENRFZQvKIItwQmLu/NXc0sa3Qfu3AQ0ba3hsZlIIKcpBrv925BsywGgoKhl2aHPa3+2ODyvnRM+PrcuWwM11gPVuZ+5sIH5sGSkIlDGeW0iUZymWOz2bkWyLRcAUNT8/aHPa89ZAlOcB9bcDCiKgpbv1sIU595/lwcfBUtmCgKlu9llIoGccKNSL9t77qwd+nWqkneXw5rggqcgBYqioHLRFlgTnDDvZ6wdPyobrtxEtGytZp+JBLK7k1C780ckpA8EoKBk/WeRV3Q/WFWrF8Hs9MCRFL7u3FC8GmanByZb58dnd59+sMenwltbzj4T9SL7vr/TQVi2bBluuOGGfe7PzMxERUXFYW/U/syYMQPx8fEYO3Yszj77bEyaNAkjR448pAyPx4PHHnsMo0ePxpgxY7B9+3bMmTMHqtr5f4WiKPjwww8RHx+PE088ERMnTkReXh7efvttET/SPiZNmoSPP/4Yn3/+OcaMGYNf/epXePLJJ5GTk3PAr7NarZgyZQqGDh2KE088ESaTCW+99ZYh20h0uPqZhsOsWPF9cD5WBRcjSUlHjHJoz8F3uFS89+9q3HbeVtx23lbsLgvgof9mQ1WVTh+vKArueir8LOEzzjiDXSYSpDDmeFhUG76tfg8raj9Gkj0bMZZDewa+areh8fNFqPjr31Hx138gWFOHlP+7BsoBjs2J14afbMc+E4nRTxkGCyxYpn+JVfoSJCpp8Bzi6+MoNhsaFizArqeews6nn0Kwrg6p1157wC6nXn1N5Iku5557LrtMJEChZVR4rO37DCv9C5BoykCMemh9tjvNmP/fEvztouV4/OIfULvTixueH3rAsfaVT4RfOYPHZiJx+qecDIvJju92vIqVZe8hydUXMbZDexcHq9OMZS9vxmuXfoHXLvsCDbta8et/HgflAH0+a/rRANhnIlH6Yc9YGwuwCkuRiFR4EHdIGSazDaVbF2HF1//Ayq//AW9bHQaPuhqKsv+x9qBRV8Jk5libSKRC+9HhsXbLJ1jZOh+J5kzEmBIPKcPstKDozZVYfP07WHz9u2itaMIxj511wGPzyAdPBcBjM5FI/d1jYVFs+K7uQ6xsmIskax/EmJMOKUO129A4dxEq/vxPVDz8TwSr65Fy2+QDz2vf8BsA7DORKP09x8Gi2vBdzWysrJuDJGsWYsxduEb18WKU/+kZ7Lr/GQSr6pFy51UH7HLyzZcAYJeJROqnjoAFVizTvsAqrWvXqUwOK0re/gE/3PgGlt30BrwVDRg2/fwDjrUH3Xc6APaZSKTcoWfDZHVg7cJ/YcPXLyEutR9ccZmHlKFabKhctQBbZj2Fze89BX9THfqece0B58KyT7kcAPtM1Jsoun6IT60BkJKSgs8++wwjRoyAx+PB6tWrkZeXh3nz5uGaa65BaWmpEdtKBmlsbERsbCwmpl0Ps7rvW153VbDcgCdCKJ0PSqON6tz3LVYOh9bS8ssPigKKuUtvJnFAt21cKyyrtSmEi4ZvRENDA2JiYoTlRpNIn1OvE9pnran5lx90iBS3S3imbsB2qrFi/1Z0r09oHgAoNnG/672h4ve36x/MFpaltXlRdtsD0va5vcvjbRfDrHT+qpxdofv9wrLamfse+ASuK3ZclCE8s89Ty4Xm6YGg0DwAgBYSn2lAl4sf+5XQPM3rxY4/3Sd9n8epvxbaZyOY4mOFZxpx3NODYvtnxL4R+5l8OxyqVfzfz1krdgrLamsO4u4xi6Xv8sn2i2FWBI6zvV5hWe2MOC/TQwYcowRT3W7hmVpLq/BMU0Ff4ZlD3ioSmudrDuAfx38ofZ/HKeeJPTYf+hSrNFSX2PP7Pl8KjQMAlBzTM+bW/KeNEZoXDHjx9fwHpe/zxIwbYFY7fyW/rtANOD4bQWs24O9a8DHfiHGJEeMn9SfvcCyK7WNx1wgCLX7MPf3f0nd5+KXTYLJ2/ip+XRH/6vfCstrtb+HUYYYKj1RdDqF5igFj7eAu8dcQTQlxwjM3PCp2/K61eVF281Tp+9xbz51h6vzdqaOJYsA5qW4T/3OrZVXCMzc9cWiL7g5Ea/Wi5LqHpe0yYNy8thHHUtHXcgEg2F/cNc125vU7hOZpTU1C8wDxc+8AAFX8PkKbJ+46YrDFh6/OfkbaPkfOm7NvEnreHNpZLiyrnRp/aE92OBhaY6PwTEXw8V7xiB9r68kJwjOb+4m/htiSIvZcKOT34sf/yn/NeULCZLHrwQyYX1INWA+mNYpfD6YHA0LzFKv4tVui9zmAMedCuV+Iu97ub/bjpXHvHFSXu7QXOeecc/DnP/8ZgUD4D0BRFJSUlODuu+/GBRdc0JVIIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqL96tLi9yeeeALNzc1ISUlBW1sbTjrpJOTn58Pj8WDatGmit5GIiIiIiIiIiIiIiIiIiIiIiIiIiIiIerkuvYZ9bGws5s2bhyVLlmDNmjVobm7GyJEjMXHiRNHbR0eQ7nFBN4l7mxuIf5ebHvMW0lqL2LcEUUYNEpoHAPrydeIzDXgbrHerxb3ds7/ZD2CjsLxoFqpvgCLyLegMeBsVvUn8W9JAEf9WeXpA8Nvc2AXuZ/fQPeLfMggV4t9S0hov7q1JNVvPeAvywxYKGfLWxyIZ8bZ22bPF/8zb7xglNC/7sR+E5gGArol9e3mjqGJ3i4DovCilmExQFHHHU9FvwwYY83Zxikl8n/WA2DGnEeMc7ZjBwjOVdduEZ87aOVJYVrDFB2CxsLxopTgcUAS+dbtiwDmu6rALz9TaDBh7hcQe99Q48W+7qsZ4hGfqtQ3CMz/dMUBoXqjVJzQvaimq2LG23jPGckYQPQ9WcozQOADAZRvLhGe+PjBHeKa9XOx4LBjqHX3WfX7oIoedackCw8JKT08Unpn1v0rhmfqu3cIzhfOJ/7vW29qEZzZM7y8sKxjoHfNgrt0BmM3iz8+inRHnzRD8Fua+glSheQBg3i1+/tkIapPgt4P3in97+WikOJ1QVHHnzvCK3w8acU3TiDkmRXCflZJdQvMAoOGMgcIztf7iz8eT54rLCvl1lIiLi2qq1QxV4DVnI7oHwfPFAGAuEt8VWMX9PwKAYhN/zTl03BDhmdZi8ectG37MFJZlyLxnFNKdDqHrwUzpacKy2oUqxY8PTenix7Gi9zlaTa3QPMCYawR+V7zwzMQNYvsXDPaOPmt906GZxP2O9eXrhWVFGHBsVgQfRwFAjXGLDRQ8dgcAvVns3DsAQ+Y+P1siLlPzegG8c1CP7dL/+NatW5GXl4fjjz8exx9/fFciiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgOWpdeTiA/Px/jx4/Ha6+9Bq8Bz7QmIiIiIiIiIiIiIiIiIiIiIiIiIiIiIvqpLi1+X7FiBYYOHYrbb78daWlpuOGGG/Ddd9+J3jYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgBdXPw+fPhwPP3009i1axdefPFFlJeX44QTTsDgwYMxY8YMVFVVid5OIiIiIiIiIiIiIiIiIiIiIiIiIiIiIurFurT4vZ3ZbMavf/1rvPvuu3j00UdRVFSEO+64A1lZWbjyyitRXl4uajuJiIiIiIiIiIiIiIiIiIiIiIiIiIiIqBc7rMXvP/zwA2666Sakp6djxowZuOOOO1BcXIx58+Zh165dOPfcc0VtJ/3MuHHjcNttt3X3ZhCRAOwzkTzYZyI5sMtE8mCfieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZyJ5sM9ExjF35YtmzJiBl156CZs2bcIZZ5yBmTNn4owzzoCqhtfS9+3bFy+//DJyc3NFbiv9xOzZs2GxWLp7M4hIAPaZSB7sM5Ec2GUiebDPRPJgn4nkwC4TyYN9JpIH+0wkB3aZSB7sM5E82Gci43Rp8fuzzz6La665BpMnT0Z6enqnj0lJScF///vfw9o42r+EhITu3gQiEoR9JpIH+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwT4TyYFdJpIH+0wkD/aZyDhqV75oy5YtmDJlyn4XvgOA1WrFVVdd1eUNowP76VtiPPPMMygoKIDdbkdqaiouvPDC7t04Ijok7DORPNhnIjmwy0TyYJ+J5ME+E8mBXSaSB/tMJA/2mUgO7DKRPNhnInmwz0TG6dIrv7drbW1FSUkJ/H5/h/uHDh16WBtFB++HH37ArbfeildffRVjx45FbW0tFi9e3N2bRURdwD4TyYN9JpIDu0wkD/aZSB7sM5Ec2GUiebDPRPJgn4nkwC4TyYN9JpIH+0wkXpcWv1dVVWHy5MmYO3dup58PhUKHtVF08EpKSuByuXDWWWfB4/EgJycHI0aMOODX+Hw++Hy+yO3GxkajN5OIDgL7TCSPQ+0zu0wUnXhsJpIHj81E8mCfieTAsTaRPHhsJpIH+0wkB461ieTBYzORPNhnIvHUrnzRbbfdhoaGBnz33XdwOByYO3cuXnnlFRQUFOCjjz4SvY10AKeccgpycnKQl5eHK664Aq+//jpaW1sP+DXTp09HbGxs5F9WVtYR2loiOhD2mUgeh9pndpkoOvHYTCQPHpuJ5ME+E8mBY20iefDYTCQP9plIDhxrE8mDx2YiebDPROJ1afH7l19+iRkzZmD06NFQVRU5OTm4/PLL8dhjj2H69Omit5EOwOPxYMWKFXjzzTeRnp6OBx54AMOGDUN9ff1+v2bKlCloaGiI/CstLT1yG0xE+8U+E8njUPvMLhNFJx6bieTBYzORPNhnIjlwrE0kDx6bieTBPhPJgWNtInnw2EwkD/aZSLwuLX5vaWlBSkoKACA+Ph5VVVUAgCFDhmDFihXito4OitlsxsSJE/HYY49hzZo12L59O7788sv9Pt5msyEmJqbDPyKKDuwzkTwOpc/sMlH04rGZSB48NhPJg30mkgPH2kTy4LGZSB7sM5EcONYmkgePzUTyYJ+JxDJ35YsKCwuxadMm5ObmYtiwYXj++eeRm5uL5557Dunp6aK3kQ7g448/xtatW3HiiSciPj4ec+bMgaZpKCws7O5NI6JDxD4TyYN9JpIDu0wkD/aZSB7sM5Ec2GUiebDPRPJgn4nkwC4TyYN9JpIH+0wkXpcWv//+979HeXk5AODBBx/Eaaedhtdeew1WqxWvvPKK0A2kA4uLi8Ps2bMxdepUeL1eFBQU4M0338SgQYO6e9OI6BCxz0TyYJ+J5MAuE8mDfSaSB/tMJAd2mUge7DORPNhnIjmwy0TyYJ+J5ME+E4nXpcXvl19+eeTjUaNGYceOHdi4cSOys7ORlJQkbONo/xYuXNjpx0TU87DPRPJgn4nkwC4TyYN9JpIH+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwT4TGeegF7/ffvvtBx06Y8aMLm0MEREREREREREREREREREREREREREREVFnDnrx+8qVKw/qcYqidHljiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIg6c9CL3xcsWGDkdhARERERERERERERERERERERERERERER7ddBL36nXiAUAhDq7q2gTujL1wnPNKenCc8MVuwWnjkyZoewrDY1iDeEpUU3PRCELvCdOFSbTVhWO63NKzzTlBgvPFMPBsXmeX1C8wBAr6sXnyn45waAwrRKYVmBFj+2CkuLXrqmQ1d0gYECs9ojfeL/pkNF24Rn5r4jNq/2w2yxgQDizi8Vnqn7/cIzM5aI3T8EA0GI/41HIV0DoAmLU0wmYVnt9GBAeCZ08dsp+mf3nzREaB4A2FeK/6sO1TcIz/SHEoVlBUPif9fRSPf5hB6bVbdLWFZEL31HPs2AjihWi/DMUJ347RyYLHYuJ9Dix0ahidHJFB8Lk2oVlheqrhGWZSgj9hEGnGeI9ubwfOGZ2nEDhGcmPyp2DBFo8QMThUZGJcVmhaKKm7vSK2uFZbXLekV8phEUu11onjHHZ3H77oiQ+Osilde1CssKtfqA+cLiopauhP+JoljEXwI14t2vjZiHVR0OoXm2DTuF5gEAMlKFR2rV4ve1mlPs/kFTesd1WL21Fboi8G9bNWDOQRP/uzBiHyH6uKe3tQnNA4CY91cKz1Q9buGZI+eLu0blaw5gxdvC4qKbqgKKKi5PZJaB9IZG4ZlKTh+xeR7x84rqIvF9DhqwD88oFHf+F2zxQfzVueijeH1i62fAWBuaAfNLQfHHe9FzgG2nDhOaBwCub4qFZyZ9VSY8s/oksfvFkPjL4r2CyYDrVJoBa0jUuFjhmYrLKTRPN+D6j9bSIjwTW8SvttLj48RltR38moWeMbIkIiIiIiIiIiIiIiIiIiIiIiIiIiIiol6Ni9+JiIiIiIiIiIiIiIiIiIiIiIiIiIiIKOpx8TsRERERERERERERERERERERERERERERRT0uficiIiIiIiIiIiIiIiIiIiIiIiIiIiKiqMfF70REREREREREREREREREREREREREREQU9bj4nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiiHhe/S2ThwoVQFAX19fXdvSlEdJjYZyI5sMtE8mCfieTBPhPJgV0mkgf7TCQHdplIHuwzkTzYZyI5sMtE8mCficTh4vco4Pf7u3sTiEgQ9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZ6Low8XvBmhqasJll10Gl8uF9PR0PPnkkxg3bhxuu+02AEBubi4efvhhXHnllYiJicH1118PAFiyZAlOOOEEOBwOZGVl4dZbb0VLS0sk99VXX8Xo0aPh8XiQlpaGSy+9FJWVlQCA7du3Y/z48QCA+Ph4KIqCyZMnH9Gfm0hG7DORHNhlInmwz0TyYJ+J5MAuE8mDfSaSA7tMJA/2mUge7DORHNhlInmwz0Q9Hxe/G+D222/H0qVL8dFHH2HevHlYvHgxVqxY0eExjz/+OIYNG4aVK1fi/vvvR3FxMU477TRccMEFWLNmDd5++20sWbIEt9xyS+RrAoEAHn74YaxevRoffPABtm/fHtkBZmVl4b333gMAbNq0CeXl5Xj66ac73T6fz4fGxsYO/4ioc+wzkRzYZSJ5sM9E8ojmPrPLRAcvmrsMsM9Eh4J9JpIDu0wkD/aZSB7sM5Ec2GUiebDPRD2fouu63t0bIZOmpiYkJibijTfewIUXXggAaGhoQEZGBq677jo89dRTyM3NxYgRI/D+++9Hvu7aa6+FyWTC888/H7lvyZIlOOmkk9DS0gK73b7P9/rhhx8wZswYNDU1we12Y+HChRg/fjzq6uoQFxe3322cOnUqHnrooX3un5B3K8wm22H89B2FirYJyyLxzOlpwjODFbuFZ56zrlpYVltzEHePWYyGhgbExMT84uN7cp/Hqb+GWbH84s94sExul7CsdlqbV3imKTFeeKYeDIoNDGli8wDobW3iM0X/3ACsXyQJywq0+PH56S8cVJ/Z5Z/QQuKyjKQowiNN+X2F5tU+Lf45pHHnlwrP1A14+zXf6aOF5gUDXnzz+YPS93m8+QKxfTaAHhK/j1BMJuGZUMT2z3/SEKF5AGBfKf5cKFRTKzyzeW6esKxgiw8//Pppacba++vyyc7fwKxYf/HnO1iKY9/tPfxQ8cdRralZeCYE73MUh0NoHgAoVvH77VBdg/DM+MWxQvMCLX58cMrL0h+bJyReDbMqrs+h6hphWYYyYB+BHjC9rHbyN3W4gmMGCM9MfVTsGCLQ4sesiTOl7/PEjBtgVsXNa+v+gLCs3k6rF3/cg2rAfsyAc6HStwuFZYVafdhy+SPSd3nsxKkwm8Xtr20L1wrLaqcYcBw1Yh7WJPr6T8CA/aJN3DisnVYt/rx545MDheZpbV6U3Sr/PNjJ9ouFnjtrRhybDZgrN2LMKfpc15DrSQacD6get/DMkfMrhWX5mgOYcdzH0syDAUduLkwPiD/uqQbMrxlxbUXJ6SM20IAxrCFrfFTx1wha5uQIywq2+LDsIOe1e3KXJ+beIvS82Yj5pVDpLuGZptRk4ZmhKnFrmACg7dRhQvMAwPVNsfBMxeUUnll9ktj9Ysjvxco375O+z+NHTYHZJO7Yp27aISyrnebzCc9U48ReBwHE/13rBlz/CdXVCc80Yh++5aWRwrK0Ni9Kb3zooLrMV34XbOvWrQgEAjj66KMj98XGxqKwsONE5+jRHRcNrV69Gi+//DLcbnfk36RJk6BpGrZtCw8wly9fjrPPPhvZ2dnweDw46aSTAAAlJSWHtI1TpkxBQ0ND5F9pqfiFVkQyYJ+J5MAuE8mDfSaSR7T3mV0mOjjR3mWAfSY6WOwzkRzYZSJ5sM9E8mCfieTALhPJg30mkoO5uzegt3K5Or6KcnNzM2644Qbceuut+zw2OzsbLS0tmDRpEiZNmoTXX38dycnJKCkpwaRJk+A/xGed2mw22GwCn9FH1Muxz0RyYJeJ5ME+E8mju/rMLhOJxWMzkTzYZyI5sMtE8mCfieTBPhPJgV0mkgf7TBTduPhdsLy8PFgsFixbtgzZ2dkAwm+LsXnzZpx44on7/bqRI0di/fr1yM/P7/Tza9euRU1NDR555BFkZWUBCL8txk9ZreG3mwoZ8PZGRL0R+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwC4TyYN9JpIH+0wkB3aZSB7sM5Ec1O7eANl4PB5cddVVuPPOO7FgwQKsW7cOv/3tb6GqKhRF2e/X3X333fj6669xyy23YNWqVdiyZQs+/PBD3HLLLQDCzw6yWq34xz/+ga1bt+Kjjz7Cww8/3CEjJycHiqLg448/RlVVFZqbmw39WYlkxz4TyYFdJpIH+0wkD/aZSA7sMpE82GciObDLRPJgn4nkwT4TyYFdJpIH+0wkBy5+N8CMGTNw7LHH4qyzzsLEiRNx3HHHYcCAAbDb7fv9mqFDh2LRokXYvHkzTjjhBIwYMQIPPPAAMjIyAADJycl4+eWX8e6772LgwIF45JFH8Pjjj3fIyMzMxEMPPYR77rkHqampkR0rEXUd+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwC4TyYN9JpIH+0wkB3aZSB7sM1HPp+i6rnf3RsiupaUFmZmZeOKJJ/Db3/62uzdnH42NjYiNjcWEvFthNtmE5YaKtgnLIvHM6WnCM4MVu4VnnrOuWlhWW3MQd49ZjIaGBsTExHQpo6f0eZz6a5gVi7Bck9slLKud1uYVnmlKjBeeqQeDYgNDmtg8AHpbm/hM0T83AOsXScKyAi1+fH76C13uc2/tMrQe8tZZB3g2dVeZ8vsKzat9WvxzSOPOLxWeqfv9wjN9p48WmhcMePHN5w9K3+fx5gvE9tkAugFvr6eYTMIzoYjtn/+kIULzAMC+Uvy5UKimVnhm89w8YVnBFh9++PXT0o6127t8svM3MCtWYbmKY/+TqF0PFX8c1ZoMeOURwfscxeEQmgcAilX8fjtU1yA8M35xrNC8QIsfH5zysvTH5gmJV8OsiutzqLpGWJahDNhHoAdML6sHuGjVVcExA4Rnpj4qdgwRaPFj1sSZ0vd5YsYNMKvi5rV1f0BYVm+n1Ys/7kE1YD9mwLlQ6duFwrJCrT5sufwR6bs8duJUmM3i9te2hWuFZbU70CsAdpUR87Am0dd/AgbsF23ixmHttGrx580bnxwoNE9r86LsVvnnwU62Xyz03Fkz4thswFy5EWNO0ee6hlxPMuB8QPW4hWeOnF8pLMvXHMCM4z6Wdh4MMG4uTA+IP+6pBsyvGXFtRcnpIzbQgDGsIWt8VPHXCFrm5AjLCrb4sOww5rV7Spcn5t4i9LzZiPmlUOku4Zmm1GThmaEqcWuYAKDt1GFC8wDA9U2x8EzF5RSeWX2S2P1iyO/Fyjfvk77P40dNgdkk7tinbtohLKud5vMJz1TjxF4HAcT/XesGXP8J1dUJzzRiH77lpZHCsrQ2L0pvfOigumwW9l0pYuXKldi4cSOOPvpoNDQ04M9//jMA4Nxzz+3mLSOiQ8U+E8mBXSaSB/tMJA/2mUgO7DKRPNhnIjmwy0TyYJ+J5ME+E8mBXSaSB/tM1PNx8btBHn/8cWzatAlWqxWjRo3C4sWLkZQk7hVviejIYZ+J5MAuE8mDfSaSB/tMJAd2mUge7DORHNhlInmwz0TyYJ+J5MAuE8mDfSbq2bj43QAjRozA8uXLu3sziEgA9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZ6Kej4vfKULxB6CoandvhhQUs9hq6cGg0DwACJZXCM+svv5Y4ZlPrvQKy9JavQAWC8vrTRSPW3ymrgvPhM0qPtPjEptX1yA2D4DicAjPhM8nPHLtlj7CsrQ2cfuGaKaYTFAUk7A8XdeEZUUo4scOikncz9xO37VbaJ77sUKheQDgnTBUeKZz+Q7hmTvHiR3naF4z8LnQyKikmE1QFIH/dwb0RAmFxGdaDTg2q4rQOOviH4XmAYABe1uYkhKFZ2Z56oVlBVQ/fhCWFr3UmBioqri/a62hUVhWO8VqEZ6pOuzCM0WP54z4uRW34HMBACZV/P57RUmG0LzwubP8tIZGaIr4v5uoZ8S5eA+gecX/XatL1wjPfCVnmdC8xiYNs4QmRic91gPdZBOWpzS2CMsyku42YD4oJHYkGxycJTQPACzfrBeeCQPm1lL+7RSWFQyo2CIsLZopgCLufE/3+4VlRTINmAeDAfN1Wm2d0DzVgOsDIcFzdQCgusR3OSunWmhesMWHMqGJUUpVxc4bGzKvLXZ+CQA0A66rKIKvEeua+PMBI+bztYYm4ZmnxqwVltWiaJghLC26KVYLlCg/d1ac4vf/RowjUF0rNM43vK/QPACwGXAdWzfgmu6uTSnCsnrLNedgcgxgFjfHq7YFhGVFMmPEjzm1BI/wTKW6Rmie88t1QvMAQDNgjZnJJm7epZ3fI3Y8FvKLH99Fo5YMJ8wWcX12rWwVlhVhwLmzVi/+GGUSvE7WiPWdhjDgGoFqEXjOFjj4LK50JiIiIiIiIiIiIiIiIiIiIiIiIiIiIqKox8XvRERERERERERERERERERERERERERERBT1uPidiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKIeF78TERERERERERERERERERERERERERERUdTj4nciIiIiIiIiIiIiIiIiIiIiIiIiIiIiinpc/E5EREREREREREREREREREREREREREREUY+L3yU0btw43Hbbbd29GUR0mNhlInmwz0TyYJ+J5MAuE8mDfSaSB/tMJAd2mUge7DORHNhlInmwz0TyYJ+JDh8XvxMRERERERERERERERERERERERERERFR1OPidyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKKeubu3gA68nw+H3w+X+R2Y2NjN24NER0O9plIDuwykTzYZyI5sMtE8mCfieTBPhPJgV0mkgf7TCQP9plIDuwykTzYZ6Jfxld+74WmT5+O2NjYyL+srKzu3iQi6iL2mUgO7DKRPNhnIjmwy0TyYJ+J5ME+E8mBXSaSB/tMJA/2mUgO7DKRPNhnol/Gxe+90JQpU9DQ0BD5V1pa2t2bRERdxD4TyYFdJpIH+0wkB3aZSB7sM5E82GciObDLRPJgn4nkwT4TyYFdJpIH+0z0y8zdvQF05NlsNthstu7eDCISgH0mkgO7TCQP9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJg30mkgO7TCQP9pnol3Hxu4QWLlzY3ZtARAKwy0TyYJ+J5ME+E8mBXSaSB/tMJA/2mUgO7DKRPNhnIjmwy0TyYJ+J5ME+Ex0+tbs3gMSbMGECpk+f3t2bQUSHiV0mkgf7TCQP9plIDuwykTzYZyJ5sM9EcmCXieTBPhPJgV0mkgf7TCQP9pno8HHxu4SKi4uxe/fu7t4MIjpM7DKRPNhnInmwz0RyYJeJ5ME+E8mDfSaSA7tMJA/2mUgO7DKRPNhnInmwz0SHz9zdG0Dibd++vbs3gYgEYJeJ5ME+E8mDfSaSA7tMJA/2mUge7DORHNhlInmwz0RyYJeJ5ME+E8mDfSY6fHzldyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKKelz8TkRERERERERERERERERERERERERERERRj4vfiYiIiIiIiIiIiIiIiIiIiIiIiIiIiCjqmbt7Ayh6hCoqoSiW7t4MKejBYHdvQrdIeuEb4ZnLp64SltXYpCFeWFqU00KAIu75TcGdu4RlGUlrauruTaAD2HbGf4Rl9ao+RztdEx8ZNCIzIDTPtGil0DwAMOm68EzdbheeufrSp4XmNTZpyLxfaGRU0rw+aIr4v+2o5/V29xZII+TzCc98q+8qYVmNTRo+EJYWvYK7KwGR580G7PvRKj6yJzCiI6ipFZ9pgM0nzROa11vG2nooBF3geTMJpCjiM43Y3xpwLkRdE9qwhfPaUcq0WXymIc0z4LxlwYv/FpbV2KQhvp+wuKjl+H4LzIpVWF5IWNJeiir+GKUbsKFam9i/aa3VgJMMA47NWrP4PcRXQxYJzestY22ttQ2a0juvlYrWE64565oBOzIDzglOFDhV3ij2ckNUC9U3ih1rG/C71drahGcaQvCY0/yF+HkrI8ZPRth60XPCshqbNMTfLSwuei1bJ3Reu8fMiPSQ+d2ewIg1QyvvmyM0r7FJQ/wLQiOjkvN/P8AssM8GzJr2GMHyiu7eBGkUjX9JWNahnDfzCg8RERERERERERERERERERERERERERERRT0uficiIiIiIiIiIiIiIiIiIiIiIiIiIiKiqMfF70REREREREREREREREREREREREREREQU9bj4nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiiHhe/ExEREREREREREREREREREREREREREVHU4+J3A4wbNw6KokBRFKxateqIfd+FCxdGvu955513xL4vkazYZSJ5sM9EcmCXieTBPhPJg30mkgO7TCQP9plIHuwzkRzYZSJ5sM9E8mCfiXo+Ln43yHXXXYfy8nIMHjwYAPD+++/jV7/6FWJjY+HxeDBo0CDcdtttkce//PLLkR3bT//Z7fbIY/4/e/cd3lZ5/2/8fSTvFdvZ29mEJJAAYY+w+mMU0rILFFIKZZR+KaPQQNkjQNllBEpLgEKBQqCE0UJCAoQNWRDIcvbesR1v6fn94cTgkmEpj6xzHt2v68oVW5Y/fjxunXPkY2nEiBGNl2dkZKh379665ZZbVF9fL0k68MADtWLFCp122mkt+rkCLqNlwB30DLiBlgF30DPgDnoG3EDLgDvoGXAHPQNuoGXAHfQMuIOegWBLS/YCXJWTk6MOHTpIkiZMmKDTTz9dt99+u0488UR5nqdvv/1W7777bpP3KSgo0OzZs5tc5nlek9ePOeYYPfXUU6qpqdFbb72l3/72t0pPT9fIkSOVkZGhDh06KDs7WzU1NYn9BIEUQcuAO+gZcAMtA+6gZ8Ad9Ay4gZYBd9Az4A56BtxAy4A76BlwBz0DwcbJ7y1g3LhxOuigg/SHP/yh8bK+ffv+6KkrPM9rvEHdnszMzMbrXHzxxXr11Vf1+uuva+TIkdbXDaApWgbcQc+AG2gZcAc9A+6gZ8ANtAy4g54Bd9Az4AZaBtxBz4A76BkInlCyF5AKOnTooJkzZ+qbb76xPjs7O1u1tbUxvU9NTY3Kysqa/AOwc35rWaJnIF5+65mWgfj4rWWJnoF4+a1nWgbiR8+AG/zWskTPQLz81jMtA/GjZ8ANfmtZomcgXn7rmZaB+NEzEDyc/N4Cfve732no0KEaNGiQSkpKdMYZZ+jvf//7j566YtOmTcrLy2vy79hjj93mTGOMxo8fr//+97864ogjYlrPqFGj1KpVq8Z/Xbt2jftzA1KJ31qW6BmIl996pmUgPn5rWaJnIF5+65mWgfjRM+AGv7Us0TMQL7/1TMtA/OgZcIPfWpboGYiX33qmZSB+9AwET1qyF5AKcnNz9eabb6q0tFQTJ07Up59+qiuvvFIPPvigPvnkE+Xk5EiS8vPzNWXKlCbvm52d3eT1N954Q3l5eaqrq1M0GtWZZ56pm266Kab1jBw5UldccUXj62VlZdxAAs3gt5Ylegbi5beeaRmIj99alugZiJffeqZlIH70DLjBby1L9AzEy2890zIQP3oG3OC3liV6BuLlt55pGYgfPQPBw8nvLahXr17q1auXzj//fF133XXq27evXnzxRf3qV7+SJIVCIfXu3XuHMw4//HA99thjysjIUKdOnZSWFvu3MDMzU5mZmXF9DgD807JEz8Cu8kvPtAzsGr+0LNEzsKv80jMtA7uOngE3+KVliZ6BXeWXnmkZ2HX0DLjBLy1L9AzsKr/0TMvArqNnIDg4+T1JSkpKlJOTo82bN8f0frm5uTu9AQXQcmgZcAc9A26gZcAd9Ay4g54BN9Ay4A56BtxBz4AbaBlwBz0D7qBnwN84+b0F3HTTTaqsrNRxxx2n7t27a+PGjXrooYdUV1eno48+uvF6xhitXLnyR+/frl07hUKhllwygG2gZcAd9Ay4gZYBd9Az4A56BtxAy4A76BlwBz0DbqBlwB30DLiDnoHg4eT3FnDYYYfpkUce0TnnnKNVq1apqKhIQ4YM0TvvvKN+/fo1Xq+srEwdO3b80fuvWLFCHTp0aMklA9gGWgbcQc+AG2gZcAc9A+6gZ8ANtAy4g54Bd9Az4AZaBtxBz4A76BkIHk5+bwGHH364Dj/88B1eZ8SIERoxYsQOrzNmzBh7iwIQM1oG3EHPgBtoGXAHPQPuoGfADbQMuIOeAXfQM+AGWgbcQc+AO+gZCB6eayFBHn30UeXl5enrr79usY/54YcfKi8vT88991yLfUzAdbQMuIOeATfQMuAOegbcQc+AG2gZcAc9A+6gZ8ANtAy4g54Bd9AzEGw88nsCPPfcc6qqqpIkdevWrcU+7j777KNp06ZJkvLy8lrs4wKuomXAHfQMuIGWAXfQM+AOegbcQMuAO+gZcAc9A26gZcAd9Ay4g56B4OPk9wTo3LlzUj5udna2evfuHfP7GWMkSfWmzup6jKm3Og+pqaw8am9WRcOsrT/zOxO0lqUf9Kw6qXmfJtBi6Ln5ErdttjsPljXz5zkWIWP/iZ5stixJ5TH0HLSWJbbN8De2zc2XqG1zIm77kXpsb5vpOU70bJFnf2RCvj/218m+dmzY14afsa/dfN9vm2utrieSgPvBPGP3dlqSjIlYn2n9ib8T8HknYtvsJWAm+9qxYdsMe/y9r+16y1Iie07A8V5QcL+BNfTcfGyb4Wfsa8eGnuFnydo2c/I7VF5eLkn6MPJ6klcC/FhRX/szy8vL1apVK/uDfWBrz5P1VpJXAvwYPTdf47a5/rXkLgTBV21/ZOfd7M+U3O+ZbTP8iG1z833f8pvcqQjfSUTLEj0jiYLyfUnAOtv0sz9TSoWe2deG/7Cv3XxbW36//KUkr6QZgvKYT0HZltqWgO8P+9qxYdsMaxJwO8a2OTYJ6zlVt1Gwip6bj20z/Ix97djQM/wsWdtmzzT3z13grGg0quXLlys/P1+et/2/tC0rK1PXrl21ZMkSFRQUWPnYzLQ3MwhrTPZMY4zKy8vVqVMnhUL2HwnXD+i5Zecxk54TpbktS3TCzOB/z+m5QbK/D8xs2XkuzqTl77n2vU3WPGYm7/tDz9+jk9SaGYQ1xjqTnhsk+/vg0swgrNHFmbT8vSB8b4OwRmayr50obJuZ2VLzkj3T9ZYlek7GzCCs0cWZrvfMvjYzgz6Tfe3vsW1mZkvNS/bMWFrmkd+hUCikLl26NPv6BQUF1n6omWl/ZhDWmMyZLv513w/Rc3LmMZOebYu1ZYlOmOm/ebHMpOfvBeFnJZVnBmGNyZxJy0259L1N5jxmJuf7Q89N0UlqzQzCGmOZSc/fc+17m8yZQVijazNpuakgfG+DsEZmsq9tG9tmZrb0vGTOdLlliZ6TOTMIa3Rtpss9s6/NTFdmsq/NtpmZLT8vmTOb27J7f+YCAAAAAAAAAAAAAAAAAAAAAHAOJ78DAAAAAAAAAAAAAAAAAAAAAHyPk9/RbJmZmbrxxhuVmZnJTB/ODMIagzTTdUH5PtAJM7FzdMJMv81L1EzXBeX7kKozg7DGIM10XVC+D3SSWjNpOT5B+D4wk+8Pdi4o34cgzAzCGlN9puuC8H0IwhqZ6f/vueuC8H1lpv87CcpM1wXl+xCEmUFYY6rPdF0Qvg9BWCMz/f89d10Qvq/M9H8nQZnpGWOMtWkAAAAAAAAAAAAAAAAAAAAAACQAj/wOAAAAAAAAAAAAAAAAAAAAAPA9Tn4HAAAAAAAAAAAAAAAAAAAAAPgeJ78DAAAAAAAAAAAAAAAAAAAAAHyPk98BAAAAAAAAAAAAAAAAAAAAAL7Hye8AAAAAAAAAAAAAAAAAAAAAAN/j5HeghTz99NN68803G1+/+uqrVVhYqAMPPFCLFi1K4soAxIKWAXfQM+AOegbcQMuAO+gZcAMtA+6gZ8Ad9Ay4gZYBd9Az4A56DhZOfgdayB133KHs7GxJ0ieffKJHHnlEd999t9q0aaPLL788yav73nnnnafy8vIfXb5582add955SVgR4C9BaVmiZ2BngtIzLQM7R8+AG4LSskTPwM4EpWdaBnYsKC1L9AzsDD0D7ghKz7QM7FhQWpboGdiZoPRMy8DO0XOweMYYk+xFwH/Kyspifp+CgoIErKR5HnrooW1e7nmesrKy1Lt3bx166KEKh8Mxzf3www/1+OOPq7S0VC+//LI6d+6sZ599Vj169NDBBx8c06ycnBzNmjVL3bp10zXXXKMVK1bomWee0cyZMzVs2DCtWbMmpnnbU1ZWpvfee0/9+vVT//79Y37/cDisFStWqF27dk0uX7t2rTp06KD6+nor64zHSSedFPP7jB49+kefSyoJWsuS/3sOSssSPbsmaD37vWUpOD37uWWJnuNBzw3o+Xt+6JmWYxe0liX/9xyUliV6dk3QevZ7y1JwevZzyxI9x4OeGwRt2yy53TMtxy5oLUv0vBX72j9Gz8Hq2e8tS8HpmZbdErSWJf/3HJSWJXp2DT1/L9V69nPLEj3HI2g9+71liZ5taMmW02J+D6SEwsJCeZ7X7Ot7nqc5c+aoZ8+ezbp+jx49djh//vz5zf7YknT//fdrzZo1qqysVFFRkSRpw4YNysnJUV5enlavXq2ePXtq4sSJ6tq1a7NmvvLKK/rlL3+ps846S1OnTlVNTY0kadOmTbrjjjv01ltvxbTGvLw8rVu3Tt26ddM777yjK664QpKUlZWlqqqqmGb90GmnnaZDDz1Ul156qaqqqrTPPvto4cKFMsbohRde0Mknn9ysOWVlZTLGyBij8vJyZWVlNb4tEonorbfeiutGZuvn+b88z9O9996ru+66S6tXr9a9996701mvvfaaTjvttMa/sNqZ559/XhUVFSm9oxO0liX/9+z3liV6dlXQevZ7y5L/ew5CyxI9xyORPafitlmi5//FtrllBG3bLPm/Z7+3LNGzq4LWs99blvzfcxBalug5HuxrB2PbLKVWz7Qcu6Btm6XU7Zl97R2j5+D17PeWJf/3TMtuClrLkv979nvLEj27ip4bpFLPiWpZoudkC1rPfm9ZSt2eg9oyJ79ju15++WUVFxfv9HrGGB133HExzf7973/f5PW6ujpNnTpV//nPf/SHP/whpllSw1NOPPHEE3ryySfVq1cvSdK8efN04YUX6je/+Y0OOuggnXHGGbr88sv18ssvN2vmbbfdptGjR+ucc87RCy+80Hj5QQcdpNtuuy3mNR599NE6//zzNWTIEM2ZM6fxazZz5kyVlJTEPG+rDz74QNddd50k6dVXX5UxRhs3btTTTz+t2267rdk3jls3iJ7nqW/fvj96u+d5uvnmm2Ne39SpU7d5+daN4zvvvKMFCxY0+5d+Dz30ULNv7Jr7vXZdkFqW/N+z31uW6NllQerZ7y1L/u85KC1L9ByPRPWcittmiZ63NU9i29wSgrRtlvzfs99blujZZUHq2e8tS/7vOSgtS/QcD/a1/b9tllKvZ1qOXZC2zVLq9sy+9o7Rc4Mg9ez3liX/90zL7gpSy5L/e/Z7yxI9u4yeU6vnRLUs0bMfBKlnv7cspW7PgW3ZANtQUlJi1q5d2+zrDxgwwCxevHiXP+7DDz9sRowYEfP79ezZ00ydOvVHl0+ZMsX06NHDGGPMRx99ZDp06NDsmdnZ2WbBggXGGGPy8vJMaWmpMcaY0tJSk5mZGfMaN2zYYC699FJz4oknmrfffrvx8htuuMHcdtttMc/bKisrq/Fr/8tf/tJcc801xhhjFi1aZHJzc5s9Z9KkSWbixInG8zwzduxYM2nSpMZ/H3/8sVm2bFnca7Rl0qRJpq6urtnX//DDD011dXUCV+R/QWvZGP/37PeWjaFnVwWtZ7+3bIz/ew5Cy8bQczyS0bPL22Zj6NkGWo5d0LbNxvi/Z7+3bAw9uypoPfu9ZWP833MQWjaGnuPBvnYwts3GpFbPtBy7oG2bjUndntnX3jF6Dl7Pfm/ZGP/3TMtuClrLxvi/Z7+3bAw9u4qeG6RSz0Fo2Rh6jkfQevZ7y8bQsw0t2TInv8NXSktLTX5+fszvl52dbb744osfXf7555+b7OxsY4wxCxYsiOnGokePHubdd981xjS9cXz66adN//79Y1pfXV2dufnmm82SJUtier/m6NOnj3nxxRdNRUWFadu2rZkwYYIxxphp06aZ1q1bxzxv4cKFJhKJ2F4mUky8LRvj756D1LIx9Aw72DbHjm0z/MjVbbMx9IzU42rPQWrZGHqGHexrx45tM/yIbXN86Bl+RM+xY18bfsW+duzYNsOP2DbHjm0z/IqeY8e2GX7Fvnbs6DkxQvE/ZjxgX3OfiuN/HX744brwwgubPAXD1KlTdfHFF+uII46QJH399dfq0aNHs2decMEFuuyyy/TZZ5/J8zwtX75czz33nK666ipdfPHFMa0vLS1Nd999t+rr62N6v+b4/e9/r7POOktdunRRx44dNWzYMEkNT5cxaNCgmOd1795doVBIlZWVmjVrlmbMmNHkX7JFo1HdddddOuiggzR06FD98Y9/VFVVVbKXhf8Rb8uSv3sOUssSPcMOts2xS7Vts0TPQeDqtlmiZ5toORhc7TlILUv0DDvY145dqm2bJXoOArbN8Um1nmk5GOg5duxr07Nfsa8dO7bNtOxHbJtjx7aZnv2KnmOXattmiZ6Dgn3t2KVazy3WcrLPvod/ffzxx2bcuHFNLnv66adNSUmJadu2rbngggvifsqBwYMHmyFDhjT+Gzx4sOnQoYMJh8Pm8ccfj3neihUrzFFHHWU8zzMZGRkmIyPDhEIhc/TRR5uVK1caY4x57733zH//+99mz4xGo+a2224zubm5xvM843meycrKMn/6059iXp8xxpx44olmzJgxcb3vznz55Zdm7NixpqKiovGyN954w3z00Ucxz1q9erU5/vjjTSgU2ua/ZLvllltMKBQyP/nJT8zw4cNNVlaW+dWvfpXsZflakFo2xv89B6VlY+jZRUHq2e8tGxOcnv3esjH0HI9E9ZyK22Zj6NkWWo5dkLbNxvi/56C0bAw9uyhIPfu9ZWOC07PfWzaGnuPBvnYwts3GpFbPtBy7IG2bjUntntnXpuedCVLPfm/ZmOD0TMvuCVLLxvi/56C0bAw9u4ieU7Nnv7dsDD3HI0g9+71lY+jZlpZq2TPGGPun1MMFxx57rIYNG6ZrrrlGUsNf1uy1114aMWKE+vfvrz//+c+68MILddNNN8U8+6abbpLneY2vh0IhtW3bVsOGDdNuu+0W95pnzZqlOXPmSJL69eunfv36xT1rq9raWs2bN08VFRXafffdlZeXF9ec0aNH6+abb9ZZZ52lvffeW7m5uU3efuKJJzZ71hVXXKFbb71Vubm5uuKKK3Z43fvuuy+mdZ511llatGiRHnjgAQ0bNkyvvvqqVq1apdtuu0333nuvjj/++Jjm2danTx9dddVVuvDCCyVJ48eP1/HHH6+qqiqFQjyZxbYEsWXJvz0HpWWJnl0UxJ792rIUnJ793rJEz/FIVM+puG2W6NkWWo5dELfNkn97DkrLEj27KIg9+7VlKTg9+71liZ7jwb52A79tm6XU7pmWYxfEbbOUGj2zr03PsQpiz35tWQpOz7TsniC2LPm356C0LNGzi+j5e6nUs99blug5HkHs2a8tS/RsS0u1zMnv2K6OHTtq3Lhx2meffSRJ1113nd5//31NnjxZkvSvf/1LN954o7799ttmzywrK2vW9QoKCmJfsGWbNm1SJBL50dN0rF+/XmlpaTGvcUfhep6nSCTS7FmHH364Xn31VRUWFurwww/f4dz33nsvpnV27NhR//73v7XvvvuqoKBAX375pfr27avXX39dd999d+P3P1kyMzM1b948de3atfGyrKwszZs3T126dEniyvwr1VuW7PYclJYlenZRqvfMttmfLUv0HA/bPQepZYme/dozLccu1bfNEvva9OyOVO+ZbbM/W5boOR7sa/tz2yylds+0HLtU3zZL/u2ZfW16jlWq98y+Ni27ItVbltg207M76Dk1e/Z7yxI9xyPVe2Zf2589t1TLadYmwTkbNmxQ+/btG19///33deyxxza+PnToUC1ZsiSmmYWFhU3+Iuh/GWPiutM8EolozJgxmjBhglavXq1oNNrk7fHswJ9xxhk64YQTdMkllzS5/KWXXtLrr7+ut956K6Z5/7umXTFx4sRtvmzD5s2b1a5dO0lSUVGR1qxZo759+2rQoEGaMmWK1Y8Vj/r6emVlZTW5LD09XXV1dUlakf8FqWXJ/z0HpWWJnl0UpJ793rIUnJ793rJEz/Gw3XMqb5sleraFlmMXpG2z5P+eg9KyRM8uClLPfm9ZCk7Pfm9Zoud4sK/tz22zlNo903LsgrRtllKrZ/a16TlWQerZ7y1LwemZlt0TpJYl//cclJYlenYRPadmz35vWaLneASpZ7+3LNGzLS3VMie/Y7vat2+vBQsWqGvXrqqtrdWUKVN08803N769vLxc6enpMc38YcjGGB133HF68skn1blz511a62WXXaYxY8bo+OOP18CBA3d4A9xcn3322TafUmLYsGG67rrrdml2dXX1jwL3i379+mn27NkqKSnRnnvuqccff1wlJSUaPXq0OnbsmOzlyRijESNGKDMzs/Gy6upqXXTRRU2eamTs2LHJWJ4vBallKVg9+7lliZ5dFKSeg9Sy5O+e/d6yRM/xsN0z2+bv0XP8aDl2Qdo2S8Hq2c8tS/TsoiD1HKSWJX/37PeWJXqOB/vabJv92DMtxy5I22aJnm2iZ/cEqecgtSz5u2dadk+QWpaC1bOfW5bo2UX0nJo9+71liZ7jEaSeg9SyRM+7oqVa5uR3bNdxxx2nP/7xj7rrrrv02muvKScnR4ccckjj22fMmKFevXrFNPOwww5r8no4HNb++++vnj177tJaX3jhBb300ks67rjjdmnOD9XU1Ki+vv5Hl9fV1amqqirmeZFIRHfccYdGjx6tVatWac6cOerZs6euv/56lZSU6Ne//rWNZe+yyy67TCtWrJAk3XjjjTrmmGP03HPPKSMjQ2PGjEnu4iSde+65P7rs7LPPTsJKgiNILUv+7zkoLUv07KIg9ez3lqXg9Oz3liV6joftnlN52yzRsy20HLsgbZsl//cclJYlenZRkHr2e8tScHr2e8sSPceDfW22zX7smZZjF6Rts0TPNtGze4LUs99bloLTMy27J0gtS/7vOSgtS/TsInpOzZ793rJEz/EIUs9+b1miZ1tarGUDbMeaNWvMIYccYjzPM/n5+Wbs2LFN3n7EEUeYa6+9dpc+Rl5eniktLd2lGcYY07FjRzN79uxdnvNDw4YNM5deeumPLr/kkkvMwQcfHPO8m2++2fTs2dP84x//MNnZ2Y2f9wsvvGD233//XV5vomzevNl89dVXZs2aNcleCuIUpJaN8X/PQW3ZGHp2QZB69nvLxgS3Z1p2Q6J7TqVtszH0jOQJ0rbZGP/3HNSWjaFnFwSpZ7+3bExwe6ZlN7CvzbbZGHp2QZC2zcbQcyLRc/AFqWe/t2xMcHum5eALUsvG+L/noLZsDD27gJ7p2RhadkWQevZ7y8bQc9Bw8jt2auPGjaa+vv5Hl69bt87U1tbu0mxbN4733HOPueSSS0w0Gt3lWVtNnjzZZGVlmUMOOcTcdNNN5qabbjKHHHKIycrKMh988EHM83r16mXGjx9vjGn6eX/33XemsLDQ2rptqampMbNmzTJ1dXXJXkrMVq1alewl+FIQWjbG/z0HrWVj6NlFQejZ7y0bE7yeg9yyMfS8PYnqOZW2zcbQc0ui5W0LwrbZGP/3HLSWjaFnFwWhZ7+3bEzweg5yy8bQ8/awr822OWhoeduCsG02hp4TgZ7dE4Se/d6yMcHrmZbdE4SWjfF/z0Fr2Rh6dhE9p2bPQW7ZGHreniD07PeWjaHnlmSj5ZD9x5KHa1q1aqVwOPyjy1evXq0BAwbs8nzP83Z5xuTJk/Xcc8+pV69eOuGEE3TSSSc1+RePgw46SJ988om6du2ql156SePGjVPv3r01Y8aMJk8P0lzLli1T7969f3R5NBpVXV1dXGtMhMrKSv36179WTk6OBgwYoMWLF0uSfve73+nOO+9M8uqknJwcrVmzpvH1448/vvFpPCRp1apV6tixYzKW5ntBaFnyf89BaVmiZ5cFoWe/tywFp2e/tyzR865IZM+psm2W6NkWWo5fELbNkv97DkrLEj27LAg9+71lKTg9+71liZ53BfvabJsl//RMy/ELwrZZomeb6NldQejZ7y1LwemZlt0VhJYl//cclJYlenYZPadWz35vWaLnXRGEnv3eskTPtrRUy2m7PAEpq6amRqWlpTG9z//eUFVXV+uiiy5Sbm5uk8vHjh0b09zCwkL9/Oc/j+l9mmPw4MF67rnnrMzafffd9eGHH6p79+5NLn/55Zc1ZMgQKx/DhpEjR2r69OmaNGmSjjnmmMbLjzrqKN1000364x//mMTVNfzMGGMaX//ggw9UVVXV5Do/fDt2zk8tS/7vOSgtS/ScivzUs99bloLTs99blug5EWLtOZW3zRI920LL9vlp2yz5v+egtCzRcyryU89+b1kKTs9+b1mi50RgX7v5gtKy5P+eadk+P22bJXq2iZ5Tj5969nvLUnB6puXU46eWJf/3HJSWJXpORfQcm6D07PeWJXpOBD/17PeWJXq2paVa5uR3tKhWrVo1ef3ss8+2Mvepp56yMmd7qqurVVtb2+SygoKCmGbccMMNOvfcc7Vs2TJFo1GNHTtWs2fP1jPPPKM33njD5nJ3yWuvvaYXX3xR+++/f5O/2howYEDMG8NksfXXo9i+RLUs+b/noLQs0TOah22z/3t2oWWJnhMtlbfNEj23JFpOvFTuOSgtS/SM5mFf2/89u9CyRM+JxrbZ/y1LbvRMy4lHz/TcUug58djX9n/PtIzmYNvs/5Ylekbz0LP/e3ahZYmeWwL72vTcEmy0zMnvaFGJvhGzqbKyUldffbVeeuklrVu37kdvj0QiMc0bPny4xo0bp1tuuUW5ubm64YYbtNdee2ncuHE6+uijbS17l61Zs0bt2rX70eWbN29mBwKNgtSyZLfnoLQs0TOaJ0g9s21uipbxQ0FqWaLn/0XP+KFU7jkoLUv0jOYJUs9sm5uiZfxQkFqW2Db/L3rGD9EzPcMdQeqZfe2maBk/FKSWJbbN/4ue8UP07P+eaRnNFaSe2dduKtV65uR3BNZee+2lCRMmqKioSEOGDNlhuFOmTIl5/h/+8AdNnDhRjz32mH75y1/qkUce0bJly/T444/rzjvvjGvNhxxyiN5999243rel7LPPPnrzzTf1u9/9TtL3f2Xz5JNP6oADDkjm0iQ1rOeH3+v/fR3BFLSeg9CyRM9oeUFrWQpGz35vWaJnF9FzYvi9Z1p2U9B6DkLLEj2j5QWtZSkYPfu9ZYmeXRS0noPQsuT/nmnZTfScGPSMlha0lqVg9EzLSIag9RyEliV6RnLQs31+b1miZxcFrWWJnm1oqZY5+R3bVVRUtMMfuvr6+hZczY8NHz5cmZmZkqSf/exn1uePGzdOzzzzjIYNG6Zf/epXOuSQQ9S7d291795dzz33nM4666y4Z1dUVCgajTa5LNan2UiUO+64Q8cee6y+/fZb1dfX68EHH9S3336rjz/+WO+//36ylydjjPr27dv4s1lRUaEhQ4YoFAo1vh1N+b1lKbg9+7lliZ5d5Peeg9qy5O+e/d6yRM/xoGd69mPPtBw7v7csBbdnP7cs0bOL/N5zUFuW/N2z31uW6Dke9My22Y8903Ls/N6yRM+JQs/u8XvPQW1Z8nfPtOwev7csBbdnP7cs0bOL6Dk1e/Z7yxI9x8PvPQe1ZYmed0VLtewZbhWwHU8//XSzrnfuuecmeCXJkZeXp2+//VbdunVTly5dNHbsWO27775asGCBBg0apIqKipjmLViwQJdeeqkmTZqk6urqxsuNMfI8L+an2Uik+fPna9SoUZo+fboqKiq011576ZprrtGgQYOSvbSU/7mMB18zuz0HqWWJnl2T6l8zts3+bFniZzMeqf41o2d/9pzqP5fx4GvGvjY9uyPVv2Zsm/3ZssTPZjxS/WvGttmfPaf6z2U8+JrRMz27I9W/Zuxr07Ir+JqxbaZnd/A1S92e/dyyxM9mPFL9a8a+tj97bqmfSx75Hdvl6o1ec/Xs2VMLFixQt27dtNtuu+mll17Svvvuq3HjxqmwsDDmeWeffbaMMfr73/+u9u3b+/JpWerq6nThhRfq+uuv11//+tdkL2ebUv3nMh58zez2HISWJXp2Vap/zdg2+7NliZ/NeKT614ye/dlzqv9cxoOvGfva9OyOVP+asW32Z8sSP5vxSPWvGdtmf/ac6j+X8eBrRs/07I5U/5qxr03LruBrxraZnt3B1yz1eg5CyxI/m/FI9a8Z+9r+7LnFfi4NsB3r1683Dz30kNm0adOP3rZx48btvi0ZCgsLTVFR0Y/+FRcXm06dOplDDz3U/P3vf2/WrNLSUhOJRMx9991nHnzwQWOMMe+++67JysoymZmZJhQKmQceeCDmNebm5ppZs2bF/H4traCgwMyfPz/Zy4hJVVWVGTNmjHnkkUfMnDlzkr0c3wlSy8b4v+egtGwMPbsoSD37vWVjgtNzEFs2hp53hp7pOShoeceC1LIx/u85KC0bQ88uClLPfm/ZmOD0HMSWjaHnnaFnts1BQcs7FqSWjaFnm+jZPUHq2e8tGxOcnmnZPUFq2Rj/9xyUlo2hZxfRc2r2HMSWjaHnnQlSz35v2Rh6TqREtMzJ79iuW265xZxyyinbffupp55qbrvtthZc0fbdd999pnXr1ubss882Dz30kHnooYfM2Wefbdq0aWNuv/12c/7555vMzEzzxBNP7HRWKBQyq1atanz9tNNOMytXrjQLFy40r7zyipk+fXpcaxw2bJh5991343rflnTOOeeY++67L9nL2K7LL7/cXHrppY2v19TUmMGDB5v09HTTqlUrk5ubaz7++OMkrtB/gtSyMf7vOSgtG0PPLgpSz35v2Zjg9Oz3lo2h53jQcwN69hdajl2QWjbG/z0HpWVj6NlFQerZ7y0bE5ye/d6yMfQcD3puwLbZX2g5dkFq2Rh6tome3ROknv3esjHB6ZmW3ROklo3xf89BadkYenYRPadmz35v2Rh6jkeQevZ7y8bQsy0t1TInv2O79txzTzN+/Pjtvn38+PFm8ODBLbii7TvppJPMY4899qPLR48ebU466SRjjDEPPfSQGThw4E5neZ7X5MYxLy/PlJaW7vIa582bZ4466igzZswY8+WXX5rp06c3+ecXt956qyksLDQnn3yyueOOO8yDDz7Y5F+yDRgwwPz73/9ufP3vf/+7KSoqMgsXLjTRaNSMGDHCHHfccUlcof8EqWVj/N9zUFo2hp5dFKSe/d6yMcHp2e8tG0PP8aDnBvTsr55pOXZBatkY//cclJaNoWcXBalnv7dsTHB69nvLxtBzPOi5Adtmf/VMy7ELUsvG0LNN9OyeIPXs95aNCU7PtOyeILVsjP97DkrLxtCzi+g5NXv2e8vG0HM8gtSz31s2hp5taamWOfkd25WXl2cWLVq03bcvWrTI5Ofnt+CKti83N9fMnTv3R5fPnTvX5ObmGmMabpxycnJ2OitRN46ffPKJ6dGjh/E8r/FfKBRq/N8vSkpKtvuvR48eyV6eyc/Pb/K9PuOMM8wFF1zQ+PrUqVNNx44dk7E03wpSy8b4v+egtGwMPbsoSD37vWVjgtOz31s2hp7jQc8N6NlfPdNy7ILUsjH+7zkoLRtDzy4KUs9+b9mY4PTs95aNoed40HMDts3+6pmWYxeklo2hZ5vo2T1B6tnvLRsTnJ5p2T1BatkY//cclJaNoWcX0XNq9uz3lo2h53gEqWe/t2wMPdvSUi2nCdiOcDis5cuXq1u3btt8+/LlyxUKhVp4VdtWXFyscePG6fLLL29y+bhx41RcXCxJ2rx5s/Lz83c6y/M8eZ73o8t21XnnnachQ4bon//8p9q3b29lZiIsWLAg2UvYoVAoJGNM4+uffvqprr/++sbXCwsLtWHDhmQszbeC1LLk/56D0rJEzy4KUs9+b1kKTs9+b1mi53jQc9PLdhU920HLsQtSy5L/ew5KyxI9uyhIPfu9ZSk4Pfu9ZYme40HPTS/bFUFpWfJ/z7QcuyC1LNGzTfTsniD17PeWpeD0TMvuCVLLkv97DkrLEj27iJ5Ts2e/tyzRczyC1LPfW5bo2ZaWapmT37FdQ4YM0Wuvvab9999/m29/9dVXNWTIkBZe1bZdf/31uvjiizVx4kTtu+++kqQvvvhCb731lkaPHi1Jevfdd3XYYYftdJYxRiNGjFBmZqYkqbq6WhdddJFyc3ObXG/s2LExrXHRokV6/fXX1bt375jer6VdccUV27zc8zxlZWWpd+/eGj58eONGp6X1799f48aN0xVXXKGZM2dq8eLFOvzwwxvfvmjRIrVv3z4pa/OrILUs+b/noLQs0bOLgtSz31uWgtOz31uW6Dke9EzPP+SXnmk5dkFqWfJ/z0FpWaJnFwWpZ7+3LAWnZ7+3LNFzPOiZbfMP+aVnWo5dkFqW6NkmenZPkHr2e8tScHqmZfcEqWXJ/z0HpWWJnl1Ez6nZs99blug5HkHq2e8tS/RsS0u17JkfnmIP/MArr7yiM844Q/fff78uvvhihcNhSVIkEtGjjz6qK6+8Us8//7xOOeWUJK+0wUcffaSHH35Ys2fPliT169dPv/vd73TggQfGNOdXv/pVs6731FNPxTT3hBNO0IgRI3TyySfH9H4t7fDDD9eUKVMUiUTUr18/SdKcOXMUDoe12267afbs2fI8T5MnT9buu+/e4ut79dVXdcYZZ+jggw/WzJkzNXToUI0bN67x7ddcc40WLFigl156qcXX5ldBa1nyd89BaVmiZxcFrWc/tywFp2e/tyzRczzoecfomW1zUAStZcnfPQelZYmeXRS0nv3cshScnv3eskTP8aDnHWPbzLY5KILWskTPttCze4LWs59bloLTMy27J2gtS/7uOSgtS/TsInreORd79nvLEj3HI2g9+7lliZ5taamWOfkdO3Tddddp1KhRys/PV8+ePSVJ8+fPV0VFhf7whz/ozjvvTPIKg+OJJ57QbbfdpvPOO0+DBg1Senp6k7efeOKJSVpZUw888IA+/PBDPfXUUyooKJAkbdq0Seeff74OPvhgXXDBBTrzzDNVVVWl//73v0lZ44QJE/TGG2+oQ4cO+t3vfqecnJzGt91888067LDDNGzYsKSsza9o2Z6gtCzRs6vo2Z6g9ByEliV6jgc920PP9tBy7GjZnqC0LNGzq+jZnqD0HISWJXqOBz3bEZSWpWD0TMuxo2V76Nkueo4dPdsTlJ5p2U20bE9QWpbo2VX0bE9Qeg5CyxI9x4Oe7aFne1qiZU5+x059/vnneu655zRv3jwZY9S3b1+deeaZjU8/4QdlZWXbvNzzPGVmZiojI6OFV/RjoVBou2/zPE+RSKQFV7N9nTt31rvvvvujv/qZOXOmfvKTn2jZsmWaMmWKfvKTn2jt2rUturYZM2Zo4MCBO/xa/tDMmTPVr18/paWlJXhlwRCEliX/9xyUliV6dlkQevZ7y1JwevZzyxI97yp6toOedx0t75ogtCz5v+egtCzRs8uC0LPfW5aC07OfW5boeVfR864LSsuSv3um5V0ThJYleraJnt0VhJ793rIUnJ5p2V1BaFnyf89BaVmiZ5fRsx1B6dnPLUv0vKuC0LPfW5bo2YYWbdkA2zB9+nQTiUSaff1vvvnG1NXVJXBFO+Z5ngmFQtv9161bN3PDDTfE9DmlqtzcXDNx4sQfXT5x4kSTl5dnjDGmtLTU5Ofnt/DKjAmFQmb16tXNvn5+fr4pLS1N4Ir8L2gtG0PPNtGzW4LWMy3b4+eWjaHneNBz6vJzz7Qcu6C1bAw920TPbglaz7Rsj59bNoae40HPqcvPPdNy7ILWsjH0bBM9uyVoPdOyPbTslqC1bAw920TPbqHn1OXnlo2h53gErWdatsfPPbdky/zpC7ZpyJAhWrlypdq2bdus6x9wwAGaNm1a41NntLQxY8bouuuu04gRIxr/Yunzzz/X008/rT/96U9as2aN7rnnHmVmZuraa69t8fXV1dUpOztb06ZN08CBA1v848di+PDhOu+883Tvvfdq6NChkqQvvvhCV111lX72s59Javja9u3bt8XXZozR9ddf3+RpMHaktrY2wSvyv6C1LPm75yC1LNGza4LWs59bloLVs59blug5HvRsFz3bQcuxC1rLkr97DlLLEj27Jmg9+7llKVg9+7lliZ7jQc/2BKllyd8903LsgtayRM820bNbgtazn1uWgtUzLbslaC1L/u45SC1L9OwaerYrSD37uWWJnuMRtJ793LJEz7a0ZMuc/I5tCtoG5emnn9a9996r0047rfGyE044QYMGDdLjjz+uCRMmqFu3brr99tuTcuOYnp6ubt26+eapL3bk8ccf1+WXX64zzjhD9fX1kqS0tDSde+65uv/++yVJu+22m5588skWX9uhhx6q2bNnN/v6BxxwgLKzsxO4Iv8LWsuSv3sOUssSPbsmaD37uWUpWD37uWWJnuNBz3bRsx20HLugtSz5u+cgtSzRs2uC1rOfW5aC1bOfW5boOR70bE+QWpb83TMtxy5oLUv0bBM9uyVoPfu5ZSlYPdOyW4LWsuTvnoPUskTPrqFnu4LUs59blug5HkHr2c8tS/RsS0u27BljTFzvCacNGzZMnufF9D7PP/+8OnbsmKAV7Vh2drZmzJihPn36NLl87ty52nPPPVVZWakFCxZowIABqqysTMoa//a3v2ns2LF69tlnVVxcnJQ1xKKiokLz58+XJPXs2VN5eXlJXhHiEbSWJf/3HLSWJXp2RdB69nvLUvB6pmV30LN99IxkCFrLkv97DlrLEj27Img9+71lKXg907I76NmuoLUs0bMrgtayRM+JQM9uCFrPfm9ZCl7PtOyGoLUs+b/noLUs0bMr6Nm+oPVMy+4IWs9+b1mi56Dhkd+xTZMmTUr2EmLStWtX/e1vf9Odd97Z5PK//e1v6tq1qyRp3bp1KioqSsbyJEkPP/yw5s2bp06dOql79+7Kzc1t8vYpU6YkaWXblpeXpz322CPZy8AuClrLkv97DlrLEj27Img9+71lKXg907I76Nk+ekYyBK1lyf89B61liZ5dEbSe/d6yFLyeadkd9GxX0FqW6NkVQWtZoudEoGc3BK1nv7csBa9nWnZD0FqW/N9z0FqW6NkV9Gxf0HqmZXcErWe/tyzRc9Bw8juccM899+jUU0/V22+/raFDh0qSvvzyS82aNUsvv/yyJOmLL77Q6aefnrQ1/uxnP0vaxwaCxO890zLQPH5vWaJnoLnoGXCH33umZaB5/N6yRM9Ac/m9Z1oGmo+eATf4vWWJnoHm8nvPtAw0Hz0DbvB7yxI9B41njDHJXgRgw4IFC/TEE09o9uzZkqR+/frpwgsvVElJSXIXBiBm9Ay4gZYBd9Az4A56BtxAy4A76BlwBz0DbqBlwB30DLiDngE30DJs4uR3oIV99dVX+u677yRJAwYM0JAhQ5K8IgDxoGXAHfQMuIOeATfQMuAOegbcQMuAO+gZcAc9A26gZcAd9Ay4g56DIS3ZCwBseeGFF3TiiScqJycn2UvZptWrV+uMM87QpEmTVFhYKEnauHGjDj/8cL3wwgtq27ZtchcI+Iife6ZloPn83LJEz0As6Blwh597pmWg+fzcskTPQCz83DMtA7GhZ8ANfm5ZomcgFn7umZaB2NAz4AY/tyzRc9CEkr0AwJZLLrlEa9eu/dHLfvG73/1O5eXlmjlzptavX6/169frm2++UVlZmf7v//4v2csDfMXPPdMy0Hx+blmiZyAW9Ay4w8890zLQfH5uWaJnIBZ+7pmWgdjQM+AGP7cs0TMQCz/3TMtAbOgZcIOfW5boOWg8Y4xJ9iKAePXq1UsHH3ywDjroIF111VWaOXOmunbtqoKCAk2bNk09e/ZM9hIbtWrVSuPHj9fQoUObXP7555/rJz/5iTZu3JichQE+EZSeaRnYsaC0LNEzsDP0DLgjKD3TMrBjQWlZomdgZ4LSMy0DO0fPgBuC0rJEz8DOBKVnWgZ2jp4BNwSlZYmegyYt2QsAdsWzzz6ryZMn64033lBlZaUOOOAAHXfccaqrq9OGDRuSvbwmotGo0tPTf3R5enq6otFoElYE+EtQeqZlYMeC0rJEz8DO0DPgjqD0TMvAjgWlZYmegZ0JSs+0DOwcPQNuCErLEj0DOxOUnmkZ2Dl6BtwQlJYleg4aHvkdzigqKtIzzzyjGTNm6Oabb5bneerZs6cOPfRQPf7448lenoYPH66NGzfqn//8pzp16iRJWrZsmc466ywVFRXp1VdfTfIKAf/wc8+0DDSfn1uW6BmIBT0D7vBzz7QMNJ+fW5boGYiFn3umZSA29Ay4wc8tS/QMxMLPPdMyEBt6Btzg55Yleg4aHvkdgXbQQQfpkEMO0YEHHqhoNKrBgwfrhBNO0J133qnPPvtMa9eu1QcffJDsZUqSHn74YZ144okqKSlR165dJUlLlizRwIED9Y9//CPJqwOSLyg90zKwY0FpWaJnYGfoGXBHUHqmZWDHgtKyRM/AzgSlZ1oGdo6eATcEpWWJnoGdCUrPtAzsHD0DbghKyxI9Bw2P/I5Ae/nll/XJJ5/o448/1ueff64BAwZo+PDhuvfeezV9+nT16dMn2Utswhij8ePHa9asWZKk/v3766ijjkryqgB/CFLPtAxsX5BalugZ2BF6BtwRpJ5pGdi+ILUs0TOwI0HqmZaBHaNnwA1BalmiZ2BHgtQzLQM7Rs+AG4LUskTPQcLJ73BGUVGR7rnnHk2ZMkVPPPGE0tPTtd9+++nQQw/VzTffnJQ1FRcXa86cOWrTpo3OO+88Pfjgg8rPz0/KWoAg8VvPtAzEx28tS/QMxIueAXf4rWdaBuLjt5Ylegbi5beeaRmIHz0DbvBbyxI9A/HyW8+0DMSPngE3+K1liZ6DLJTsBQA2HX300XrkkUeUlZWld999VxdeeKFWr16dtPXU1taqrKxMkvT000+ruro6aWsBgsZPPdMyED8/tSzRM7Ar6Blwh596pmUgfn5qWaJnYFf4qWdaBnYNPQNu8FPLEj0Du8JPPdMysGvoGXCDn1qW6DnI0pK9AMCWRx99VG3atGl8vWPHjjrooIN0xhlnJG1NBxxwgH72s59p7733ljFG//d//6fs7OxtXvfvf/97C68O8C+/9UzLQHz81rJEz0C86Blwh996pmUgPn5rWaJnIF5+65mWgfjRM+AGv7Us0TMQL7/1TMtA/OgZcIPfWpboOcg4+R3O+MUvftH48rfffqtOnTolcTUN/vGPf+j+++9XaWmpPM/Tpk2b+OsgoBn81jMtA/HxW8sSPQPxomfAHX7rmZaB+PitZYmegXj5rWdaBuJHz4Ab/NayRM9AvPzWMy0D8aNnwA1+a1mi5yDzjDEm2YsAbLj00kt1yy23qLi4ONlL2aYePXroyy+/VOvWrZO9FMD3/NwzLQPN5+eWJXoGYkHPgDv83DMtA83n55YlegZi4eeeaRmIDT0DbvBzyxI9A7Hwc8+0DMSGngE3+LlliZ6DJpTsBQC7YunSpY0vP//886qoqJAkDRo0SEuWLEnWsrZpwYIFP7ph3LhxY3IWA/hQUHqmZWDHgtKyRM/AztAz4I6g9EzLwI4FpWWJnoGdCUrPtAzsHD0DbghKyxI9AzsTlJ5pGdg5egbcEJSWJXoOGk5+R6Dttttu6t69u84880xVV1c33iAuXLhQdXV1SV5dU3fddZdefPHFxtdPO+00FRcXq3Pnzpo+fXoSVwb4Q1B6pmVgx4LSskTPwM7QM+COoPRMy8COBaVliZ6BnQlKz7QM7Bw9A24ISssSPQM7E5SeaRnYOXoG3BCUliV6DhpOfkegbdy4Uf/617+09957KxqN6rjjjlPfvn1VU1Oj//73v1q1alWyl9ho9OjR6tq1qyTp3Xff1bvvvqv//Oc/OvbYY/WHP/whyasDki8oPdMysGNBaVmiZ2Bn6BlwR1B6pmVgx4LSskTPwM4EpWdaBnaOngE3BKVliZ6BnQlKz7QM7Bw9A24ISssSPQeNZ4wxyV4EEK+qqiplZ2dLkoqKivTVV19pxYoVOuqoozRw4EDNnDlTXbt21ezZs5O8Uik7O1tz5sxR165dddlll6m6ulqPP/645syZo/32208bNmxI9hKBpApKz7QM7FhQWpboGdgZegbcEZSeaRnYsaC0LNEzsDNB6ZmWgZ2jZ8ANQWlZomdgZ4LSMy0DO0fPgBuC0rJEz0GTluwFALuisLBQgwcP1kEHHaTa2lpVVVXpoIMOUlpaml588UV17txZX3zxRbKXKanhxnvJkiXq2rWr/vOf/+i2226TJBljFIlEkrw6IPmC0jMtAzsWlJYlegZ2hp4BdwSlZ1oGdiwoLUv0DOxMUHqmZWDn6BlwQ1BalugZ2Jmg9EzLwM7RM+CGoLQs0XPQcPI7Am3ZsmX65JNP9PHHH6u+vl577723hg4dqtraWk2ZMkVdunTRwQcfnOxlSpJOOukknXnmmerTp4/WrVunY489VpI0depU9e7dO8mrA5IvKD3TMrBjQWlZomdgZ+gZcEdQeqZlYMeC0rJEz8DOBKVnWgZ2jp4BNwSlZYmegZ0JSs+0DOwcPQNuCErLEj0HjWeMMcleBGBDUVGRPvjgA3333Xc655xz1KFDB61atUr77ruv3n///WQvT3V1dXrwwQe1ZMkSjRgxQkOGDJEk3X///crPz9f555+f5BUC/uHnnmkZaD4/tyzRMxALegbc4eeeaRloPj+3LNEzEAs/90zLQGzoGXCDn1uW6BmIhZ97pmUgNvQMuMHPLUv0HDSc/A5nFBUVafr06erWrZvy8/M1ffp05eTk6P3339fpp5+e7OUBiAE9A26gZcAd9Ay4g54BN9Ay4A56BtxBz4AbaBlwBz0D7qBnwA20DJs4+R3OWLJkiTp37qxQKKSBAwfq7bffVteuXZO9rCbmzp2riRMnavXq1YpGo03edsMNNyRpVYD/+L1nWgaax+8tS/QMNBc9A+7we8+0DDSP31uW6BloLr/3TMtA89Ez4Aa/tyzRM9Bcfu+ZloHmo2fADX5vWaLnIOHkd6CF/PWvf9XFF1+sNm3aqEOHDvI8r/FtnudpypQpSVwdgOaiZcAd9Ay4g54BN9Ay4A56BtxAy4A76BlwBz0DbqBlwB30DLiDnoOFk9+BFtK9e3ddcskluuaaa5K9FAC7gJYBd9Az4A56BtxAy4A76BlwAy0D7qBnwB30DLiBlgF30DPgDnoOFk5+B1pIQUGBpk2bpp49eyZ7KQB2AS0D7qBnwB30DLiBlgF30DPgBloG3EHPgDvoGXADLQPuoGfAHfQcLKFkLwBIFaeeeqreeeedZC8DwC6iZcAd9Ay4g54BN9Ay4A56BtxAy4A76BlwBz0DbqBlwB30DLiDnoMlLdkLAFJF7969df311+vTTz/VoEGDlJ6e3uTt//d//5eklQGIBS0D7qBnwB30DLiBlgF30DPgBloG3EHPgDvoGXADLQPuoGfAHfQcLJ4xxiR7EUAq6NGjx3bf5nme5s+f34KrARAvWgbcQc+AO+gZcAMtA+6gZ8ANtAy4g54Bd9Az4AZaBtxBz4A76DlYOPkdAAAAAAAAAAAAAAAAAAAAAOB7oWQvAAAAAAAAAAAAAAAAAAAAAACAnUlL9gKAVLJ06VK9/vrrWrx4sWpra5u87b777kvSqgDEipYBd9Az4A56BtxAy4A76BlwAy0D7qBnwB30DLiBlgF30DPgDnoODk5+B1rIhAkTdOKJJ6pnz56aNWuWBg4cqIULF8oYo7322ivZywPQTLQMuIOeAXfQM+AGWgbcQc+AG2gZcAc9A+6gZ8ANtAy4g54Bd9BzsISSvQAgVYwcOVJXXXWVvv76a2VlZemVV17RkiVLdNhhh+nUU09N9vIANBMtA+6gZ8Ad9Ay4gZYBd9Az4AZaBtxBz4A76BlwAy0D7qBnwB30HCyeMcYkexFAKsjPz9e0adPUq1cvFRUVafLkyRowYICmT5+u4cOHa+HChcleIoBmoGXAHfQMuIOeATfQMuAOegbcQMuAO+gZcAc9A26gZcAd9Ay4g56DhUd+B1pIbm6uamtrJUkdO3ZUaWlp49vWrl2brGUBiBEtA+6gZ8Ad9Ay4gZYBd9Az4AZaBtxBz4A76BlwAy0D7qBnwB30HCxpyV4AkCr2339/TZ48Wf3799dxxx2nK6+8Ul9//bXGjh2r/fffP9nLA9BMtAy4g54Bd9Az4AZaBtxBz4AbaBlwBz0D7qBnwA20DLiDngF30HOweMYYk+xFAKlg/vz5qqio0B577KHNmzfryiuv1Mcff6w+ffrovvvuU/fu3ZO9RADNQMuAO+gZcAc9A26gZcAd9Ay4gZYBd9Az4A56BtxAy4A76BlwBz0HCye/Q9FoVMuXL1d+fr48z0v2cpwUiUT06aefasCAASosLEz2clKWMUbl5eXq1KmTQqFQspeTEPScWLTsH673TMuJR8/+Qc/YVfTsD7SMXUXL/kHP2FX07B/0jF1By/5By9hV9Owf9IxdRc/+4HrLEj0nGi37h+s903Li0bN/0DN2FT37Qywtp7XQmuBjy5cvV9euXZO9DKDFLFmyRF26dEn2MhKCnpFqXO2ZlpGK6BlwAy0D7qBnwB30DLiBlgF30DPgBldblugZqcfVnmkZqYieATc0p2VOfofy8/MlSYeET1Sal25trqmvtzYLwZDWqYP1mb//z7vWZlVWRHXagQsbf+ZdtPVzOzT7ZKs9RyurrM1CMIRysq3PfOTL96zNqqiIaui+a53teevndVj+aUrzMqzNjZRXWJvVKBFPIpSIv1S2vE4vzf5udCL2ncKti63PvGjCJ1bnVVZEdO7Bc53vOQjb5kTc9kera63P9NLt9hduXWR1niRF1m2wPjOUm2N9ZsnYSmuzajfX6dnjX3W+5YN1nNJkr+VEfF8ViVgfGa2x33JC9iGCIBS2PvLOGR9bnbe5Iqoj91/tfM+H9bhIaaFMa3OjS1dYm9Vo957WR3rfzbc+M9ShndV5de1bWZ0nSWlzllqf6eXnWp+5Yf9OVudF6qo17dXbnO/50JxT7O5rV9VYm7VVKMP+MWQits9ehr37ICTJ1Nj/WibieDzcprX1mUe9PtvarOqKet10xEfOt3ywjrfackqzvK8dyrK3z7SVidg/Hgi3s9/yb98ab3VeZUVUZx003/mebW+bE3GcW79PP+szw1/Yu+3fyvZd5dHaBNwHXWB/vzhaYe8+q60yx9m7r7x+c63Gn/K0sy1LibsvDLDh5dnTrc0qr4iqx96Lne05Ub9zVgKOy6r2KrE+M+db+/fXRTdusj7TNhOJWp+ZiN8hzr2mj9V50epqLb3J/fvBDvZOsLuvbez/vHhp9vcdTF0Cfk+Voo+gHy4qtD7z8kkfWJsVy/mdnPyOxqfCSPPS7Z78nqI3EKnM5i+Nt8rNt/9UNC4//UvTnu0dvEQ9/pgl1YRsHvxukU/PzfZ9yxlWW/YS8gvEgJz8bnmdnpeAk98T8HmHQ/Zbzsm3f9KelAo9+3/bnIjb/qhn/zbCdn/hBOzDJuL2NpSAnjPy6qzPdL5l2T1uTkR38hJw8nsCWk7IPkQQePa3o3kJ2M+WUqDnUKbSwhZPfk/EvrbF9W3lJeB2J2R5W2rSsqzOk2T3F7xbeAnYhwin2//cpRTo2fq+dgJ+SZyA24jE7GvbXadJwNcyEcfjiTh2zsqzv87UaJmT6+ywfPJ7Arajibh9SMTxfS73g8UkUdvmRBznKgH7nOEE3IbZ/lmJJuI+6ITcp2j/Pqv03AQcEzjaspS4+8IAGwr4nXOzJep3zgrZP95JS8T9QQnYP4wm4j59y0wC9p0ScUwQyuJ+sFgk7tg5EffdJODk90T8nsrRn5WdScT9YMk6vzMxvx0DAAAAAAAAAAAAAAAAAAAAAMAiTn4HAAAAAAAAAAAAAAAAAAAAAPgeJ78DAAAAAAAAAAAAAAAAAAAAAHyPk98tGzZsmDzPk+d5mjZtWot+7JKSksaPvXHjxhb92ICL6BlwAy0D7qBnwB30DLiBlgF30DPgDnoG3EDLgDvoGXAHPQNuoGXADZz8ngAXXHCBVqxYoYEDBzZe9sorr2jYsGFq1aqV8vLytMcee+iWW27R+vXrJUljxoxRYWHhdmeuWbNGF198sbp166bMzEx16NBB/+///T999NFHjdf54osv9MorryTs8wJSET0DbqBlwB30DLiDngE30DLgDnoG3EHPgBtoGXAHPQPuoGfADbQMBB8nvydATk6OOnTooLS0NEnSddddp9NPP11Dhw7V22+/rW+++Ub33nuvpk+frmeffbZZM08++WRNnTpVTz/9tObMmaPXX39dw4YN07p16xqv07ZtWxUXFyfkcwJSFT0DbqBlwB30DLiDngE30DLgDnoG3EHPgBtoGXAHPQPuoGfADbQMBF9ashfgus8//1x33HGHHnjgAV122WWNl5eUlOjoo49u1tNXbNy4UR9++KEmTZqkww47TJLUvXt37bvvvolaNoBtoGfADbQMuIOeAXfQM+AGWgbcQc+AO+gZcAMtA+6gZ8Ad9Ay4gZaBYOKR3xPsueeeU15eni655JJtvn1HT4WxVV5envLy8vTaa6+ppqZml9dUU1OjsrKyJv8A7Bw9A26gZcAd9Ay4w2890zIQH7+1LNEzEC96Btzht55pGYiP31qW6BmIFz0D7vBbz7QMxMdvLUv0DDQHJ78n2Ny5c9WzZ0+lp6fHPSMtLU1jxozR008/rcLCQh100EG69tprNWPGjLjmjRo1Sq1atWr817Vr17jXBqQSegbcQMuAO+gZcIffeqZlID5+a1miZyBe9Ay4w2890zIQH7+1LNEzEC96Btzht55pGYiP31qW6BloDk5+TzBjjJU5J598spYvX67XX39dxxxzjCZNmqS99tpLY8aMiXnWyJEjtWnTpsZ/S5YssbJGwHX0DLiBlgF30DPgDr/1TMtAfPzWskTPQLzoGXCH33qmZSA+fmtZomcgXvQMuMNvPdMyEB+/tSzRM9AcnPyeYH379tX8+fNVV1e3y7OysrJ09NFH6/rrr9fHH3+sESNG6MYbb4x5TmZmpgoKCpr8A7Bz9Ay4gZYBd9Az4A6/9UzLQHz81rJEz0C86Blwh996pmUgPn5rWaJnIF70DLjDbz3TMhAfv7Us0TPQHJz8nmBnnnmmKioq9Oijj27z7Rs3box79u67767NmzfH/f4AYkPPgBtoGXAHPQPuoGfADbQMuIOeAXfQM+AGWgbcQc+AO+gZcAMtA8GUluwFuG6//fbT1VdfrSuvvFLLli3Tz3/+c3Xq1Enz5s3T6NGjdfDBB+uyyy6TJEUiEU2bNq3J+2dmZqpdu3Y69dRTdd5552mPPfZQfn6+vvzyS919990aPnx4Ej4rIDXRM+AGWgbcQc+AO+gZcAMtA+6gZ8Ad9Ay4gZYBd9Az4A56BtxAy0AwcfJ7C7jrrru0995765FHHtHo0aMVjUbVq1cvnXLKKTr33HMbr1dRUaEhQ4Y0ed9evXpp5syZ2m+//XT//fertLRUdXV16tq1qy644AJde+21Lf3pACmNngE30DLgDnoG3EHPgBtoGXAHPQPuoGfADbQMuIOeAXfQM+AGWgaCh5PfW8hpp52m0047bbtvHzFihEaMGLHdt48aNUqjRo1KwMoAxIqeATfQMuAOegbcQc+AG2gZcAc9A+6gZ8ANtAy4g54Bd9Az4AZaBoIllOwFuOjRRx9VXl6evv766xb9uAMGDNCxxx7boh8TcB09A26gZcAd9Ay4g54BN9Ay4A56BtxBz4AbaBlwBz0D7qBnwA20DAQfj/xu2XPPPaeqqipJUrdu3Vr0Y7/11luqq6uTJBUUFLToxwZcRM+AG2gZcAc9A+6gZ8ANtAy4g54Bd9Az4AZaBtxBz4A76BlwAy0DbuDkd8s6d+6ctI/dvXv3pH1swEX0DLiBlgF30DPgDnoG3EDLgDvoGXAHPQNuoGXAHfQMuIOeATfQMuAGTn5Ho0Uj91IoK8vavB63TrE2aytTV299pqIR+zNTVP3yFdZnjhh3kbVZ0apqSX+yNs/XwmHJC1sb56VnWJu1lamvsz5TxtifmaK8NPu7CGui9mZWRKPWZvlZtLJKUS8B2z6LRi+abH3mRd0Ptj7TNlPv7+/LVpG166zPfGif/a3Oqze1kmZZnelHocJWCoUyrc2LVlZam7WV16Wj9Zmhxcusz1TY3j6OJEU6FFmdJ0nexk3WZyoBx0JvTtnD2qyGfe0U4HkN/ywxNTXWZm01/x+7W5/Z89zZ1mdaP74PyrF9AtZ55j1XWZ0Xqa2WdK3VmX4ULcxRNGzvfjAtsP+99eYstj4zWpuAY/F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= tokenizer.convert_ids_to_tokens(model_inputs.input_ids[0])\n","print(tokens)\n","\n","\n","n_layers = len(model_output.attentions)\n","n_heads = len(model_output.attentions[0][0])\n","fig, axes = plt.subplots(6, 12)\n","fig.set_size_inches(18.5*2, 10.5*2)\n","for layer in range(n_layers):\n","    for i in range(n_heads):\n","        axes[layer, i].imshow(model_output.attentions[layer][0, i])\n","        axes[layer][i].set_xticks(list(range(9)))\n","        axes[layer][i].set_xticklabels(labels=tokens, rotation=\"vertical\")\n","        axes[layer][i].set_yticks(list(range(9)))\n","        axes[layer][i].set_yticklabels(labels=tokens)\n","\n","        if layer == 5:\n","            axes[layer, i].set(xlabel=f\"head={i}\")\n","        if i == 0:\n","            axes[layer, i].set(ylabel=f\"layer={layer}\")\n","\n","plt.subplots_adjust(wspace=0.3)\n","plt.show()"],"id":"SH_MAK-soD4F"},{"cell_type":"markdown","metadata":{"id":"uumcErs2-X80"},"source":["## Part 1: Finetuning\n","\n","For your projects, you are much more likely to want to finetune a pretrained model. This is a little bit more involved, but is still quite easy."],"id":"uumcErs2-X80"},{"cell_type":"markdown","metadata":{"id":"WDdGp4Ua-X81"},"source":["### 2.1 Loading in a dataset\n","\n","In addition to having models, the [the hub](https://huggingface.co/datasets) also has datasets."],"id":"WDdGp4Ua-X81"},{"cell_type":"code","execution_count":null,"metadata":{"id":"OTsW-Wwi-X81","colab":{"base_uri":"https://localhost:8080/","height":484,"referenced_widgets":["53e5505d6b8e40fd8526a2286d3b4427","99ab67176be349a9b2aa772d68fbded5","1158d41eac344485bd1aa9b11626fad3","5427832b55d342d28168c339cd2cefb2","d5045b4a0a8241d7b0c9058b337241f4","efaa5d4288e4448cada8c2c4286f361a","40d27e539f894a219b921964ae451b0c","ef559d3188b94d069b6a17d39e933d8b","bccea1354cbb4ea0b25b5eec04be7eb1","cdf8e4e1ea9a45c78d93abe5c37a05ba","9fbbf8e865ad45cc88cc8f07c4e73f68","eba71a72edb9480382482c23f82d2755","343b07185fc947e8b1e9660e0375948d","bcadea7929bb4f4cb357976f1c6a3a1b","1795f389c1dc4c65a4d6c258467732fb","a4f5e911244645cdb7203c813cac3216","a4356cb17a2f4752a8b2bce59c30c2a0","f378a00c49834315ab93b2a13f1ef112","1b587b18010c49b397bf64ee45df618c","e3ee0901e9da44b79e8ff1985a7ad0e1","aeec8b3cef6b46f9a85b7be5b61f9b07","9edebb12962d400cb94a626c44040f6a","96b2dad87f5348b4bbc985d4d64d623a","b02ff8cd1ee04337a9311986a80e2bc0","ce44c748275e45e687cd45f7484f093d","fdc31519873b40d48081fdb6c0845482","57f681441d584779ab9f198d35e44bd7","710e2d7c8c4047e38af04d0ded98ae85","095e66894f49410faa60c1b77b82af24","73179b16da9b4d8894b9e6b8da235a22","df09d039f4c641559f560bc4783a54dd","4cf659cf9c6c480a941112e790f0a2c9","48178ff9363e4e6794bbce2ad2eaa423","81cae148644643bfa128b87de106ad5b","8d4274e8266b47f7800f9eb469f56b98","3f22a2dedbe743948e452663aa9fccd4","a73b03443a1a4b81bacf096f02ff251f","21b6d4029e394f1dbad48e90528d43bc","742bdbbf2bee45bb98bbf5c570b49281","ed984f362fa24d13af8bd74abce7d298","ed7daaab7fbd40e9a3345faba8804001","efb0f57f13f04f9a8f2b35bda1b67557","56da068857c944078c87d3fa98603d0c","9d65ebe6c02d4d8e97aff1b1c4edc854","acfc82cb20414f91874f564bd30c5592","7f4764a643f34019a95b9e2769d0d865","2188bf306009482f8253922e9c6aafb6","3953c2eebcf9447eb751b23854c04709","d93f818261a34d99af7a18bd3f29e19c","e24208ea291b45978328ce88025635ce","86347bf875b2400e9a1dfa60bc15eb0f","1aafee47ab7e4054807012fa564233a7","3c6aba4a433a40e2991f4ca0db876763","446b118064fd4a0ead4a9b9fcb76080e","368c54595e0c4a70a42a504cc66d1924","e5776a1bcf2647eeae41aeaa3854826d","f6998f280463497ba4ac22d945801731","dba8dc75ecd0488c83746140fbbdda68","7b1f7a8edd784a48a44c85251d6ad6d9","7cc3fd47b20f45348982317c37d8378d","f40ffa3654d94117987e0ba68edf0484","cd0b0a553a204ba4a9e43e20c6f1f2d2","6cf2615b0381411aafc78ae0d08488ee","8353888de6614befac53f12b5de451cc","24c40406f3114eb49607c8ddc31d63f0","ca7a6b56e9d74be6a51f8132fec007b7","0791f7366fa040199ceb8c567d14fe7d","cd9b64fcc4b646d8989d83aa8e3173a0","27de12c02f3e4451939dca29da590736","999aecd5bd0e47e5b29deabc5e30797e","35e8dd5b6d384322b5cecbfeb3ea23f1","7469094850804b17bf3e00b2b91fc444","71b379f180d44cc29f5d952cdc93a933","7a9241653a7d431cbb1f5e1e11275c70","aa21647ad1df4e3f90beb54908d70e24","9e599c6790b74b49bf98daf59b8da0fb","42b3dac174a64386b09d1f9a2668430d"]},"executionInfo":{"status":"ok","timestamp":1770365649715,"user_tz":480,"elapsed":2522,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"d2a575b0-6ac8-42ef-a21f-f14e22519d16"},"outputs":[{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53e5505d6b8e40fd8526a2286d3b4427"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/train-00000-of-00001.parquet:   0%|          | 0.00/21.0M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eba71a72edb9480382482c23f82d2755"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/test-00000-of-00001.parquet:   0%|          | 0.00/20.5M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"96b2dad87f5348b4bbc985d4d64d623a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/unsupervised-00000-of-00001.p(…):   0%|          | 0.00/42.0M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81cae148644643bfa128b87de106ad5b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"acfc82cb20414f91874f564bd30c5592"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5776a1bcf2647eeae41aeaa3854826d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0791f7366fa040199ceb8c567d14fe7d"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 25000\n","    })\n","    test: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 25000\n","    })\n","    unsupervised: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 50000\n","    })\n","})"]},"metadata":{},"execution_count":19}],"source":["from datasets import load_dataset, DatasetDict\n","\n","\n","\n","# DataLoader(zip(list1, list2))\n","dataset_name = \"stanfordnlp/imdb\"\n","\n","imdb_dataset = load_dataset(dataset_name)\n","\n","\n","# Just take the first 50 tokens for speed/running on cpu\n","def truncate(example):\n","    return {\n","        'text': \" \".join(example['text'].split()[:50]),\n","        'label': example['label']\n","    }\n","\n","imdb_dataset"],"id":"OTsW-Wwi-X81"},{"cell_type":"code","execution_count":null,"metadata":{"id":"vCaX-gNo0OEV","colab":{"base_uri":"https://localhost:8080/","height":255,"referenced_widgets":["478a9fadd2c540ed9cd3add2a60f6258","f6bdd63087614b2ca2374168bd0d4a58","fb5b03c23d094d5c92b628afd72dd542","9e29b59f269149f7a870cda6089cbdc4","563d15464a0f4c0486a4817304f52df0","7feb97983cc249f493d678e74e221093","7aa76134417b4cf3bf956f9e1803374f","952087561b0243ce9d67ef36992794dd","16c6750f024245528dacf81992f95da8","e34e1c24a1d04926aafedf5c008b21dc","d171f444116c4875ae35229435c8d403","7209724a409443f2a5d1f9c80afecc66","f754a37ea0314ddbb545c2acabec3a8a","16f589f6ab194882865afe0972875644","a8edb36e86d84dd4aa122df208fbcd98","3385b455d8254841bbdbbaf64f624b86","1edb84c984354b2d84b619e38d2ff73f","ec6bd2c15abd4e4fb796b99c910a881c","98f6f5582b92438399acecd226a0d12f","906646ec827a4bd19e02163f21478909","0f4f64a64dae4321b5466f6113c8108a","b5d547596c894b70b773ad77ab03d183"]},"executionInfo":{"status":"ok","timestamp":1770365651284,"user_tz":480,"elapsed":1554,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"99f98b5d-6bae-4a66-e7a8-597261b848bf"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/128 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"478a9fadd2c540ed9cd3add2a60f6258"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/32 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7209724a409443f2a5d1f9c80afecc66"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 128\n","    })\n","    val: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 32\n","    })\n","})"]},"metadata":{},"execution_count":20}],"source":["\n","# Take 128 random examples for train and 32 validation\n","small_imdb_dataset = DatasetDict(\n","    train=imdb_dataset['train'].shuffle(seed=1111).select(range(128)).map(truncate),\n","    val=imdb_dataset['train'].shuffle(seed=1111).select(range(128, 160)).map(truncate),\n",")\n","small_imdb_dataset"],"id":"vCaX-gNo0OEV"},{"cell_type":"code","execution_count":null,"metadata":{"id":"bBS4c44A-X82","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365652786,"user_tz":480,"elapsed":1104,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"606229e1-63dd-49e3-98b1-778f1412d683"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': [\"Probably Jackie Chan's best film in the 1980s, and the one that put him on the map. The scale of this self-directed police drama is evident from the opening and closing scenes, during which a squatters' village and shopping mall are demolished. There are, clearly, differences between the original Chinese\",\n","  'A wonderful movie! Anyone growing up in an Italian family will definitely see themselves in these characters. A good family movie with sadness, humor, and very good acting from all. You will enjoy this movie!! We need more like it.',\n","  'HORRENDOUS! Avoid like the plague. I would rate this in the top 10 worst movies ever. Special effects, acting, mood, sound, etc. appear to be done by day care students...wait, I have seen programs better than this. Opens like a soft porn show with a blurred nude female doing a',\n","  'And I absolutely adore Isabelle Blais!!! She was so cute in this movie, and far different from her role in \"Quebec-Montreal\" where she was more like a man-eater. I think she should have been nominated for a Jutra. I mean, Syvlie Moreau was good, but Isabelle was far superior, IMO.',\n","  'Must confess to having seen a few howlers in my time, but this one is up there with the worst of them. Plot troubling to follow. Sex and violence thrown in to disorient and distract from the really poorly put together film.<br /><br />I can only imagine that the cast',\n","  \"I pity people calling kamal hassan 'ulaganaayakan' maybe for them ulagam is tollywood ! comeon guys..this movie is a thriller without thrill..<br /><br />come out of your ulagam and just watch some high class thrillers like The Usual Suspects or even The Silence of the Lambs.<br /><br />technically good but\",\n","  'I remember my parents not understanding Saturday Night Live when I was 15. They also did not understand Rock n Roll and many other things. Now that I am approaching their age, I still remember, and find I understand many of the things my kids love. But this is pathetic.',\n","  'This animated movie is a masterpiece! The narration, music, animation, and storyline where all remarkable. My girlfriend and I saw it again for a second time and we got more insight from it. We invited a couple friends to see Spirit with us and they really enjoyed it a lot.',\n","  'I vaugely recall seeing this when I was 3 years old, then my parents accidentally taped over all but a few seconds of it with some other cartoon. Then I was about 8 or 9 years old when I rediscovered it and since I was then able to comprehend things',\n","  'I, like many people, saw this film in the theatre when it first came out in \\'97. It was a below average film at best, defiantly not the \"masterpiece\" that all these \"Titanic\" fanboys like to make it out as. First off, DiCaprio is a terrible actor no matter which'],\n"," 'label': [1, 1, 0, 1, 0, 0, 0, 1, 1, 0]}"]},"metadata":{},"execution_count":21}],"source":["small_imdb_dataset['train'][:10]"],"id":"bBS4c44A-X82"},{"cell_type":"code","execution_count":null,"metadata":{"id":"3bjqop3N-X8_","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["f2f9c8314cd44a7cbb9425e75af293c0","0c988a5ace0a4e8b8762714f297cf57c","68ef3ddfffc741899bf79ef2f24e7a8a","7a81dd1673ca423d8800cac637e66cb1","bc165aff40334447b1f836638801c21a","8001d81644ee4a7087a64824d9139c94","7afebf7e36db41b6b096b20b450af57e","70ad9efbc07f4d2da4f0c7f503dfe4b5","ac6b0e6893b34b1aa8749429178873b2","6ea79607dd1941d1a628bc155ac72516","7c280586312e48d5b89c80b369e5633a","3523388befea407e82a9a622d9ea3504","4ae967616aa14919adc3d56b74a02c49","31c808e1c6cd49dd9227e8d00b827239","fa3e721eb9ce4acda0cc0b00a57d6e77","6272873054fc407da4c452fed19d7217","8e40d5ee84ae4d7097f3a44e19e5ec12","05a2c2ca942546d99fb5aaab7a5b291f","fa1154d87988444392b2d4dec34dc874","1e741c80390342a281a761597855fd01","e55fbb1f112346e9b07834b532b11d83","ffa1ca73f85047afabc09e0efdc7e41d"]},"executionInfo":{"status":"ok","timestamp":1770365654806,"user_tz":480,"elapsed":1500,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"f74c4e2b-e242-416f-a25a-73aa306d6abf"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/128 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f2f9c8314cd44a7cbb9425e75af293c0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/32 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3523388befea407e82a9a622d9ea3504"}},"metadata":{}}],"source":["# Prepare the dataset - this tokenizes the dataset in batches of 16 examples.\n","small_tokenized_dataset = small_imdb_dataset.map(\n","    lambda example: tokenizer(example['text'], padding=True, truncation=True), # https://huggingface.co/docs/transformers/pad_truncation\n","    batched=True,\n","    batch_size=16\n",")\n","\n","small_tokenized_dataset = small_tokenized_dataset.remove_columns([\"text\"])\n","small_tokenized_dataset = small_tokenized_dataset.rename_column(\"label\", \"labels\")\n","small_tokenized_dataset.set_format(\"torch\")"],"id":"3bjqop3N-X8_"},{"cell_type":"code","execution_count":null,"metadata":{"id":"450eUYlf-X8_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365655858,"user_tz":480,"elapsed":685,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"747ab962-69f4-4953-f7f4-589702e6f4d1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'labels': tensor([1, 1]),\n"," 'input_ids': tensor([[  101, 10109,  9662, 10185,   112,   188,  1436,  1273,  1107,  1103,\n","           3011,   117,  1105,  1103,  1141,  1115,  1508,  1140,  1113,  1103,\n","           4520,   119,  1109,  3418,  1104,  1142,  2191,   118,  2002,  2021,\n","           3362,  1110, 10238,  1121,  1103,  2280,  1105,  5134,  4429,   117,\n","           1219,  1134,   170,  4816,  6718, 18899,   112,  1491,  1105,  6001,\n","           8796,  1132,  6515,   119,  1247,  1132,   117,  3817,   117,  5408,\n","           1206,  1103,  1560,  1922,   102,     0,     0,     0,     0,     0,\n","              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","              0,     0],\n","         [  101,   138,  7310,  2523,   106, 15859,  2898,  1146,  1107,  1126,\n","           2169,  1266,  1209,  5397,  1267,  2310,  1107,  1292,  2650,   119,\n","            138,  1363,  1266,  2523,  1114, 12928,   117,  8594,   117,  1105,\n","           1304,  1363,  3176,  1121,  1155,   119,  1192,  1209,  5548,  1142,\n","           2523,   106,   106,  1284,  1444,  1167,  1176,  1122,   119,   102,\n","              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n","              0,     0]]),\n"," 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n","         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),\n"," 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n","         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","          1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"]},"metadata":{},"execution_count":23}],"source":["small_tokenized_dataset['train'][0:2]"],"id":"450eUYlf-X8_"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q3e7_htt-X8_"},"outputs":[],"source":["from torch.utils.data import DataLoader\n","\n","train_dataloader = DataLoader(small_tokenized_dataset['train'], batch_size=16)\n","eval_dataloader = DataLoader(small_tokenized_dataset['val'], batch_size=16)"],"id":"Q3e7_htt-X8_"},{"cell_type":"markdown","metadata":{"id":"qRm6Tw_z-X8-"},"source":["### 2.2 Training\n","\n","To train your models, you can just use the same kind of training loop that you would use in Pytorch. Hugging Face models are also `torch.nn.Module`s so backpropagation happens the same way and you can even use the same optimizers. Hugging Face also includes optimizers and learning rate schedules that were used to train Transformer models, so you can use these too.\n","\n","For optimization, we're using the AdamW Optimizer, which is almost identical to Adam except it also includes weight decay.\n","And we're using a linear learning rate scheduler, which reduces the learning rate a little bit after each training step over the course of training.\n","\n","There are other optimizers and learning rate schedulers you can use, but these are the default. If you want to explore, you can look at the ones [Hugging Face offers](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules#schedules), the ones available through [Pytorch](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) (e.g. [ReduceLROnPlateau](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html), which only decreases the learning rate when the validation loss stops decreasing), or write your own (like the one in Assignment 4)."],"id":"qRm6Tw_z-X8-"},{"cell_type":"code","execution_count":null,"metadata":{"id":"A7CvjTIv-X9A","colab":{"base_uri":"https://localhost:8080/","height":393,"referenced_widgets":["20588b8e36504d48924b2b9807271bb3","0347e21226fa488ebbd767cd40871664","1ea3ac36ea5b4880809e239f1052c759","7c8c5654b8c644029961f188672834ff","ba632335d5fa4d8188908ba93c6ce466","e94611b5968f49d49859bbf2d6f16879","4346dffb04c74c79b3d6c59d50dacf4b","178105cde35443edb3cd2254e41ba2f6","cda1c49f3de7415e9e1289e4d2453d66","5f04b998e7a741bfb79c28622b34d13e","edbc13cead0f452b8a07e19dcb32199e","ac6e2f748fa14f2d909c6318db4b74db","9adf08ff7c034a6da9116fc468851f28","d14b4c6cbfc3418393b3adcd372e03e8","f455d62a5a08430ab492d6ca26aab17c","758d29160e514ab0a8b4c51ce855bc13","e93c4cfa8a1e488488c680116b6c0b21","ecddb830e10a4c568623d47c1b849b9f","ed411edb600e4527bd368f4aa76051f8","232eb8cdbd754cb4945fde91128c3b08","df59fceb812a412d9411aaf5d2d3f998","2c161d8dcab547bd863d6f1d596ebade"]},"executionInfo":{"status":"ok","timestamp":1770365692998,"user_tz":480,"elapsed":37122,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"87d39180-8888-45e9-c5e2-c569f94f7cd0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"20588b8e36504d48924b2b9807271bb3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-cased\n","Key                     | Status     | \n","------------------------+------------+-\n","vocab_transform.bias    | UNEXPECTED | \n","vocab_projector.bias    | UNEXPECTED | \n","vocab_layer_norm.bias   | UNEXPECTED | \n","vocab_transform.weight  | UNEXPECTED | \n","vocab_layer_norm.weight | UNEXPECTED | \n","pre_classifier.bias     | MISSING    | \n","classifier.bias         | MISSING    | \n","classifier.weight       | MISSING    | \n","pre_classifier.weight   | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/8 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac6e2f748fa14f2d909c6318db4b74db"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Validation loss: 1.1481705904006958\n","Saving checkpoint!\n"]}],"source":["from transformers import get_linear_schedule_with_warmup\n","from torch.optim import AdamW\n","from tqdm.notebook import tqdm\n","\n","\n","model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n","\n","num_epochs = 1\n","num_training_steps = len(train_dataloader)\n","optimizer = AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)\n","lr_scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=num_training_steps)\n","\n","best_val_loss = float(\"inf\")\n","progress_bar = tqdm(range(num_training_steps))\n","for epoch in range(num_epochs):\n","    # training\n","    model.train()\n","    for batch_i, batch in enumerate(train_dataloader):\n","\n","        # batch = ([text1, text2], [0, 1])\n","\n","        output = model(**batch)\n","\n","        optimizer.zero_grad()\n","        output.loss.backward()\n","        optimizer.step()\n","        lr_scheduler.step()\n","        progress_bar.update(1)\n","\n","    # validation\n","    model.eval()\n","    for batch_i, batch in enumerate(eval_dataloader):\n","        with torch.no_grad():\n","            output = model(**batch)\n","        loss += output.loss\n","\n","    avg_val_loss = loss / len(eval_dataloader)\n","    print(f\"Validation loss: {avg_val_loss}\")\n","    if avg_val_loss < best_val_loss:\n","        print(\"Saving checkpoint!\")\n","        best_val_loss = avg_val_loss\n","        # torch.save({\n","        #     'epoch': epoch,\n","        #     'model_state_dict': model.state_dict(),\n","        #     'optimizer_state_dict': optimizer.state_dict(),\n","        #     'val_loss': best_val_loss,\n","        #     },\n","        #     f\"checkpoints/epoch_{epoch}.pt\"\n","        # )"],"id":"A7CvjTIv-X9A"},{"cell_type":"code","source":[],"metadata":{"id":"2nz1v79ZbgHq"},"id":"2nz1v79ZbgHq","execution_count":null,"outputs":[]},{"cell_type":"code","source":["batch['input_ids'].max()"],"metadata":{"id":"cl_JX8HKbVmb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365693029,"user_tz":480,"elapsed":20,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"b65b5402-57b7-4bb0-d74f-6196968e0780"},"id":"cl_JX8HKbVmb","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(27013)"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"YrfE9c04-X9A"},"source":["While you can use PyTorch to train your models like we did in Assignment 4, Hugging Face offers a powerful `Trainer` class to handle most needs. I think it works pretty well, though there are some customizations I'd recommend."],"id":"YrfE9c04-X9A"},{"cell_type":"code","execution_count":null,"metadata":{"id":"fQDkqtT7-X9B","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["acf66b2cccbd4d98804d8ec78d7f7786","58e5918370d042c4ba2366c74b07ae94","b3f6be728084431c839446bf23266b4f","7263c6b429aa4af3afdb187ba8934d05","928c6fd7d2214165bac90e56eda6bb2f","affa16b5903f492b8ccb113138a22a20","0e0695cfb0234df99b46d18cef0cb8ac","64d0bc6bb6dd448f9b2ff162b4e2a7ba","5ae622e0d9ef4f858abad56873752a99","bf5e54042e75474fa604eb009783b82e","f6d33488c4784b85a95c68ae5f5d0a71","c979e2dbd1434bfea3a2965634ff67ac","336b51e7707941f4b6dc777715cd45e7","05e57f460bc34df8acb08c7d367fcc8b","f5f93075dded4d07a947965e037801e7","14a79507613e4c858ac85f2db2ed4e78","c3ac305fb637493fa602648b82eb71cf","5e59ea2f3f4848cf9d547b3747e79f58","5e0a6c2a92234f7781e1e1a7874eaa83","212129c040ef465bac89be7d88e78c1d","2019f0f37d6f491e9fc097f02a04a566","16ef53de63264e4ca64f129aeb9f7078"]},"executionInfo":{"status":"ok","timestamp":1770365694133,"user_tz":480,"elapsed":1088,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"d9f2a0d3-57ef-4682-db0d-cd1a60aa57ab"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/128 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"acf66b2cccbd4d98804d8ec78d7f7786"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/32 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c979e2dbd1434bfea3a2965634ff67ac"}},"metadata":{}}],"source":["imdb_dataset = load_dataset(\"stanfordnlp/imdb\")\n","\n","small_imdb_dataset = DatasetDict(\n","    train=imdb_dataset['train'].shuffle(seed=1111).select(range(128)).map(truncate),\n","    val=imdb_dataset['train'].shuffle(seed=1111).select(range(128, 160)).map(truncate),\n",")\n","\n","small_tokenized_dataset = small_imdb_dataset.map(\n","    lambda example: tokenizer(example['text'], padding='max_length', truncation=True, max_length=512),\n","    batched=True,\n","    batch_size=16\n",")"],"id":"fQDkqtT7-X9B"},{"cell_type":"markdown","metadata":{"id":"RULukU5eoD4M"},"source":["`TrainingArguments` specifies different training parameters like how often to evaluate and save model checkpoints, where to save them, etc. There are **many** aspects you can customize and it's worth checking them out [here](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments). Some things you can control include:\n","* learning rate, weight decay, gradient clipping,\n","* checkpointing, logging, and evaluation frequency\n","* where you log to (default is tensorboard, but if you use WandB or MLFlow they have integrations)\n","\n","The `Trainer` actually performs the training. You can pass it the `TrainingArguments`, model, the datasets, tokenizer, optimizer, and even model checkpoints to resume training from. The `compute_metrics` function is called at the end of evaluation/validation to calculate evaluation metrics."],"id":"RULukU5eoD4M"},{"cell_type":"code","execution_count":null,"metadata":{"id":"FEmERCu_-X9B","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":327,"referenced_widgets":["911fca2da57b4d26aea49dfb1dd10794","d3773e4b137e44258777d965f05f0f40","ae225c0d330a49f7bf04bf220f13951b","696ea33f406e443a9d90ce468ca21b55","59102f142c364a03862e952c894a0e78","9ec0f787e8ed417182dc9110e48420aa","8e9cb8e2bdf848319f48607e80330b4e","32f75e18474f463ca7461ef1666a582b","5360c3e4197b4ac3a77681a0e2a25683","eea7acaf993642f19f2d08febcf583da","d2bb31bef6974a94bcb8fa2febd34bd4"]},"executionInfo":{"status":"ok","timestamp":1770365697605,"user_tz":480,"elapsed":3470,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"2571f166-9e3b-446d-ced3-1b90e4bbf9dd"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"911fca2da57b4d26aea49dfb1dd10794"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-cased\n","Key                     | Status     | \n","------------------------+------------+-\n","vocab_transform.bias    | UNEXPECTED | \n","vocab_projector.bias    | UNEXPECTED | \n","vocab_layer_norm.bias   | UNEXPECTED | \n","vocab_transform.weight  | UNEXPECTED | \n","vocab_layer_norm.weight | UNEXPECTED | \n","pre_classifier.bias     | MISSING    | \n","classifier.bias         | MISSING    | \n","classifier.weight       | MISSING    | \n","pre_classifier.weight   | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]}],"source":["from transformers import TrainingArguments, Trainer\n","\n","model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n","\n","arguments = TrainingArguments(\n","    output_dir=\"sample_hf_trainer\",\n","    per_device_train_batch_size=16,\n","    per_device_eval_batch_size=16,\n","    num_train_epochs=2,\n","    eval_strategy=\"epoch\", # run validation at the end of each epoch\n","    save_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    load_best_model_at_end=True,\n","    seed=224\n",")\n","\n","\n","def compute_metrics(eval_pred):\n","    \"\"\"Called at the end of validation. Gives accuracy\"\"\"\n","    logits, labels = eval_pred\n","    predictions = np.argmax(logits, axis=-1)\n","    # calculates the accuracy\n","    return {\"accuracy\": np.mean(predictions == labels)}\n","\n","\n","trainer = Trainer(\n","    model=model,\n","    args=arguments,\n","    train_dataset=small_tokenized_dataset['train'],\n","    eval_dataset=small_tokenized_dataset['val'], # change to test when you do your final evaluation!\n","    compute_metrics=compute_metrics\n",")"],"id":"FEmERCu_-X9B"},{"cell_type":"markdown","metadata":{"id":"MbyK1W95-X9B"},"source":["#### Callbacks: Logging and Early Stopping\n","\n","\n","Hugging Face Transformers also allows you to write `Callbacks` if you want certain things to happen at different points during training (e.g. after evaluation or after an epoch has finished). For example, there is a callback for early stopping, and I usually write one for logging as well.\n","\n","For more information on callbacks see [here](https://huggingface.co/docs/transformers/main_classes/callback#transformers.TrainerCallback)."],"id":"MbyK1W95-X9B"},{"cell_type":"code","execution_count":null,"metadata":{"id":"vKO3TkAnoD4N"},"outputs":[],"source":["from transformers import TrainerCallback, EarlyStoppingCallback\n","\n","class LoggingCallback(TrainerCallback):\n","    def __init__(self, log_path):\n","        self.log_path = log_path\n","    # will call on_log on each logging step, specified by TrainerArguement. (i.e TrainerArguement.logginng_step)\n","    def on_log(self, args, state, control, logs=None, **kwargs):\n","        _ = logs.pop(\"total_flos\", None)\n","        if state.is_local_process_zero:\n","            with open(self.log_path, \"a\") as f:\n","                f.write(json.dumps(logs) + \"\\n\")\n","    # def on_epoch(...)\n","\n","\n","trainer.add_callback(EarlyStoppingCallback(early_stopping_patience=1, early_stopping_threshold=0.0))\n","trainer.add_callback(LoggingCallback(\"sample_hf_trainer/log.jsonl\"))"],"id":"vKO3TkAnoD4N"},{"cell_type":"code","execution_count":null,"metadata":{"id":"tunwonc2-X9C","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":272,"referenced_widgets":["c135cc67160f40e8b1cf1a96c26860ef","9adb900a2ac742ba9d0c07b73cdf4aa9","4245e7d310864969aa7fe4cb1022f03e","f21b6f2a483545e1bd3cebcae3421c2b","4766eb96beee4de19516817ba96e06e5","6632d405fc4c4a11807c267b327afb2c","23ad699a912a4acdbb056dee9693dddb","a6d99e6efec5472b8125a16be50a2620","a527a4206916412a89e081b465090036","7e20fc72bb2341a8b3afc9e59aff1c99","6c7b28c17dbf4edda454c4058ede9b80","7bb5329cb65d4a71ac29ae4a1c4c1258","af69480943f04a95a75910c6ecd49080","4936309260544a999b9b249e44986e2f","e32392cc66e74165b7f7bf72667cf559","7220a3229f1f4d89bc953ac8522d1984","fbe61c84eec349cba68e34cc30070f35","4741aa9930a44841826f0eb40d655e67","7db38945f8ef444ab66454a7adf8c13e","09560cd9b8b7433aa6d801f98b0021dc","c4a1322d576949ddb6ee41a66195dd9b","6c0c4a6877574a1389d10c770e9af36f"]},"executionInfo":{"status":"ok","timestamp":1770365728507,"user_tz":480,"elapsed":25085,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"91b010d7-5a37-409e-cc6f-e7d9edc1c45d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [16/16 00:23, Epoch 2/2]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>0.696584</td>\n","      <td>0.437500</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>0.688462</td>\n","      <td>0.468750</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c135cc67160f40e8b1cf1a96c26860ef"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7bb5329cb65d4a71ac29ae4a1c4c1258"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["There were missing keys in the checkpoint model loaded: ['distilbert.embeddings.LayerNorm.weight', 'distilbert.embeddings.LayerNorm.bias'].\n","There were unexpected keys in the checkpoint model loaded: ['distilbert.embeddings.LayerNorm.beta', 'distilbert.embeddings.LayerNorm.gamma'].\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=16, training_loss=0.6930800080299377, metrics={'train_runtime': 25.0289, 'train_samples_per_second': 10.228, 'train_steps_per_second': 0.639, 'train_loss': 0.6930800080299377, 'epoch': 2.0})"]},"metadata":{},"execution_count":30}],"source":["# train the model\n","trainer.train()"],"id":"tunwonc2-X9C"},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Q185j9V-X9C","colab":{"base_uri":"https://localhost:8080/","height":17},"executionInfo":{"status":"ok","timestamp":1770365746458,"user_tz":480,"elapsed":532,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"78ea996a-5e2a-4217-b671-809ad5e32ab8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}}],"source":["# evaluating the model is very easy\n","\n","# results = trainer.evaluate()                           # just gets evaluation metrics\n","results = trainer.predict(small_tokenized_dataset['val']) # also gives you predictions"],"id":"_Q185j9V-X9C"},{"cell_type":"code","execution_count":null,"metadata":{"id":"UJ0aGxeh-X9D","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365748436,"user_tz":480,"elapsed":17,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"dffeb08f-f241-46fe-b9b4-c4080f450c38"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["PredictionOutput(predictions=array([[ 0.07129372,  0.02969447],\n","       [ 0.08056778,  0.02678281],\n","       [ 0.0634118 ,  0.00768808],\n","       [ 0.09071101,  0.00838343],\n","       [ 0.06805893,  0.03839238],\n","       [ 0.07912882,  0.03911883],\n","       [ 0.09714072, -0.00019298],\n","       [ 0.05575925, -0.00505521],\n","       [ 0.05049302,  0.02381124],\n","       [ 0.0772339 ,  0.0589922 ],\n","       [ 0.087749  ,  0.01944431],\n","       [ 0.11575917, -0.01718631],\n","       [ 0.11654887,  0.00513059],\n","       [ 0.08301201,  0.04862146],\n","       [ 0.1029847 ,  0.00404389],\n","       [ 0.07352939,  0.0139792 ],\n","       [ 0.09378905,  0.01292037],\n","       [ 0.06744891,  0.04538827],\n","       [ 0.09250271,  0.04091704],\n","       [ 0.0400167 ,  0.05796501],\n","       [ 0.09719709,  0.00260161],\n","       [ 0.07710757,  0.00137375],\n","       [ 0.04982676,  0.04539316],\n","       [ 0.09237278,  0.00893394],\n","       [ 0.06950966,  0.02589746],\n","       [ 0.11519994,  0.02891537],\n","       [ 0.0523802 ,  0.04327528],\n","       [ 0.08928558,  0.03334618],\n","       [ 0.04267707,  0.00514271],\n","       [ 0.10109308,  0.00440804],\n","       [ 0.07990889,  0.03045121],\n","       [ 0.06541935,  0.06090667]], dtype=float32), label_ids=array([1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n","       0, 1, 1, 0, 1, 1, 1, 0, 0, 1]), metrics={'test_loss': 0.6884616613388062, 'test_accuracy': 0.46875, 'test_runtime': 0.5118, 'test_samples_per_second': 62.522, 'test_steps_per_second': 3.908})"]},"metadata":{},"execution_count":32}],"source":["results"],"id":"UJ0aGxeh-X9D"},{"cell_type":"code","execution_count":null,"metadata":{"id":"kSGsZo3xoD4O","colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["de47e0db68b049ebaed3ac2a39831fb2","a1916ec6659647c0b7b75df3175bc6c1","1b39466648be44ed8db2b107d9265fde","9bbf939f34384b91b8dfd9c96cb02513","1c50959c1bb24b70b70eb8ba2ca3da80","3967fda6f7be439198268d77e03939bb","f9cba329421a4be98f714f2f4cc36d03","471919dc087a43a3a6d016216ed1cc20","a23ba08a40ba4becae2dd1aa219b8003","baf81761461d4726b9bb1b07e45daf9b","21b4f29e66924ee1a3fb52790a3671e7"]},"executionInfo":{"status":"ok","timestamp":1770365753391,"user_tz":480,"elapsed":475,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"e73d8fd5-5c97-47e3-97b0-4193aeeaf75e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/104 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"de47e0db68b049ebaed3ac2a39831fb2"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["NEGATIVE\n"]}],"source":["# To load our saved model, we can pass the path to the checkpoint into the `from_pretrained` method:\n","test_str = \"I enjoyed the movie!\"\n","\n","finetuned_model = AutoModelForSequenceClassification.from_pretrained(\"sample_hf_trainer/checkpoint-8\")\n","model_inputs = tokenizer(test_str, return_tensors=\"pt\")\n","prediction = torch.argmax(finetuned_model(**model_inputs).logits)\n","print([\"NEGATIVE\", \"POSITIVE\"][prediction])"],"id":"kSGsZo3xoD4O"},{"cell_type":"markdown","metadata":{"id":"MllSTgehoD4O"},"source":["Included here are also some practical tips for fine-tuning:\n","\n","**Good default hyperparameters.** The hyperparameters you will depend on your task and dataset. You should do a hyperparameter search to find the best ones. That said, here are some good initial values for fine-tuning.\n","* Epochs: {2, 3, 4} (larger amounts of data need fewer epochs)\n","* Batch size (bigger is better: as large as you can make it)\n","* Optimizer: AdamW\n","* AdamW learning rate: {2e-5, 5e-5}\n","* Learning rate scheduler: linear warm up for first {0, 100, 500} steps of training\n","* weight_decay (l2 regularization): {0, 0.01, 0.1}\n","\n","You should monitor your validation loss to decide when you've found good hyperparameters."],"id":"MllSTgehoD4O"},{"cell_type":"markdown","metadata":{"id":"gsWGQfrm-X9D"},"source":["There's a lot more that we can integrate into the Trainer to make it more useful including logging, saving model checkpoints, and more! You can even sub-class it to add your own personalized components. You can check out [this link](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer) for more information about the Trainer."],"id":"gsWGQfrm-X9D"},{"cell_type":"markdown","metadata":{"id":"9nCrUosgoD4P"},"source":["## Appendix 0: Generation\n","\n","In the example above we finetuned the model on a classification task, but you can also finetune models on generation tasks. The `generate` function makes it easy to generate from these models. For example."],"id":"9nCrUosgoD4P"},{"cell_type":"code","execution_count":null,"metadata":{"id":"QfQEV8EKoD4P","colab":{"base_uri":"https://localhost:8080/","height":427,"referenced_widgets":["17e9f91e480d4a7d9d4ca36d324c961e","55c920a301b84c36ba2a979b830d21a6","a325c9c24edf4f4eae64b2e4b0fe4439","7394a89fce0a47e1b53be85bbd3b11de","5acc7e45d14b4a14a6e11537561c21f8","1413d33e34754d108023113917b4725b","9af74e0f2f044c489dcbd5e21e027c97","788998ad7534484ead9706547d284a2a","ee2dfe03b4224d16959ac1378dd93867","2084a276dfef40e98d12307c55668daa","900bc8348f88454e87154bef7405d536","5cfaffca37874dcb805e65837f6f9fc1","712ecb070a5f48ce9846c83c4028d40b","637ac34e37294a5d94e19cf9811c3f12","fda7d68fa0294fa2946bd8f84a528acd","6c19688971b146b1a59436fd91ef2f77","48cfe968518d4f069c3d95a81148e045","cd6d37fa7cc24c528d21ef399ed20b43","3cbcb7cb4cfa471dbc130a70bfd1abcc","3f8fc38b0e564fcc93dc735363209a75","57039ffcfe9b436bbb30f78c54d15932","cd2abd6c8c4e4c92897803a4424ffeaf","d13195144fa149938b5d648f29bd7df5","27b0a8f79e4a402a8c201a40eb5998af","f9783b8ff0254cdeb4e8a1d4d593dd07","2ab54f349907434ebce9d6ae7bd825a6","1f262d0e50f24380ac113aa9aba99123","2a6a71a5012847b392d5e2e65c211957","e1d55d1304564bceb37d1a64cd7bd4d1","6e444d6394d747b6873522f79306baf2","5b50e5657dd84a44a7e0dff365e389f3","7f0c4e68a46442ada97e79c517f2bd23","88acb3c4311b49fa84cbc9a1ff65bcd0","bf9fdbd988e0416aa54eda64f9280717","7af1d3ececec4969a75d6be7d1ae242a","496dc873512e45ab877789d28618fa9f","ab36dd8c409d4c21a987305580912aba","c1b0622de5d04b60bb88719337068d37","f6f9d4893e4a47c9857a8f0967c35ff4","8dd3e186d2284b1181883279d334b4ab","04162887505d44a982edbb14afa1fffa","a8925cf25f7947fdab078abd4500adf4","56e1d6e835324cf2b1ed47153b44de19","32e422967ffc4fcc9fd324b86ea23764","a4e1ec9c030c4e618bf6439a41f5e382","fa9de77b071e42f2bb8fab54540915ab","3106d000279e425eb42b830c066d49e6","7211612f377141bcac81d8ab5db6fb2d","b3730434c6694204ae25f6bd77fba5b5","18c524541abc450da2190e39d3b35736","c9b143b6027748aaa7ab41e9612de4ae","2641b867c4244c46b0b327d9edcc0762","87a10bc09c4f479fa6d84692b571f217","679f6473647646c686b4e5f7f57db9e9","6c5385cdf8514f959c59063677eeb88d","645188671f8b4f669e2dadea96d1e9db","0964a5a8493d4f96bf6648bfda0658a0","02f5877c0aa148bd9f5a98b86949aa29","1c90feae1b164599afed0ade57a7a146","1f95e09e4b564ab1bc5e7d5807a49908","3073d3ef6e7e481b92423f801f3d30f6","e3e5475906264607831283bfd7c87575","6041ffd75982415baa5d1a80065e0c5e","d4eba9b571324cb2b91fee47b0faceb9","74c70ecf9aec4924bc43a1287b1a462f","b7d4f30a77804dbf934a02da4b78ed20","663a54fc8fb3461dba40078b4745ca59","11f446542583497589c8a5a834a9f414","0a9c36e7628d44f595b0218e0f0ea7cb","dfd8b686dd82462eaf795a086cdc9c1a","6c4ca0616df348fcabe45c8eed970039","3ddb1f677802449aa74cf4ae800f5aea","cd4490467d3b47c8a4f4e38b989b7e59","2d16c77b45c64dbbbbf8e52d62ff9bcb","a9629921663f43f5a5b39a53f027ef87","9f92191d059941cfae4aacf05ea89cd8","3c61e61a4d374a60be1f15f11ef0f2ef","5f1b9c3905cf49d28a2dd883f0a4011b","5d37fe370d9e4191a78c8256f729c803","6481fe783bbe4a70ab0c76ec1971822d","564b929da4754dbabb334d87d823d2d2","0ab708ab80364879ad4d829f0853ac57","28b5ae24405b409396c092368812c851","872a2dff31174a3a952cc31077c141f1","25f7b7b244244570882320096b87292a","98a42e872c174f019747d5b9f545fa17","ea4318c4827346dc8498c38fad7a6d5e","62057380d92a448d91594a41a9528c31","99ffb7e38f3b4c5eb7b7f838949798d4","d2d0917d08494ca78335628baf93f6d4","b7abb7d34c8843eda6273218d6bf57e5","b2f01c70ba0f4854aa04c6e7ba58816e","ed9ee807957c4d57ac8430b5ecb46152","e860c08a26b64393b2e7d6dcb4bc5191","26c827ab64b2479094cfaaf8ddc75cc1","760341ce8bd04e9284e427a2411ff2bd","07182f008500487994fec75f7651f281","e29cca74a2844d82a573c743bcd87f54","1cafc04fbf7a4acfbe813978283c2e95"]},"executionInfo":{"status":"ok","timestamp":1770365769642,"user_tz":480,"elapsed":7280,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"0bdf432f-f70b-4c91-e6ed-d74c68a9047f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17e9f91e480d4a7d9d4ca36d324c961e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5cfaffca37874dcb805e65837f6f9fc1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d13195144fa149938b5d648f29bd7df5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf9fdbd988e0416aa54eda64f9280717"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4e1ec9c030c4e618bf6439a41f5e382"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"645188671f8b4f669e2dadea96d1e9db"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/353M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"663a54fc8fb3461dba40078b4745ca59"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/76 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f1b9c3905cf49d28a2dd883f0a4011b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["GPT2LMHeadModel LOAD REPORT from: distilgpt2\n","Key                                        | Status     |  | \n","-------------------------------------------+------------+--+-\n","transformer.h.{0, 1, 2, 3, 4, 5}.attn.bias | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"99ffb7e38f3b4c5eb7b7f838949798d4"}},"metadata":{}}],"source":["from transformers import AutoModelForCausalLM\n","\n","gpt2_tokenizer = AutoTokenizer.from_pretrained('gpt2')\n","\n","gpt2 = AutoModelForCausalLM.from_pretrained('distilgpt2')\n","gpt2.config.pad_token_id = gpt2.config.eos_token_id  # Prevents warning during decoding"],"id":"QfQEV8EKoD4P"},{"cell_type":"code","execution_count":null,"metadata":{"id":"G5wo61xmoD4Q","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770365803202,"user_tz":480,"elapsed":30381,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"afceea37-6fa4-4ba3-8568-57891de5e4f2"},"outputs":[{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["1) Once upon a time the question was answered, this is the answer to the question, as this would be a good idea if it were answered with a simple but informative, simple answer.\n","\n","One of the more powerful questions that we often hear from\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["2) Once upon a time we were already on a pretty bad track as we started to think of all the new things in the series, and I was so amazed by it. We hadn't been going all that far, but I did start to think a\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["3) Once upon a time of my own. But I was thinking, how could we go about that? How could we really build a place for me?‧ … Why would we need a lot of people to give me some space to work?\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["4) Once upon a time, you could do something special, or you could write something that took place before.”\n","\n","\n","That” was what made a really powerful piece of art.\n","It” meant something that could even be seen\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["5) Once upon a time of such great growth and demand that an unprecedented increase in the amount of food consumed, a growing number of people are being encouraged to buy a cup of coffee or even an espresso to take a nap, when they are not being fed\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["6) Once upon a time I found it more and more difficult for me to take over the management of the organization. It was a lot easier for me to leave, but after this experience, I found that it was a more difficult situation for me to take\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["7) Once upon a time a woman in a state of mourning was killed or killed or killed while riding a motorized motorcycle in the county of New Hampshire, or for carrying an adult firearm in a vehicle, it was the duty of those drivers to enforce those\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["8) Once upon a time in the early 20th century, as it was often called, some people took to Twitter to call themselves the “‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎‎\n"]},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"stream","name":"stdout","text":["9) Once upon a time of year, that's not a bad thing for a bird of prey, but if you look at the numbers that bird populations now have, you'd see that the biggest birds are in Alaska, Alaska, Alaska, and most of\n","10) Once upon a time of war, the Japanese government was able to achieve its objectives without any difficulty.\n","\n","The Japanese government in 1941 provided the first major military victory against the Japanese, and they had defeated the Japanese military with a huge victory in the\n"]}],"source":["prompt = \"Once upon a time\"\n","\n","tokenized_prompt = gpt2_tokenizer(prompt, return_tensors=\"pt\")\n","\n","for i in range(10):\n","    output = gpt2.generate(**tokenized_prompt,\n","                  max_length=50,\n","                  do_sample=True,\n","                  top_p=0.9)\n","\n","    print(f\"{i + 1}) {gpt2_tokenizer.batch_decode(output)[0]}\")"],"id":"G5wo61xmoD4Q"},{"cell_type":"markdown","metadata":{"id":"QLAHLU4q9HYQ"},"source":["## Appendix 1: Defining Custom Datasets\n","\n","There are a few ways to go about defining datasets, but I'm going to show an example using Pytorch Dataloaders. This example uses an encoder-decoder dataaset,the [E2E Dataset](https://arxiv.org/abs/1706.09254), which is maps structured information about restaurants to natural language descriptions.\n","\n","We use automatically converted Parquet files found at [tuetschek/e2e_nlg](https://huggingface.co/datasets/tuetschek/e2e_nlg/tree/refs%2Fconvert%2Fparquet) HuggingFace dataset. You can read about the process in this [HuggingFace document](https://huggingface.co/docs/dataset-viewer/en/parquet)."],"id":"QLAHLU4q9HYQ"},{"cell_type":"code","execution_count":null,"metadata":{"id":"MLqz11UioD4Q"},"outputs":[],"source":["# Option 1: Load into Hugging Face Datasets\n","\n","import pandas as pd\n","from datasets import Dataset\n","\n","df = pd.read_parquet(\"hf://datasets/tuetschek/e2e_nlg@refs%2Fconvert%2Fparquet/default/train/0000.parquet\")\n","custom_dataset = Dataset.from_pandas(df)"],"id":"MLqz11UioD4Q"},{"cell_type":"code","execution_count":null,"metadata":{"id":"lRauc5JBoD4R"},"outputs":[],"source":["import pandas as pd\n","from torch.utils.data import Dataset\n","\n","class E2EDataset(Dataset):\n","    \"\"\"Tokenize data when we call __getitem__\"\"\"\n","    def __init__(self, path, tokenizer):\n","        df = pd.read_parquet(path)\n","        self.data = df[[\"human_reference\", \"meaning_representation\"]].to_dict(orient=\"records\")\n","        self.tokenizer = tokenizer\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, i):\n","        inputs = self.tokenizer(self.data[i][\"human_reference\"])\n","        labels = self.tokenizer(self.data[i][\"meaning_representation\"])\n","        inputs[\"labels\"] = labels.input_ids\n","        return inputs"],"id":"lRauc5JBoD4R"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"eRu5mFIpoD4R"},"outputs":[],"source":["bart_tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')"],"id":"eRu5mFIpoD4R"},{"cell_type":"code","execution_count":null,"metadata":{"id":"I32zL1nEoD4S"},"outputs":[],"source":["dataset = E2EDataset(\"hf://datasets/tuetschek/e2e_nlg@refs%2Fconvert%2Fparquet/default/train/0000.parquet\", bart_tokenizer)"],"id":"I32zL1nEoD4S"},{"cell_type":"code","execution_count":null,"metadata":{"id":"I50Sh862oD4T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770367290591,"user_tz":480,"elapsed":7,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"f201f15d-12c5-49fa-fb09-54ef5b823636"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': tensor([[   0,  713,   16,    5,   78, 1296,    4,    2],\n","        [   0,  713,   16,    5,  200, 1296,    4,    2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1],\n","        [1, 1, 1, 1, 1, 1, 1, 1]]), 'labels': tensor([[    0, 41858,   112,     2],\n","        [    0, 41858,   132,     2]])}"]},"metadata":{},"execution_count":54}],"source":["bart_tokenizer(text=[\"This is the first test.\", \"This is the second test.\"], text_target=[\"Target 1\", \"Target 2\"], return_tensors=\"pt\", padding=True, truncation=True)"],"id":"I50Sh862oD4T"},{"cell_type":"code","execution_count":null,"metadata":{"id":"w8frTRD3oD4T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770367293098,"user_tz":480,"elapsed":10,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"da4594be-2135-4cf3-8294-5196dc98d80e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [0, 133, 48596, 8881, 583, 22450, 1614, 1069, 5183, 34, 10, 195, 999, 691, 4, 1437, 14614, 386, 23, 984, 541, 4, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [0, 13650, 10975, 133, 48596, 7479, 3529, 40118, 10975, 22478, 7479, 425, 43430, 10975, 4321, 87, 984, 541, 7479, 2111, 691, 10975, 245, 66, 9, 195, 7479, 583, 10975, 347, 2001, 1140, 1614, 1069, 5183, 742, 2]}"]},"metadata":{},"execution_count":55}],"source":["dataset[0]"],"id":"w8frTRD3oD4T"},{"cell_type":"markdown","metadata":{"id":"tHI3KuNZ-X8w"},"source":["## Appendix 2: Pipelines\n","\n","There are some standard NLP tasks like sentiment classification or question answering where there are already pre-trained (and fine-tuned!) models available through Hugging Face Transformer's [_Pipeline_](https://huggingface.co/docs/transformers/v4.16.2/en/main_classes/pipelines#transformers.pipeline) interface.\n","\n","For your projects, you likely won't be using it too much, but it's still worth knowing about!\n","\n","Here's an example with Sentiment Analysis:"],"id":"tHI3KuNZ-X8w"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"gOj5ODS0-X8x","colab":{"base_uri":"https://localhost:8080/","height":171,"referenced_widgets":["d36e5c6a8ba244ecaec588a17b802991","01b133a9595c4bc697a0b752f0c0e540","b34e24a671384bcdb658a1d938133d32","c0bc305070cb4250acd26730467665e9","616301eee9b244ee97629753277b1410","d16b289c98c8487087174bfec6eabb23","281fbfd4c26f412b87d726689f7fa56a","c76ed6515bc8455ea52fa988f7246989","83ef259c627f4237a9f8d6beb4de49a4","42fe989b19084135bdb1e0e0cb9abae4","d19c893d1664461c8e6662beb1923f4f"]},"executionInfo":{"status":"ok","timestamp":1770367302504,"user_tz":480,"elapsed":4064,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"aaad24bc-9d09-4e05-ea13-6ce58ba7a786"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/393 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d36e5c6a8ba244ecaec588a17b802991"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["RobertaForSequenceClassification LOAD REPORT from: siebert/sentiment-roberta-large-english\n","Key                             | Status     |  | \n","--------------------------------+------------+--+-\n","roberta.embeddings.position_ids | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]}],"source":["from transformers import pipeline\n","\n","sentiment_analysis = pipeline(\"sentiment-analysis\", model=\"siebert/sentiment-roberta-large-english\")"],"id":"gOj5ODS0-X8x"},{"cell_type":"markdown","metadata":{"id":"D5wZuMG2-X8y"},"source":["You can run the pipeline by just calling it on a string"],"id":"D5wZuMG2-X8y"},{"cell_type":"code","execution_count":null,"metadata":{"id":"GrygLkiQ-X8y","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770367304257,"user_tz":480,"elapsed":142,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"76d90203-75ee-456e-f966-735ae9366efa"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'label': 'POSITIVE', 'score': 0.998448371887207}]"]},"metadata":{},"execution_count":57}],"source":["sentiment_analysis(\"Hugging Face Transformers is really cool!\")"],"id":"GrygLkiQ-X8y"},{"cell_type":"markdown","metadata":{"id":"0e2E8qKH-X8z"},"source":["Or on a list of strings:"],"id":"0e2E8qKH-X8z"},{"cell_type":"code","execution_count":null,"metadata":{"id":"EpBvCpVM-X8z","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770367306258,"user_tz":480,"elapsed":41,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"bf373b04-c282-4980-dcd5-d32056c00d15"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'label': 'POSITIVE', 'score': 0.9988769888877869},\n"," {'label': 'NEGATIVE', 'score': 0.9994940757751465}]"]},"metadata":{},"execution_count":58}],"source":["sentiment_analysis([\"I didn't know if I would like Hákarl, but it turned out pretty good.\",\n","                    \"I didn't know if I would like Hákarl, and it was just as bad as I'd heard.\"])"],"id":"EpBvCpVM-X8z"},{"cell_type":"markdown","metadata":{"id":"Ptc0BViy-X80"},"source":["You can find more information on pipelines (including which ones are available) [here](https://huggingface.co/docs/transformers/main_classes/pipelines)"],"id":"Ptc0BViy-X80"},{"cell_type":"markdown","metadata":{"id":"3oRAmG_w-X9H"},"source":["## Appendix 4: Masked Language Modeling"],"id":"3oRAmG_w-X9H"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"ZXD2-Gsu-X9H","colab":{"base_uri":"https://localhost:8080/","height":383,"referenced_widgets":["edd5d8735f704e75923f281d0c82cec0","5a539f3501bd4dc295384d7cc6e98fa7","0543fdca25ec4ed892d0b560b7ec9999","2eed2711f6ce47848a53f720aa71e559","ee2f540ba4d24489a790b2c816140b55","baa546ac8cdc43eeb371f5a85faf9ead","11af293b08b34676a3c65a08f93eab67","ab753e638727494a8e4c0eea91b9858e","0ca95ce8a1d3441b94aa9999921aec6b","4d45a9c594f54f6399aa895c0dbbd0c8","cae0d1efb24e485fbc9c33524059c998","f77a3c1806b142e7a4a340e2279a0301","c6abd78f9544477bae083d70121fcc7e","b5790f5efbef48edad2f4e246f042967","f224c2ad5f0f44e7b4a712e8e83e4ec9","d9c0470611b24da189aca0029b0356da","19dbe32164f74c8b930f644886e055f1","34105e77b69d45c782c20e16afb78406","d4f11a70340c4484a7f5abc4285f4c69","6e345a1ff2e247ebbd3fe3fda833e1ef","18b8c12e6c9f46e0a2eb7b9d77eff912","e43bcecec3d1430ea568fe8e802f5c3c","5117b5269aa64f8c94de42b32f5b6e78","ec2e069527ac45cf9fe7ce265a916056","fc624e10ecdd4adca40b636dbc072c27","efde8debbd79459a9be1c3f08f5e5384","85ea37700674400db6c587f78cd7af0e","7d6c3f39ffe34dbfa8ce270ce042dc2e","7e7e947394be4ca5a2b8329dc91c94d4","7beecfa5628843668fa0647e2bc314e6","9f36d842a2684c88bda17c1cbc309518","c8da26c919ac45eca6983c90a24ff143","44e6e1e0f8c54af896f6a9c57bbf50a8","4efa21196bb44c81a35f0a68fb82bb74","e22309f83a394c3694040de5d1856621","42f70c238e12499f9bd2390bfe37eef6","249dc06fda0a43499b2f63a000ddc90a","f06c5fc7a8694bb08c588c4b622cec49","d06cd2b815514e4aa91085df2b917c07","955d0c5c175e4ee0a29b83d5d6efc4d9","aa03f954fd5b4741865fc7ea81173e46","cc78fb608b214ca99bff74ad96bd34eb","c55ed19e8aaf44d7a70e6e9088df43a6","610135dd46c54ab2b235e60519a4af47","2b2d3c215feb4478b6e9ac71789450cf","26a752261ae2459f8205cad64dfe4714","12426d0e53284688b35458721eaf2918","86a494c1574e4c09a05c40055c311e91","b9a3770adea7448796224519ca0ef500","a5030f3754bd43928a5ce1877541f249","f81d5e790922418a988ef558994e950c","7622bf5c83614c938e9870b2bd791c16","315d288fa69742679b3a9b8930c989ed","e58295862d8a43afa7979e0b9b32f389","36966212978d4ed8a176ac606d156d6f","bfe7f03529354f37be0e7c5c4f42296e","97c8f33e21134ebdbeb88f240a718457","71b51637cf5d4efc9f64bed69c9701b4","aa2b2e065d074c9b99ba96e1b9e9f3a5","01a973e23e1c4497bc70a0ed51b0fc29","653f384c940247c3b266c4ca9239cad1","4a9fedc13b0c435984738a7bfd4b77ee","8b60caa0666041d8a555cf3fc7780cba","39fa83449f494988975babd772009bd7","8f3e4917648741eb922e1a9f0388aa96","214126ce0efb4154b1b9c8e182af84c5"]},"executionInfo":{"status":"ok","timestamp":1770367318268,"user_tz":480,"elapsed":10399,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"6622ccc2-cf12-4727-d4a0-04345f975af8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"edd5d8735f704e75923f281d0c82cec0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f77a3c1806b142e7a4a340e2279a0301"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5117b5269aa64f8c94de42b32f5b6e78"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4efa21196bb44c81a35f0a68fb82bb74"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2b2d3c215feb4478b6e9ac71789450cf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/202 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bfe7f03529354f37be0e7c5c4f42296e"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["BertForMaskedLM LOAD REPORT from: bert-base-cased\n","Key                         | Status     |  | \n","----------------------------+------------+--+-\n","cls.seq_relationship.weight | UNEXPECTED |  | \n","cls.seq_relationship.bias   | UNEXPECTED |  | \n","bert.pooler.dense.weight    | UNEXPECTED |  | \n","bert.pooler.dense.bias      | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]}],"source":["from transformers import AutoModelForMaskedLM\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", fast=True)\n","bert = AutoModelForMaskedLM.from_pretrained(\"bert-base-cased\")"],"id":"ZXD2-Gsu-X9H"},{"cell_type":"code","execution_count":null,"metadata":{"id":"fvfHGKrq-X9I","colab":{"base_uri":"https://localhost:8080/","height":570,"referenced_widgets":["404a1cf3573e432192a25ec3122f08f9","23754bc6407d4c6f8e3f75c489487b81","55a82baf28ba4f58bd7576e11c755e82","d2f133ba4a054ca59628e8795134849f","55717314206b4246b2459e4c59813c31","8ce80009b26e46258229d19c50837719","bc75cebe56724803b3e209fe8b789269","d7a8c4c2c2114a7da08d233b0aeab6b1","1cabac824b024e54ba820091ce873e24","e4b3ee3a91c64e38861d9802c3cd3aaf","cff791dfa341424f8d364771bdcb28a9"]},"executionInfo":{"status":"ok","timestamp":1770367319793,"user_tz":480,"elapsed":1523,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"ceda057f-e981-48e4-add4-d72d5060296d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/202 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"404a1cf3573e432192a25ec3122f08f9"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["BertForMaskedLM LOAD REPORT from: bert-base-cased\n","Key                         | Status     |  | \n","----------------------------+------------+--+-\n","cls.seq_relationship.weight | UNEXPECTED |  | \n","cls.seq_relationship.bias   | UNEXPECTED |  | \n","bert.pooler.dense.weight    | UNEXPECTED |  | \n","bert.pooler.dense.bias      | UNEXPECTED |  | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'score': 0.35529038310050964,\n","  'token': 7215,\n","  'token_str': 'excited',\n","  'sequence': 'I am excited to learn about HuggingFace!'},\n"," {'score': 0.1562117487192154,\n","  'token': 1280,\n","  'token_str': 'going',\n","  'sequence': 'I am going to learn about HuggingFace!'},\n"," {'score': 0.07893485575914383,\n","  'token': 9582,\n","  'token_str': 'eager',\n","  'sequence': 'I am eager to learn about HuggingFace!'},\n"," {'score': 0.03559960052371025,\n","  'token': 1303,\n","  'token_str': 'here',\n","  'sequence': 'I am here to learn about HuggingFace!'},\n"," {'score': 0.03524090349674225,\n","  'token': 17261,\n","  'token_str': 'delighted',\n","  'sequence': 'I am delighted to learn about HuggingFace!'}]"]},"metadata":{},"execution_count":60}],"source":["prompt = \"I am [MASK] to learn about HuggingFace!\"\n","model = pipeline(\"fill-mask\", \"bert-base-cased\")\n","model(prompt)"],"id":"fvfHGKrq-X9I"},{"cell_type":"code","execution_count":null,"metadata":{"id":"0B2qek-v-X9I","scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1770367320100,"user_tz":480,"elapsed":306,"user":{"displayName":"Minsik Oh","userId":"12652243681094206057"}},"outputId":"e9035b5b-1031-4da0-c29e-4262de451365"},"outputs":[{"output_type":"stream","name":"stdout","text":["I am [MASK] to learn about HuggingFace!\n","  1) excited\t0.355\n","  2) going\t0.156\n","  3) eager\t0.079\n","  4) here\t0.036\n","  5) delighted\t0.035\n"]}],"source":["inputs = tokenizer(prompt, return_tensors=\"pt\")\n","mask_index = np.where(inputs['input_ids'] == tokenizer.mask_token_id)\n","outputs = bert(**inputs)\n","top_5_predictions = torch.softmax(outputs.logits[mask_index], dim=1).topk(5)\n","\n","print(prompt)\n","for i in range(5):\n","    prediction = tokenizer.decode(top_5_predictions.indices[0, i])\n","    prob = top_5_predictions.values[0, i]\n","    print(f\"  {i+1}) {prediction}\\t{prob:.3f}\")"],"id":"0B2qek-v-X9I"}],"metadata":{"colab":{"provenance":[{"file_id":"13r94i6Fh4oYf-eJRSi7S_y_cen5NYkBm","timestamp":1770360664205},{"file_id":"1pxc-ehTtnVM72-NViET_D2ZqOlpOi2LH","timestamp":1707202153994}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 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