import collections
import math
import string

import numpy as np

from keras.src import backend
from keras.src import constraints
from keras.src import initializers
from keras.src import ops
from keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.activations.softmax import Softmax
from keras.src.layers.core.einsum_dense import EinsumDense
from keras.src.layers.layer import Layer
from keras.src.layers.regularization.dropout import Dropout


@keras_export("keras.layers.MultiHeadAttention")
class MultiHeadAttention(Layer):
    """MultiHeadAttention layer.

    This is an implementation of multi-headed attention as described in the
    paper "Attention is all you Need"
    [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762).
    If `query`, `key,` `value` are the same, then
    this is self-attention. Each timestep in `query` attends to the
    corresponding sequence in `key`, and returns a fixed-width vector.

    This layer first projects `query`, `key` and `value`. These are
    (effectively) a list of tensors of length `num_attention_heads`, where the
    corresponding shapes are `(batch_size, <query dimensions>, key_dim)`,
    `(batch_size, <key/value dimensions>, key_dim)`,
    `(batch_size, <key/value dimensions>, value_dim)`.

    Then, the query and key tensors are dot-producted and scaled. These are
    softmaxed to obtain attention probabilities. The value tensors are then
    interpolated by these probabilities, then concatenated back to a single
    tensor.

    Finally, the result tensor with the last dimension as `value_dim` can take
    a linear projection and return.

    Args:
        num_heads: Number of attention heads.
        key_dim: Size of each attention head for query and key.
        value_dim: Size of each attention head for value.
        dropout: Dropout probability.
        use_bias: Boolean, whether the dense layers use bias vectors/matrices.
        output_shape: The expected shape of an output tensor, besides the batch
            and sequence dims. If not specified, projects back to the query
            feature dim (the query input's last dimension).
        attention_axes: axes over which the attention is applied. `None` means
            attention over all axes, but batch, heads, and features.
        kernel_initializer: Initializer for dense layer kernels.
        bias_initializer: Initializer for dense layer biases.
        kernel_regularizer: Regularizer for dense layer kernels.
        bias_regularizer: Regularizer for dense layer biases.
        activity_regularizer: Regularizer for dense layer activity.
        kernel_constraint: Constraint for dense layer kernels.
        bias_constraint: Constraint for dense layer kernels.
        seed: Optional integer to seed the dropout layer.

    Call arguments:
        query: Query tensor of shape `(B, T, dim)`, where `B` is the batch size,
            `T` is the target sequence length, and dim is the feature dimension.
        value: Value tensor of shape `(B, S, dim)`, where `B` is the batch size,
            `S` is the source sequence length, and dim is the feature dimension.
        key: Optional key tensor of shape `(B, S, dim)`. If not given, will
            use `value` for both `key` and `value`, which is the most common
            case.
        attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
            attention to certain positions. The boolean mask specifies which
            query elements can attend to which key elements, 1 indicates
            attention and 0 indicates no attention. Broadcasting can happen for
            the missing batch dimensions and the head dimension.
        return_attention_scores: A boolean to indicate whether the output should
            be `(attention_output, attention_scores)` if `True`, or
            `attention_output` if `False`. Defaults to `False`.
        training: Python boolean indicating whether the layer should behave in
            training mode (adding dropout) or in inference mode (no dropout).
            Will go with either using the training mode of the parent
            layer/model, or `False` (inference) if there is no parent layer.
        use_causal_mask: A boolean to indicate whether to apply a causal mask to
            prevent tokens from attending to future tokens (e.g., used in a
            decoder Transformer).

    Returns:
        attention_output: The result of the computation, of shape `(B, T, E)`,
            where `T` is for target sequence shapes and `E` is the query input
            last dimension if `output_shape` is `None`. Otherwise, the
            multi-head outputs are projected to the shape specified by
            `output_shape`.
        attention_scores: (Optional) multi-head attention coefficients over
            attention axes.
    """

    def __init__(
        self,
        num_heads,
        key_dim,
        value_dim=None,
        dropout=0.0,
        use_bias=True,
        output_shape=None,
        attention_axes=None,
        kernel_initializer="glorot_uniform",
        bias_initializer="zeros",
        kernel_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        kernel_constraint=None,
        bias_constraint=None,
        seed=None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.supports_masking = True
        self._num_heads = num_heads
        self._key_dim = key_dim
        # Cache 1.0 / math.sqrt(self._key_dim).
        self._inverse_sqrt_key_dim = None
        self._value_dim = value_dim if value_dim else key_dim
        self._dropout = dropout
        self._use_bias = use_bias
        self._output_shape = output_shape
        self._kernel_initializer = initializers.get(kernel_initializer)
        self._bias_initializer = initializers.get(bias_initializer)
        self._kernel_regularizer = regularizers.get(kernel_regularizer)
        self._bias_regularizer = regularizers.get(bias_regularizer)
        self._activity_regularizer = regularizers.get(activity_regularizer)
        self._kernel_constraint = constraints.get(kernel_constraint)
        self._bias_constraint = constraints.get(bias_constraint)
        if isinstance(attention_axes, int):
            attention_axes = (attention_axes,)
        elif attention_axes and not isinstance(attention_axes, (list, tuple)):
            raise ValueError(
                "`attention_axes` must be an int, list, or tuple."
                f"Received: attention_axes={attention_axes}"
            )
        self._attention_axes = attention_axes
        self.seed = seed

    @property
    def num_heads(self):
        return self._num_heads

    @property
    def key_dim(self):
        return self._key_dim

    @property
    def value_dim(self):
        return self._value_dim

    @property
    def dropout(self):
        return self._dropout

    @property
    def use_bias(self):
        return self._use_bias

    @property
    def output_shape(self):
        return self._output_shape

    @property
    def attention_axes(self):
        return self._attention_axes

    def get_config(self):
        base_config = super().get_config()
        config = {
            "num_heads": self._num_heads,
            "key_dim": self._key_dim,
            "value_dim": self._value_dim,
            "dropout": self._dropout,
            "use_bias": self._use_bias,
            "output_shape": self._output_shape,
            "attention_axes": self._attention_axes,
            "kernel_initializer": initializers.serialize(
                self._kernel_initializer
            ),
            "bias_initializer": initializers.serialize(self._bias_initializer),
            "kernel_regularizer": regularizers.serialize(
                self._kernel_regularizer
            ),
            "bias_regularizer": regularizers.serialize(self._bias_regularizer),
            "activity_regularizer": regularizers.serialize(
                self._activity_regularizer
            ),
            "kernel_constraint": constraints.serialize(self._kernel_constraint),
            "bias_constraint": constraints.serialize(self._bias_constraint),
            "seed": self.seed,
        }
        return {**base_config, **config}

    def build(
        self,
        query_shape,
        value_shape,
        key_shape=None,
    ):
        """Builds layers and variables.

        Args:
            query_shape: Shape of the `query` tensor.
            value_shape: Shape of the `value` tensor.
            key: Optional shape of the `key` tensor.
        """
        key_shape = value_shape if key_shape is None else key_shape
        query_rank = len(query_shape)
        value_rank = len(value_shape)
        key_rank = len(key_shape)
        einsum_equation, bias_axes, output_rank = _build_proj_equation(
            query_rank - 1, bound_dims=1, output_dims=2
        )
        self._query_dense = EinsumDense(
            einsum_equation,
            output_shape=_get_output_shape(
                output_rank - 1, [self._num_heads, self._key_dim]
            ),
            bias_axes=bias_axes if self._use_bias else None,
            name="query",
            **self._get_common_kwargs_for_sublayer(),
        )
        self._query_dense.build(query_shape)
        einsum_equation, bias_axes, output_rank = _build_proj_equation(
            key_rank - 1, bound_dims=1, output_dims=2
        )
        self._key_dense = EinsumDense(
            einsum_equation,
            output_shape=_get_output_shape(
                output_rank - 1, [self._num_heads, self._key_dim]
            ),
            bias_axes=bias_axes if self._use_bias else None,
            name="key",
            **self._get_common_kwargs_for_sublayer(),
        )
        self._key_dense.build(key_shape)
        einsum_equation, bias_axes, output_rank = _build_proj_equation(
            value_rank - 1, bound_dims=1, output_dims=2
        )
        self._value_dense = EinsumDense(
            einsum_equation,
            output_shape=_get_output_shape(
                output_rank - 1, [self._num_heads, self._value_dim]
            ),
            bias_axes=bias_axes if self._use_bias else None,
            name="value",
            **self._get_common_kwargs_for_sublayer(),
        )
        self._value_dense.build(value_shape)

        # Builds the attention computations for multi-head dot product
        # attention.  These computations could be wrapped into the keras
        # attention layer once it supports multi-head einsum computations.
        self._build_attention(output_rank)
        self._output_dense = self._make_output_dense(
            query_shape,
            self._get_common_kwargs_for_sublayer(),
            "attention_output",
        )
        output_dense_input_shape = list(
            self._query_dense.compute_output_shape(query_shape)
        )
        output_dense_input_shape[-1] = self._value_dim
        self._output_dense.build(tuple(output_dense_input_shape))
        self.built = True

    @property
    def query_dense(self):
        return self._query_dense

    @property
    def key_dense(self):
        return self._key_dense

    @property
    def value_dense(self):
        return self._value_dense

    @property
    def output_dense(self):
        return self._output_dense

    def _get_common_kwargs_for_sublayer(self):
        common_kwargs = dict(
            kernel_regularizer=self._kernel_regularizer,
            bias_regularizer=self._bias_regularizer,
            activity_regularizer=self._activity_regularizer,
            kernel_constraint=self._kernel_constraint,
            bias_constraint=self._bias_constraint,
            dtype=self.dtype_policy,
        )
        # Create new clone of kernel/bias initializer, so that we don't reuse
        # the initializer instance, which could lead to same init value since
        # initializer is stateless.
        kernel_initializer = self._kernel_initializer.__class__.from_config(
            self._kernel_initializer.get_config()
        )
        bias_initializer = self._bias_initializer.__class__.from_config(
            self._bias_initializer.get_config()
        )
        common_kwargs["kernel_initializer"] = kernel_initializer
        common_kwargs["bias_initializer"] = bias_initializer
        return common_kwargs

    def _make_output_dense(self, query_shape, common_kwargs, name=None):
        """Builds the output projection matrix.

        Args:
            free_dims: Number of free dimensions for einsum equation building.
            common_kwargs: Common keyword arguments for einsum layer.
            name: Name for the projection layer.

        Returns:
            Projection layer.
        """
        query_rank = len(query_shape)
        if self._output_shape:
            if not isinstance(self._output_shape, collections.abc.Sized):
                output_shape = [self._output_shape]
            else:
                output_shape = self._output_shape
        else:
            output_shape = [query_shape[-1]]
        einsum_equation, bias_axes, output_rank = _build_proj_equation(
            query_rank - 1, bound_dims=2, output_dims=len(output_shape)
        )
        return EinsumDense(
            einsum_equation,
            output_shape=_get_output_shape(output_rank - 1, output_shape),
            bias_axes=bias_axes if self._use_bias else None,
            name=name,
            **common_kwargs,
        )

    def _build_attention(self, rank):
        """Builds multi-head dot-product attention computations.

        This function builds attributes necessary for `_compute_attention` to
        customize attention computation to replace the default dot-product
        attention.

        Args:
            rank: the rank of query, key, value tensors.
        """
        if self._attention_axes is None:
            self._attention_axes = tuple(range(1, rank - 2))
        else:
            self._attention_axes = tuple(self._attention_axes)
        (
            self._dot_product_equation,
            self._combine_equation,
            attn_scores_rank,
        ) = _build_attention_equation(rank, attn_axes=self._attention_axes)
        norm_axes = tuple(
            range(
                attn_scores_rank - len(self._attention_axes), attn_scores_rank
            )
        )
        self._softmax = Softmax(axis=norm_axes, dtype=self.dtype_policy)
        self._dropout_layer = Dropout(
            rate=self._dropout, dtype=self.dtype_policy, seed=self.seed
        )
        self._inverse_sqrt_key_dim = 1.0 / math.sqrt(float(self._key_dim))

    def _masked_softmax(self, attention_scores, attention_mask=None):
        # Normalize the attention scores to probabilities.
        # attention_scores = [B, N, T, S]
        if attention_mask is not None:
            # The expand dim happens starting from the `num_heads` dimension,
            # (<batch_dims>, num_heads, <query_attention_dims,
            # key_attention_dims>)
            mask_expansion_axis = -len(self._attention_axes) * 2 - 1
            for _ in range(
                len(attention_scores.shape) - len(attention_mask.shape)
            ):
                attention_mask = ops.expand_dims(
                    attention_mask, axis=mask_expansion_axis
                )
        return self._softmax(attention_scores, mask=attention_mask)

    def _compute_attention(
        self, query, key, value, attention_mask=None, training=None
    ):
        """Applies Dot-product attention with query, key, value tensors.

        This function defines the computation inside `call` with projected
        multi-head Q, K, V inputs. Users can override this function for
        customized attention implementation.

        Args:
            query: Projected query tensor of shape `(B, T, N, key_dim)`.
            key: Projected key tensor of shape `(B, S, N, key_dim)`.
            value: Projected value tensor of shape `(B, S, N, value_dim)`.
            attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
                attention to certain positions. It is generally not needed if
                the `query` and `value` (and/or `key`) are masked.
            training: Python boolean indicating whether the layer should behave
                in training mode (adding dropout) or in inference mode (doing
                nothing).

        Returns:
          attention_output: Multi-headed outputs of attention computation.
          attention_scores: Multi-headed attention weights.
        """
        # Note: Applying scalar multiply at the smaller end of einsum improves
        # XLA performance, but may introduce slight numeric differences in
        # the Transformer attention head.
        query = ops.multiply(
            query, ops.cast(self._inverse_sqrt_key_dim, query.dtype)
        )

        # Take the dot product between "query" and "key" to get the raw
        # attention scores.
        attention_scores = ops.einsum(self._dot_product_equation, key, query)

        attention_scores = self._masked_softmax(
            attention_scores, attention_mask
        )

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        if self.dropout:
            final_attn_scores = self._dropout_layer(
                attention_scores, training=training
            )
        else:
            final_attn_scores = attention_scores

        # `context_layer` = [B, T, N, H]
        attention_output = ops.einsum(
            self._combine_equation, final_attn_scores, value
        )
        return attention_output, attention_scores

    def call(
        self,
        query,
        value,
        key=None,
        query_mask=None,
        value_mask=None,
        key_mask=None,
        attention_mask=None,
        return_attention_scores=False,
        training=None,
        use_causal_mask=False,
    ):
        if key is None:
            key = value

        attention_mask = self._compute_attention_mask(
            query,
            value,
            query_mask=query_mask,
            value_mask=value_mask,
            key_mask=key_mask,
            attention_mask=attention_mask,
            use_causal_mask=use_causal_mask,
        )

        #   N = `num_attention_heads`
        #   H = `size_per_head`
        # `query` = [B, T, N ,H]
        query = self._query_dense(query)

        # `key` = [B, S, N, H]
        key = self._key_dense(key)

        # `value` = [B, S, N, H]
        value = self._value_dense(value)

        attention_output, attention_scores = self._compute_attention(
            query, key, value, attention_mask, training
        )
        attention_output = self._output_dense(attention_output)

        if return_attention_scores:
            return attention_output, attention_scores
        return attention_output

    def _compute_attention_mask(
        self,
        query,
        value,
        query_mask=None,
        value_mask=None,
        key_mask=None,
        attention_mask=None,
        use_causal_mask=False,
    ):
        """Computes the attention mask, using the Keras masks of the inputs.

        * The `query`'s mask is reshaped from [B, T] to [B, T, 1].
        * The `value`'s mask is reshaped from [B, S] to [B, 1, S].
        * The `key`'s mask is reshaped from [B, S] to [B, 1, S]. The `key`'s
          mask is ignored if `key` is `None` or if `key is value`.
        * If `use_causal_mask=True`, then the causal mask is computed. Its shape
          is [1, T, S].

        All defined masks are merged using a logical AND operation (`&`).

        In general, if the `query` and `value` are masked, then there is no need
        to define the `attention_mask`.

        Args:
            query: Projected query tensor of shape `(B, T, N, key_dim)`.
            key: Projected key tensor of shape `(B, T, N, key_dim)`.
            value: Projected value tensor of shape `(B, T, N, value_dim)`.
            attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
                attention to certain positions.
            use_causal_mask: A boolean to indicate whether to apply a causal
                mask to prevent tokens from attending to future tokens (e.g.,
                used in a decoder Transformer).

        Returns:
            attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
                attention to certain positions, based on the Keras masks of the
                `query`, `key`, `value`, and `attention_mask` tensors, and the
                causal mask if `use_causal_mask=True`.
        """
        auto_mask = None
        if query_mask is not None:
            query_mask = ops.cast(query_mask, "bool")  # defensive casting
            # B = batch size, T = max query length
            auto_mask = ops.expand_dims(query_mask, -1)  # shape is [B, T, 1]
        if value_mask is not None:
            value_mask = ops.cast(value_mask, "bool")  # defensive casting
            # B = batch size, S == max value length
            mask = ops.expand_dims(value_mask, -2)  # shape is [B, 1, S]
            auto_mask = mask if auto_mask is None else auto_mask & mask
        if key_mask is not None:
            key_mask = ops.cast(key_mask, "bool")  # defensive casting
            # B == batch size, S == max key length == max value length
            mask = ops.expand_dims(key_mask, -2)  # shape is [B, 1, S]
            auto_mask = mask if auto_mask is None else auto_mask & mask
        if use_causal_mask:
            # the shape of the causal mask is [1, T, S]
            mask = self._compute_causal_mask(query, value)
            auto_mask = mask if auto_mask is None else auto_mask & mask
        if auto_mask is not None:
            # merge attention_mask & automatic mask, to shape [B, T, S]
            attention_mask = (
                auto_mask
                if attention_mask is None
                else ops.cast(attention_mask, bool) & auto_mask
            )
        return attention_mask

    def _compute_causal_mask(self, query, value=None):
        """Computes a causal mask (e.g., for masked self-attention layers).

        For example, if query and value both contain sequences of length 4,
        this function returns a boolean tensor equal to:

        ```
        [[[True,  False, False, False],
          [True,  True,  False, False],
          [True,  True,  True,  False],
          [True,  True,  True,  True]]]
        ```

        Args:
            query: query tensor of shape `(B, T, ...)`.
            value: value tensor of shape `(B, S, ...)` (optional, defaults to
                query).

        Returns:
            mask: a boolean tensor of shape `(1, T, S)` containing a lower
                triangular matrix of shape `(T, S)`.
        """
        q_seq_length = ops.shape(query)[1]
        v_seq_length = q_seq_length if value is None else ops.shape(value)[1]
        ones_mask = ops.ones((1, q_seq_length, v_seq_length), dtype="int32")
        row_index = ops.cumsum(ones_mask, axis=-2)
        col_index = ops.cumsum(ones_mask, axis=-1)
        return ops.greater_equal(row_index, col_index)

    def compute_output_shape(
        self,
        query_shape,
        value_shape,
        key_shape=None,
    ):
        if key_shape is None:
            key_shape = value_shape

        if query_shape[-1] != value_shape[-1]:
            raise ValueError(
                "The last dimension of `query_shape` and `value_shape` "
                f"must be equal, but are {query_shape[-1]}, {value_shape[-1]}. "
                "Received: query_shape={query_shape}, value_shape={value_shape}"
            )

        if value_shape[1:-1] != key_shape[1:-1]:
            raise ValueError(
                "All dimensions of `value` and `key`, except the last one, "
                f"must be equal. Received: value_shape={value_shape} and "
                f"key_shape={key_shape}"
            )

        if self._output_shape:
            return query_shape[:-1] + self._output_shape

        return query_shape

    def compute_output_spec(
        self,
        query,
        value,
        key=None,
        query_mask=None,
        value_mask=None,
        key_mask=None,
        attention_mask=None,
        return_attention_scores=False,
        training=None,
        use_causal_mask=False,
    ):
        if key is not None:
            key_shape = key.shape
        else:
            key_shape = None
        output_shape = self.compute_output_shape(
            query.shape, value.shape, key_shape
        )
        output_spec = backend.KerasTensor(
            output_shape, dtype=self.compute_dtype
        )
        if return_attention_scores:
            length = query.shape[1]
            attention_shape = (query.shape[0], self.num_heads, length, length)
            return output_spec, backend.KerasTensor(
                attention_shape, dtype=self.compute_dtype
            )
        return output_spec


def _index_to_einsum_variable(i):
    """Coverts an index to a einsum variable name.

    We simply map indices to lowercase characters, e.g. 0 -> 'a', 1 -> 'b'.
    """
    return string.ascii_lowercase[i]


def _build_attention_equation(rank, attn_axes):
    """Builds einsum equations for the attention computation.

    Query, key, value inputs after projection are expected to have the shape as:
    `(bs, <non-attention dims>, <attention dims>, num_heads, channels)`.
    `bs` and `<non-attention dims>` are treated as `<batch dims>`.

    The attention operations can be generalized:
    1. Query-key dot product:
        (<batch dims>, <query attention dims>, num_heads, channels),
        (<batch dims>, <key attention dims>, num_heads, channels) ->
        (<batch dims>, num_heads, <query attention dims>, <key attention dims>)
    2. Combination:
        (<batch dims>, num_heads, <query attention dims>, <key attention dims>),
        (<batch dims>, <value attention dims>, num_heads, channels) -> (<batch
        dims>, <query attention dims>, num_heads, channels)

    Args:
        rank: Rank of query, key, value tensors.
        attn_axes: List/tuple of axes, `[-1, rank)`,
            that attention will be applied to.

    Returns:
        Einsum equations.
    """
    target_notation = ""
    for i in range(rank):
        target_notation += _index_to_einsum_variable(i)
    # `batch_dims` includes the head dim.
    batch_dims = tuple(np.delete(range(rank), attn_axes + (rank - 1,)))
    letter_offset = rank
    source_notation = ""
    for i in range(rank):
        if i in batch_dims or i == rank - 1:
            source_notation += target_notation[i]
        else:
            source_notation += _index_to_einsum_variable(letter_offset)
            letter_offset += 1

    product_notation = "".join(
        [target_notation[i] for i in batch_dims]
        + [target_notation[i] for i in attn_axes]
        + [source_notation[i] for i in attn_axes]
    )
    dot_product_equation = "%s,%s->%s" % (
        source_notation,
        target_notation,
        product_notation,
    )
    attn_scores_rank = len(product_notation)
    combine_equation = "%s,%s->%s" % (
        product_notation,
        source_notation,
        target_notation,
    )
    return dot_product_equation, combine_equation, attn_scores_rank


def _build_proj_equation(free_dims, bound_dims, output_dims):
    """Builds an einsum equation for projections inside multi-head attention."""
    input_str = ""
    kernel_str = ""
    output_str = ""
    bias_axes = ""
    letter_offset = 0
    for i in range(free_dims):
        char = _index_to_einsum_variable(i + letter_offset)
        input_str += char
        output_str += char

    letter_offset += free_dims
    for i in range(bound_dims):
        char = _index_to_einsum_variable(i + letter_offset)
        input_str += char
        kernel_str += char

    letter_offset += bound_dims
    for i in range(output_dims):
        char = _index_to_einsum_variable(i + letter_offset)
        kernel_str += char
        output_str += char
        bias_axes += char
    equation = f"{input_str},{kernel_str}->{output_str}"

    return equation, bias_axes, len(output_str)


def _get_output_shape(output_rank, known_last_dims):
    return [None] * (output_rank - len(known_last_dims)) + list(known_last_dims)
