# Copyright 2022 The JAX Authors.
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"""Bi-LSTM text classification model from Flax example SST2, see:

https://github.com/google/flax/tree/main/examples/sst2
"""

from __future__ import annotations

from collections.abc import Callable
import functools
from typing import Any

from flax import linen as nn
import jax
from jax import numpy as jnp
from jax import Array


def sequence_mask(lengths: Array, max_length: int) -> Array:
  """Computes a boolean mask over sequence positions for each given length.

  Example:
  ```
  sequence_mask([1, 2], 3)
  [[True, False, False],
   [True, True, False]]
  ```

  Args:
    lengths: The length of each sequence. <int>[batch_size]
    max_length: The width of the boolean mask. Must be >= max(lengths).

  Returns:
    A mask with shape: <bool>[batch_size, max_length] indicating which
    positions are valid for each sequence.
  """
  return jnp.arange(max_length)[None] < lengths[:, None]


@jax.vmap
def flip_sequences(inputs: Array, lengths: Array) -> Array:
  """Flips a sequence of inputs along the time dimension.

  This function can be used to prepare inputs for the reverse direction of a
  bidirectional LSTM. It solves the issue that, when naively flipping multiple
  padded sequences stored in a matrix, the first elements would be padding
  values for those sequences that were padded. This function keeps the padding
  at the end, while flipping the rest of the elements.

  Example:
  ```python
  inputs = [[1, 0, 0],
            [2, 3, 0]
            [4, 5, 6]]
  lengths = [1, 2, 3]
  flip_sequences(inputs, lengths) = [[1, 0, 0],
                                     [3, 2, 0],
                                     [6, 5, 4]]
  ```

  Args:
    inputs: An array of input IDs <int>[batch_size, seq_length].
    lengths: The length of each sequence <int>[batch_size].

  Returns:
    An ndarray with the flipped inputs.
  """
  # Note: since this function is vmapped, the code below is effectively for
  # a single example.
  max_length = inputs.shape[0]
  return jnp.flip(jnp.roll(inputs, max_length - lengths, axis=0), axis=0)


class WordDropout(nn.Module):
  """Applies word dropout to a batch of input IDs.

  This is basically the same as `nn.Dropout`, but allows specifying the
  value of dropped out items.
  """
  dropout_rate: float
  unk_idx: int
  deterministic: bool | None = None

  @nn.compact
  def __call__(self, inputs: Array, deterministic: bool | None = None):
    deterministic = nn.module.merge_param(
        'deterministic', self.deterministic, deterministic)
    if deterministic or self.dropout_rate == 0.:
      return inputs
    rng = self.make_rng('dropout')
    mask = jax.random.bernoulli(rng, p=self.dropout_rate, shape=inputs.shape)
    return jnp.where(mask, jnp.array([self.unk_idx]), inputs)


class Embedder(nn.Module):
  """Embeds batches of token IDs into feature space.

  Attributes:
    vocab_size: The size of the vocabulary (i.e., the number of embeddings).
    embedding_size: The dimensionality of the embeddings.
    embedding_init: The initializer used to initialize the embeddings.
    frozen: Freezes the embeddings table, keeping it fixed at initial values.
    dropout_rate: Percentage of units to drop after embedding the inputs.
    word_dropout_rate: Percentage of input words to replace with unk_idx.
    unk_idx: The index (integer) to use to replace inputs for word dropout.
  """
  vocab_size: int
  embedding_size: int
  embedding_init: Callable[...,
                           Array] = nn.initializers.normal(stddev=0.1)
  frozen: bool = False
  dropout_rate: float = 0.
  word_dropout_rate: float = 0.
  unk_idx: int | None = None
  deterministic: bool | None = None
  dtype: jnp.dtype = jnp.dtype('float32')

  def setup(self):
    self.embedding = self.param(
        'embedding',
        self.embedding_init,
        (self.vocab_size,
         self.embedding_size),
        self.dtype)
    self.dropout_layer = nn.Dropout(rate=self.dropout_rate)
    self.word_dropout_layer = WordDropout(
        dropout_rate=self.word_dropout_rate,
        unk_idx=self.unk_idx)

  def __call__(self, inputs: Array,
               deterministic: bool | None = None) -> Array:
    """Embeds the input sequences and applies word dropout and dropout.

    Args:
      inputs: Batch of input token ID sequences <int64>[batch_size, seq_length].
      deterministic: Disables dropout when set to True.

    Returns:
      The embedded inputs, shape: <float32>[batch_size, seq_length,
      embedding_size].
    """
    deterministic = nn.module.merge_param(
        'deterministic', self.deterministic, deterministic)
    inputs = self.word_dropout_layer(inputs, deterministic=deterministic)
    embedded_inputs = self.embedding[inputs]

    # Keep the embeddings fixed at initial (e.g. pretrained) values.
    if self.frozen:
      embedded_inputs = jax.lax.stop_gradient(embedded_inputs)

    return self.dropout_layer(embedded_inputs, deterministic=deterministic)


class SimpleLSTM(nn.Module):
  """A simple unidirectional LSTM."""

  @functools.partial(
      nn.transforms.scan,
      variable_broadcast='params',
      in_axes=1, out_axes=1,
      split_rngs={'params': False})
  @nn.compact
  def __call__(self, carry, x):
    return nn.OptimizedLSTMCell(features=carry[0].shape[-1])(carry, x)

  @staticmethod
  def initialize_carry(batch_dims, hidden_size):
    # Use fixed random key since default state init fn is just zeros.
    return nn.OptimizedLSTMCell(hidden_size, parent=None).initialize_carry(
        jax.random.PRNGKey(0), (batch_dims, 1)
    )


class SimpleBiLSTM(nn.Module):
  """A simple bi-directional LSTM."""
  hidden_size: int

  def setup(self):
    self.forward_lstm = SimpleLSTM()
    self.backward_lstm = SimpleLSTM()

  def __call__(self, embedded_inputs, lengths):
    batch_size = embedded_inputs.shape[0]

    # Forward LSTM.
    initial_state = SimpleLSTM.initialize_carry((batch_size,), self.hidden_size)
    _, forward_outputs = self.forward_lstm(initial_state, embedded_inputs)

    # Backward LSTM.
    reversed_inputs = flip_sequences(embedded_inputs, lengths)
    initial_state = SimpleLSTM.initialize_carry((batch_size,), self.hidden_size)
    _, backward_outputs = self.backward_lstm(initial_state, reversed_inputs)
    backward_outputs = flip_sequences(backward_outputs, lengths)

    # Concatenate the forward and backward representations.
    outputs = jnp.concatenate([forward_outputs, backward_outputs], -1)
    return outputs


class MLP(nn.Module):
  """A simple Multilayer perceptron with 1 hidden layer.

  Attributes:
    hidden_size: The size of the hidden layer.
    output_size: The size of the output.
    activation: The activation function to apply to the hidden layer.
    dropout_rate: The dropout rate applied to the hidden layer.
    output_bias: If False, do not use a bias term in the last layer.
    deterministic: Disables dropout if set to True.
  """
  hidden_size: int
  output_size: int
  activation: Callable[..., Any] = nn.tanh
  dropout_rate: float = 0.0
  output_bias: bool = False
  deterministic: bool | None = None

  def setup(self):
    self.intermediate_layer = nn.Dense(self.hidden_size)
    self.output_layer = nn.Dense(self.output_size, use_bias=self.output_bias)
    self.dropout_layer = nn.Dropout(rate=self.dropout_rate)

  def __call__(self, inputs: Array, deterministic: bool | None = None):
    """Applies the MLP to the last dimension of the inputs.

    Args:
      inputs: <float32>[batch_size, ..., input_features].
      deterministic: Disables dropout when set to True.

    Returns:
      The MLP output <float32>[batch_size, ..., output_size]
    """
    deterministic = nn.module.merge_param(
        'deterministic', self.deterministic, deterministic)
    hidden = self.intermediate_layer(inputs)
    hidden = self.activation(hidden)
    hidden = self.dropout_layer(hidden, deterministic=deterministic)
    output = self.output_layer(hidden)
    return output


class KeysOnlyMlpAttention(nn.Module):
  """Computes MLP-based attention scores based on keys alone, without a query.

  Attention scores are computed by feeding the keys through an MLP. This
  results in a single scalar per key, and for each sequence the attention
  scores are normalized using a softmax so that they sum to 1. Invalid key
  positions are ignored as indicated by the mask. This is also called
  "Bahdanau attention" and was originally proposed in:
  ```
  Bahdanau et al., 2015. Neural Machine Translation by Jointly Learning to
  Align and Translate. ICLR. https://arxiv.org/abs/1409.0473
  ```

  Attributes:
    hidden_size: The hidden size of the MLP that computes the attention score.
  """
  hidden_size: int

  @nn.compact
  def __call__(self, keys: Array, mask: Array) -> Array:
    """Applies model  to the input keys and mask.

    Args:
      keys: The inputs for which to compute an attention score. Shape:
        <float32>[batch_size, seq_length, embeddings_size].
      mask: A mask that determinines which values in `keys` are valid. Only
        values for which the mask is True will get non-zero attention scores.
        <bool>[batch_size, seq_length].

    Returns:
      The normalized attention scores. <float32>[batch_size, seq_length].
    """
    hidden = nn.Dense(self.hidden_size, name='keys', use_bias=False)(keys)
    energy = nn.tanh(hidden)
    scores = nn.Dense(1, name='energy', use_bias=False)(energy)
    scores = scores.squeeze(-1)  # New shape: <float32>[batch_size, seq_len].
    scores = jnp.where(mask, scores, -jnp.inf)  # Using exp(-inf) = 0 below.
    scores = nn.softmax(scores, axis=-1)

    # Captures the scores if 'intermediates' is mutable, otherwise does nothing.
    self.sow('intermediates', 'attention', scores)

    return scores


class AttentionClassifier(nn.Module):
  """A classifier that uses attention to summarize the inputs.

  Attributes:
    hidden_size: The hidden size of the MLP classifier.
    output_size: The number of output classes for the classifier.
    dropout_rate: The dropout rate applied over the encoded_inputs, the summary
      of the inputs, and inside the MLP. Applied when `deterministic` is False.
    deterministic: Disables dropout if True.
  """
  hidden_size: int
  output_size: int
  dropout_rate: float = 0.
  deterministic: bool | None = None

  def setup(self):
    self.dropout_layer = nn.Dropout(rate=self.dropout_rate)
    self.keys_only_mlp_attention = KeysOnlyMlpAttention(
        hidden_size=self.hidden_size)
    self.mlp = MLP(
        hidden_size=self.hidden_size,
        output_size=self.output_size,
        output_bias=False,
        dropout_rate=self.dropout_rate)

  def __call__(self, encoded_inputs: Array, lengths: Array,
               deterministic: bool | None = None) -> Array:
    """Applies model to the encoded inputs.

    Args:
      encoded_inputs: The inputs (e.g., sentences) that have already been
        encoded by some encoder, e.g., an LSTM. <float32>[batch_size,
        seq_length, encoded_inputs_size].
      lengths: The lengths of the inputs. <int64>[batch_size].
      deterministic: Disables dropout when set to True.

    Returns:
      An array of logits <float32>[batch_size, output_size].
    """
    deterministic = nn.module.merge_param(
        'deterministic', self.deterministic, deterministic)
    encoded_inputs = self.dropout_layer(
        encoded_inputs, deterministic=deterministic)

    # Compute attention. attention.shape: <float32>[batch_size, seq_len].
    mask = sequence_mask(lengths, encoded_inputs.shape[1])
    attention = self.keys_only_mlp_attention(encoded_inputs, mask)

    # Summarize the inputs by taking their weighted sum using attention scores.
    context = jnp.expand_dims(attention, 1) @ encoded_inputs
    context = context.squeeze(1)  # <float32>[batch_size, encoded_inputs_size]
    context = self.dropout_layer(context, deterministic=deterministic)

    # Make the final prediction from the context vector (the summarized inputs).
    logits = self.mlp(context, deterministic=deterministic)
    return logits


class TextClassifier(nn.Module):
  """A Text Classification model."""

  embedding_size: int
  hidden_size: int
  vocab_size: int
  output_size: int

  dropout_rate: float
  word_dropout_rate: float
  unk_idx: int = 1
  deterministic: bool | None = None

  def setup(self):
    self.embedder = Embedder(
        vocab_size=self.vocab_size,
        embedding_size=self.embedding_size,
        dropout_rate=self.dropout_rate,
        word_dropout_rate=self.word_dropout_rate,
        unk_idx=self.unk_idx)
    self.encoder = SimpleBiLSTM(hidden_size=self.hidden_size)
    self.classifier = AttentionClassifier(
        hidden_size=self.hidden_size,
        output_size=self.output_size,
        dropout_rate=self.dropout_rate)

  def embed_token_ids(self, token_ids: Array,
                      deterministic: bool | None = None) -> Array:
    deterministic = nn.module.merge_param(
        'deterministic', self.deterministic, deterministic)
    return self.embedder(token_ids, deterministic=deterministic)

  def logits_from_embedded_inputs(
      self, embedded_inputs: Array, lengths: Array,
      deterministic: bool | None = None) -> Array:
    deterministic = nn.module.merge_param(
        'deterministic', self.deterministic, deterministic)
    encoded_inputs = self.encoder(embedded_inputs, lengths)
    return self.classifier(
        encoded_inputs, lengths, deterministic=deterministic)

  def __call__(self, token_ids: Array, lengths: Array,
               deterministic: bool | None = None) -> Array:
    """Embeds the token IDs, encodes them, and classifies with attention."""
    embedded_inputs = self.embed_token_ids(
        token_ids, deterministic=deterministic)
    logits = self.logits_from_embedded_inputs(
        embedded_inputs, lengths, deterministic=deterministic)
    return logits