# Copyright 2023 The Flax Authors.
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"""Attention core modules for Flax."""

import functools
from typing import (Any, Callable, Optional, Tuple)
from flax.linen.dtypes import promote_dtype

from flax.linen import initializers
from flax.linen.linear import default_kernel_init
from flax.linen.linear import DenseGeneral
from flax.linen.linear import DotGeneralT
from flax.linen.linear import PrecisionLike
from flax.linen.module import compact
from flax.linen.module import merge_param
from flax.linen.module import Module

import jax
from jax import lax
from jax import random
import jax.numpy as jnp

PRNGKey = Any
Shape = Tuple[int, ...]
Dtype = Any
Array = Any


def dot_product_attention_weights(query: Array,
                                  key: Array,
                                  bias: Optional[Array] = None,
                                  mask: Optional[Array] = None,
                                  broadcast_dropout: bool = True,
                                  dropout_rng: Optional[PRNGKey] = None,
                                  dropout_rate: float = 0.,
                                  deterministic: bool = False,
                                  dtype: Optional[Dtype] = None,
                                  precision: PrecisionLike = None):
  """Computes dot-product attention weights given query and key.

  Used by :func:`dot_product_attention`, which is what you'll most likely use.
  But if you want access to the attention weights for introspection, then
  you can directly call this function and call einsum yourself.

  Args:
    query: queries for calculating attention with shape of
      `[batch..., q_length, num_heads, qk_depth_per_head]`.
    key: keys for calculating attention with shape of
      `[batch..., kv_length, num_heads, qk_depth_per_head]`.
    bias: bias for the attention weights. This should be broadcastable to the
      shape `[batch..., num_heads, q_length, kv_length]`.
      This can be used for incorporating causal masks, padding masks,
      proximity bias, etc.
    mask: mask for the attention weights. This should be broadcastable to the
      shape `[batch..., num_heads, q_length, kv_length]`.
      This can be used for incorporating causal masks.
      Attention weights are masked out if their corresponding mask value
      is `False`.
    broadcast_dropout: bool: use a broadcasted dropout along batch dims.
    dropout_rng: JAX PRNGKey: to be used for dropout
    dropout_rate: dropout rate
    deterministic: bool, deterministic or not (to apply dropout)
    dtype: the dtype of the computation (default: infer from inputs and params)
    precision: numerical precision of the computation see `jax.lax.Precision`
      for details.

  Returns:
    Output of shape `[batch..., num_heads, q_length, kv_length]`.
  """
  query, key = promote_dtype(query, key, dtype=dtype)
  dtype = query.dtype

  assert query.ndim == key.ndim, 'q, k must have same rank.'
  assert query.shape[:-3] == key.shape[:-3], (
      'q, k batch dims must match.')
  assert query.shape[-2] == key.shape[-2], (
      'q, k num_heads must match.')
  assert query.shape[-1] == key.shape[-1], 'q, k depths must match.'

  # calculate attention matrix
  depth = query.shape[-1]
  query = query / jnp.sqrt(depth).astype(dtype)
  # attn weight shape is (batch..., num_heads, q_length, kv_length)
  attn_weights = jnp.einsum('...qhd,...khd->...hqk', query, key,
                            precision=precision)

  # apply attention bias: masking, dropout, proximity bias, etc.
  if bias is not None:
    attn_weights = attn_weights + bias
  # apply attention mask
  if mask is not None:
    big_neg = jnp.finfo(dtype).min
    attn_weights = jnp.where(mask, attn_weights, big_neg)

  # normalize the attention weights
  attn_weights = jax.nn.softmax(attn_weights).astype(dtype)

  # apply attention dropout
  if not deterministic and dropout_rate > 0.:
    keep_prob = 1.0 - dropout_rate
    if broadcast_dropout:
      # dropout is broadcast across the batch + head dimensions
      dropout_shape = tuple([1] * (key.ndim - 2)) + attn_weights.shape[-2:]
      keep = random.bernoulli(dropout_rng, keep_prob, dropout_shape) # type: ignore
    else:
      keep = random.bernoulli(dropout_rng, keep_prob, attn_weights.shape) # type: ignore
    multiplier = (keep.astype(dtype) /
                  jnp.asarray(keep_prob, dtype=dtype))
    attn_weights = attn_weights * multiplier

  return attn_weights


def dot_product_attention(query: Array,
                          key: Array,
                          value: Array,
                          bias: Optional[Array] = None,
                          mask: Optional[Array] = None,
                          broadcast_dropout: bool = True,
                          dropout_rng: Optional[PRNGKey] = None,
                          dropout_rate: float = 0.,
                          deterministic: bool = False,
                          dtype: Optional[Dtype] = None,
                          precision: PrecisionLike = None):
  """Computes dot-product attention given query, key, and value.

  This is the core function for applying attention based on
  https://arxiv.org/abs/1706.03762. It calculates the attention weights given
  query and key and combines the values using the attention weights.

  Note: query, key, value needn't have any batch dimensions.

  Args:
    query: queries for calculating attention with shape of
      `[batch..., q_length, num_heads, qk_depth_per_head]`.
    key: keys for calculating attention with shape of
      `[batch..., kv_length, num_heads, qk_depth_per_head]`.
    value: values to be used in attention with shape of
      `[batch..., kv_length, num_heads, v_depth_per_head]`.
    bias: bias for the attention weights. This should be broadcastable to the
      shape `[batch..., num_heads, q_length, kv_length]`.
      This can be used for incorporating causal masks, padding masks,
      proximity bias, etc.
    mask: mask for the attention weights. This should be broadcastable to the
      shape `[batch..., num_heads, q_length, kv_length]`.
      This can be used for incorporating causal masks.
      Attention weights are masked out if their corresponding mask value
      is `False`.
    broadcast_dropout: bool: use a broadcasted dropout along batch dims.
    dropout_rng: JAX PRNGKey: to be used for dropout
    dropout_rate: dropout rate
    deterministic: bool, deterministic or not (to apply dropout)
    dtype: the dtype of the computation (default: infer from inputs)
    precision: numerical precision of the computation see `jax.lax.Precision`
      for details.

  Returns:
    Output of shape `[batch..., q_length, num_heads, v_depth_per_head]`.
  """
  query, key, value = promote_dtype(query, key, value, dtype=dtype)
  dtype = query.dtype
  assert key.ndim == query.ndim == value.ndim, 'q, k, v must have same rank.'
  assert query.shape[:-3] == key.shape[:-3] == value.shape[:-3], (
      'q, k, v batch dims must match.')
  assert query.shape[-2] == key.shape[-2] == value.shape[-2], (
      'q, k, v num_heads must match.')
  assert key.shape[-3] == value.shape[-3], 'k, v lengths must match.'

  # compute attention weights
  attn_weights = dot_product_attention_weights(
      query, key, bias, mask, broadcast_dropout, dropout_rng, dropout_rate,
      deterministic, dtype, precision)

  # return weighted sum over values for each query position
  return jnp.einsum('...hqk,...khd->...qhd', attn_weights, value,
                    precision=precision)


class MultiHeadDotProductAttention(Module):
  """Multi-head dot-product attention.

    Attributes:
      num_heads: number of attention heads. Features (i.e. inputs_q.shape[-1])
        should be divisible by the number of heads.
      dtype: the dtype of the computation
        (default: infer from inputs and params)
      param_dtype: the dtype passed to parameter initializers (default: float32)
      qkv_features: dimension of the key, query, and value.
      out_features: dimension of the last projection
      broadcast_dropout: bool: use a broadcasted dropout along batch dims.
      dropout_rate: dropout rate
      deterministic: if false, the attention weight is masked randomly
        using dropout, whereas if true, the attention weights
        are deterministic.
      precision: numerical precision of the computation see `jax.lax.Precision`
        for details.
      kernel_init: initializer for the kernel of the Dense layers.
      bias_init: initializer for the bias of the Dense layers.
      use_bias: bool: whether pointwise QKVO dense transforms use bias.
      attention_fn: dot_product_attention or compatible function. Accepts
        query, key, value, and returns output of shape
        `[bs, dim1, dim2, ..., dimN,, num_heads, value_channels]``
      decode: whether to prepare and use an autoregressive cache.
  """
  num_heads: int
  dtype: Optional[Dtype] = None
  param_dtype: Dtype = jnp.float32
  qkv_features: Optional[int] = None
  out_features: Optional[int] = None
  broadcast_dropout: bool = True
  dropout_rate: float = 0.
  deterministic: Optional[bool] = None
  precision: PrecisionLike = None
  kernel_init: Callable[[PRNGKey, Shape, Dtype], Array] = default_kernel_init
  bias_init: Callable[[PRNGKey, Shape, Dtype], Array] = initializers.zeros_init()
  use_bias: bool = True
  attention_fn: Callable[..., Array] = dot_product_attention
  decode: bool = False
  qkv_dot_general: DotGeneralT = lax.dot_general
  out_dot_general: DotGeneralT = lax.dot_general

  @compact
  def __call__(self,
               inputs_q: Array,
               inputs_kv: Array,
               mask: Optional[Array] = None,
               deterministic: Optional[bool] = None):
    """Applies multi-head dot product attention on the input data.

    Projects the inputs into multi-headed query, key, and value vectors,
    applies dot-product attention and project the results to an output vector.

    Args:
      inputs_q: input queries of shape
        `[batch_sizes..., length, features]`.
      inputs_kv: key/values of shape
        `[batch_sizes..., length, features]`.
      mask: attention mask of shape
        `[batch_sizes..., num_heads, query_length, key/value_length]`.
        Attention weights are masked out if their corresponding mask value
        is `False`.
      deterministic: if false, the attention weight is masked randomly
        using dropout, whereas if true, the attention weights
        are deterministic.

    Returns:
      output of shape `[batch_sizes..., length, features]`.
    """
    features = self.out_features or inputs_q.shape[-1]
    qkv_features = self.qkv_features or inputs_q.shape[-1]
    assert qkv_features % self.num_heads == 0, (
        f'Memory dimension ({qkv_features}) must be divisible by number of'
        f' heads ({self.num_heads}).'
    )
    head_dim = qkv_features // self.num_heads

    dense = functools.partial(
        DenseGeneral,
        axis=-1,
        dtype=self.dtype,
        param_dtype=self.param_dtype,
        features=(self.num_heads, head_dim),
        kernel_init=self.kernel_init,
        bias_init=self.bias_init,
        use_bias=self.use_bias,
        precision=self.precision,
        dot_general=self.qkv_dot_general,
    )
    # project inputs_q to multi-headed q/k/v
    # dimensions are then [batch..., length, n_heads, n_features_per_head]
    query, key, value = (dense(name='query')(inputs_q),
                         dense(name='key')(inputs_kv),
                         dense(name='value')(inputs_kv))

    # During fast autoregressive decoding, we feed one position at a time,
    # and cache the keys and values step by step.
    if self.decode:
      # detect if we're initializing by absence of existing cache data.
      is_initialized = self.has_variable('cache', 'cached_key')
      cached_key = self.variable('cache', 'cached_key',
                                 jnp.zeros, key.shape, key.dtype)
      cached_value = self.variable('cache', 'cached_value',
                                   jnp.zeros, value.shape, value.dtype)
      cache_index = self.variable('cache', 'cache_index',
                                  lambda: jnp.array(0, dtype=jnp.int32))
      if is_initialized:
        *batch_dims, max_length, num_heads, depth_per_head = (
            cached_key.value.shape)
        # shape check of cached keys against query input
        expected_shape = tuple(batch_dims) + (1, num_heads, depth_per_head)
        if expected_shape != query.shape:
          raise ValueError('Autoregressive cache shape error, '
                           'expected query shape %s instead got %s.' %
                           (expected_shape, query.shape))
        # update key, value caches with our new 1d spatial slices
        cur_index = cache_index.value
        indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
        key = lax.dynamic_update_slice(cached_key.value, key, indices)
        value = lax.dynamic_update_slice(cached_value.value, value, indices)
        cached_key.value = key
        cached_value.value = value
        cache_index.value = cache_index.value + 1
        # causal mask for cached decoder self-attention:
        # our single query position should only attend to those key
        # positions that have already been generated and cached,
        # not the remaining zero elements.
        mask = combine_masks(
            mask,
            jnp.broadcast_to(jnp.arange(max_length) <= cur_index,
                             tuple(batch_dims) + (1, 1, max_length)))

    dropout_rng = None
    if self.dropout_rate > 0.:  # Require `deterministic` only if using dropout.
      m_deterministic = merge_param('deterministic', self.deterministic,
                                    deterministic)
      if not m_deterministic:
        dropout_rng = self.make_rng('dropout')
    else:
      m_deterministic = True

    # apply attention
    x = self.attention_fn(
        query,
        key,
        value,
        mask=mask,
        dropout_rng=dropout_rng,
        dropout_rate=self.dropout_rate,
        broadcast_dropout=self.broadcast_dropout,
        deterministic=m_deterministic,
        dtype=self.dtype,
        precision=self.precision)  # pytype: disable=wrong-keyword-args
    # back to the original inputs dimensions
    out = DenseGeneral(
        features=features,
        axis=(-2, -1),
        kernel_init=self.kernel_init,
        bias_init=self.bias_init,
        use_bias=self.use_bias,
        dtype=self.dtype,
        param_dtype=self.param_dtype,
        precision=self.precision,
        dot_general=self.out_dot_general,
        name='out', # type: ignore[call-arg]
    )(x)
    return out


class SelfAttention(MultiHeadDotProductAttention):
  """Self-attention special case of multi-head dot-product attention."""

  @compact
  def __call__(self, inputs_q: Array, mask: Optional[Array] = None, # type: ignore
               deterministic: Optional[bool] = None):
    """Applies multi-head dot product self-attention on the input data.

    Projects the inputs into multi-headed query, key, and value vectors,
    applies dot-product attention and project the results to an output vector.

    Args:
      inputs_q: input queries of shape
        `[batch_sizes..., length, features]`.
      mask: attention mask of shape
        `[batch_sizes..., num_heads, query_length, key/value_length]`.
        Attention weights are masked out if their corresponding mask value
        is `False`.
      deterministic: if false, the attention weight is masked randomly
        using dropout, whereas if true, the attention weights
        are deterministic.

    Returns:
      output of shape `[batch_sizes..., length, features]`.
    """
    return super().__call__(inputs_q, inputs_q, mask,
                            deterministic=deterministic)


# mask-making utility functions


def make_attention_mask(query_input: Array,
                        key_input: Array,
                        pairwise_fn: Callable[..., Any] = jnp.multiply,
                        extra_batch_dims: int = 0,
                        dtype: Dtype = jnp.float32):
  """Mask-making helper for attention weights.

  In case of 1d inputs (i.e., `[batch..., len_q]`, `[batch..., len_kv]`, the
  attention weights will be `[batch..., heads, len_q, len_kv]` and this
  function will produce `[batch..., 1, len_q, len_kv]`.

  Args:
    query_input: a batched, flat input of query_length size
    key_input: a batched, flat input of key_length size
    pairwise_fn: broadcasting elementwise comparison function
    extra_batch_dims: number of extra batch dims to add singleton
      axes for, none by default
    dtype: mask return dtype

  Returns:
    A `[batch..., 1, len_q, len_kv]` shaped mask for 1d attention.
  """
  mask = pairwise_fn(jnp.expand_dims(query_input, axis=-1),
                     jnp.expand_dims(key_input, axis=-2))
  mask = jnp.expand_dims(mask, axis=-3)
  mask = jnp.expand_dims(mask, axis=tuple(range(extra_batch_dims)))
  return mask.astype(dtype)


def make_causal_mask(x: Array,
                     extra_batch_dims: int = 0,
                     dtype: Dtype = jnp.float32) -> Array:
  """Make a causal mask for self-attention.

  In case of 1d inputs (i.e., `[batch..., len]`, the self-attention weights
  will be `[batch..., heads, len, len]` and this function will produce a
  causal mask of shape `[batch..., 1, len, len]`.

  Args:
    x: input array of shape `[batch..., len]`
    extra_batch_dims: number of batch dims to add singleton axes for,
      none by default
    dtype: mask return dtype

  Returns:
    A `[batch..., 1, len, len]` shaped causal mask for 1d attention.
  """
  idxs = jnp.broadcast_to(jnp.arange(x.shape[-1], dtype=jnp.int32), x.shape)
  return make_attention_mask(idxs, idxs, jnp.greater_equal,
                             extra_batch_dims=extra_batch_dims, dtype=dtype)


def combine_masks(*masks: Optional[Array],
                  dtype: Dtype = jnp.float32) -> Array:
  """Combine attention masks.

  Args:
    *masks: set of attention mask arguments to combine, some can be None.
    dtype: dtype for the returned mask.

  Returns:
    Combined mask, reduced by logical and, returns None if no masks given.
  """
  masks_list = [m for m in masks if m is not None]
  if not masks_list:
    return None
  assert all(map(lambda x: x.ndim == masks_list[0].ndim, masks_list)), (
      f'masks must have same rank: {tuple(map(lambda x: x.ndim, masks_list))}')
  mask, *other_masks = masks_list
  for other_mask in other_masks:
    mask = jnp.logical_and(mask, other_mask)
  return mask.astype(dtype)
