#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Transformer encoder."""

from __future__ import absolute_import, division, print_function

from functools import partial

import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers


def multi_head_attention(
    queries,
    keys,
    values,
    attn_bias,
    d_key,
    d_value,
    d_model,
    n_head=1,
    dropout_rate=0.0,
    cache=None,
    param_initializer=None,
    name="multi_head_att",
):
    """
    Multi-Head Attention. Note that attn_bias is added to the logit before
    computing softmax activation to mask certain selected positions so that
    they will not considered in attention weights.
    """
    keys = queries if keys is None else keys
    values = keys if values is None else values

    if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
        raise ValueError("Inputs: queries, keys and values should all be 3-D tensors.")

    def __compute_qkv(queries, keys, values, n_head, d_key, d_value):
        """
        Add linear projection to queries, keys, and values.
        """
        q = layers.fc(
            input=queries,
            size=d_key * n_head,
            num_flatten_dims=2,
            param_attr=fluid.ParamAttr(name=name + "_query_fc.w_0", initializer=param_initializer),
            bias_attr=name + "_query_fc.b_0",
        )
        k = layers.fc(
            input=keys,
            size=d_key * n_head,
            num_flatten_dims=2,
            param_attr=fluid.ParamAttr(name=name + "_key_fc.w_0", initializer=param_initializer),
            bias_attr=name + "_key_fc.b_0",
        )
        v = layers.fc(
            input=values,
            size=d_value * n_head,
            num_flatten_dims=2,
            param_attr=fluid.ParamAttr(name=name + "_value_fc.w_0", initializer=param_initializer),
            bias_attr=name + "_value_fc.b_0",
        )
        return q, k, v

    def __split_heads(x, n_head):
        """
        Reshape the last dimension of input tensor x so that it becomes two
        dimensions and then transpose. Specifically, input a tensor with shape
        [bs, max_sequence_length, n_head * hidden_dim] then output a tensor
        with shape [bs, n_head, max_sequence_length, hidden_dim].
        """
        hidden_size = x.shape[-1]
        # The value 0 in shape attr means copying the corresponding dimension
        # size of the input as the output dimension size.
        reshaped = layers.reshape(x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True)

        # permuate the dimensions into:
        # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])

    def __combine_heads(x):
        """
        Transpose and then reshape the last two dimensions of input tensor x
        so that it becomes one dimension, which is reverse to __split_heads.
        """
        if len(x.shape) == 3:
            return x
        if len(x.shape) != 4:
            raise ValueError("Input(x) should be a 4-D Tensor.")

        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
        # The value 0 in shape attr means copying the corresponding dimension
        # size of the input as the output dimension size.
        return layers.reshape(x=trans_x, shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]], inplace=True)

    def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate):
        """
        Scaled Dot-Product Attention
        """
        scaled_q = layers.scale(x=q, scale=d_key**-0.5)
        product = paddle.matmul(x=scaled_q, y=k, transpose_y=True)
        if attn_bias:
            product += attn_bias
        weights = layers.softmax(product)
        if dropout_rate:
            weights = layers.dropout(
                weights, dropout_prob=dropout_rate, dropout_implementation="upscale_in_train", is_test=False
            )
        out = paddle.matmul(weights, v)
        return out

    q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)

    if cache is not None:  # use cache and concat time steps
        # Since the inplace reshape in __split_heads changes the shape of k and
        # v, which is the cache input for next time step, reshape the cache
        # input from the previous time step first.
        k = cache["k"] = layers.concat([layers.reshape(cache["k"], shape=[0, 0, d_model]), k], axis=1)
        v = cache["v"] = layers.concat([layers.reshape(cache["v"], shape=[0, 0, d_model]), v], axis=1)

    q = __split_heads(q, n_head)
    k = __split_heads(k, n_head)
    v = __split_heads(v, n_head)

    ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate)

    out = __combine_heads(ctx_multiheads)

    # Project back to the model size.
    proj_out = layers.fc(
        input=out,
        size=d_model,
        num_flatten_dims=2,
        param_attr=fluid.ParamAttr(name=name + "_output_fc.w_0", initializer=param_initializer),
        bias_attr=name + "_output_fc.b_0",
    )
    return proj_out


def positionwise_feed_forward(x, d_inner_hid, d_hid, dropout_rate, hidden_act, param_initializer=None, name="ffn"):
    """
    Position-wise Feed-Forward Networks.
    This module consists of two linear transformations with a ReLU activation
    in between, which is applied to each position separately and identically.
    """
    hidden = layers.fc(
        input=x,
        size=d_inner_hid,
        num_flatten_dims=2,
        act=hidden_act,
        param_attr=fluid.ParamAttr(name=name + "_fc_0.w_0", initializer=param_initializer),
        bias_attr=name + "_fc_0.b_0",
    )
    if dropout_rate:
        hidden = layers.dropout(
            hidden, dropout_prob=dropout_rate, dropout_implementation="upscale_in_train", is_test=False
        )
    out = layers.fc(
        input=hidden,
        size=d_hid,
        num_flatten_dims=2,
        param_attr=fluid.ParamAttr(name=name + "_fc_1.w_0", initializer=param_initializer),
        bias_attr=name + "_fc_1.b_0",
    )
    return out


def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.0, name=""):
    """
    Add residual connection, layer normalization and dropout to the out tensor
    optionally according to the value of process_cmd.
    This will be used before or after multi-head attention and position-wise
    feed-forward networks.
    """
    for cmd in process_cmd:
        if cmd == "a":  # add residual connection
            out = out + prev_out if prev_out else out
        elif cmd == "n":  # add layer normalization
            out_dtype = out.dtype
            if out_dtype == fluid.core.VarDesc.VarType.FP16:
                out = layers.cast(x=out, dtype="float32")
            out = layers.layer_norm(
                out,
                begin_norm_axis=len(out.shape) - 1,
                param_attr=fluid.ParamAttr(
                    name=name + "_layer_norm_scale", initializer=fluid.initializer.Constant(1.0)
                ),
                bias_attr=fluid.ParamAttr(name=name + "_layer_norm_bias", initializer=fluid.initializer.Constant(0.0)),
            )
            if out_dtype == fluid.core.VarDesc.VarType.FP16:
                out = layers.cast(x=out, dtype="float16")
        elif cmd == "d":  # add dropout
            if dropout_rate:
                out = layers.dropout(
                    out, dropout_prob=dropout_rate, dropout_implementation="upscale_in_train", is_test=False
                )
    return out


pre_process_layer = partial(pre_post_process_layer, None)
post_process_layer = pre_post_process_layer


def encoder_layer(
    enc_input,
    attn_bias,
    n_head,
    d_key,
    d_value,
    d_model,
    d_inner_hid,
    prepostprocess_dropout,
    attention_dropout,
    relu_dropout,
    hidden_act,
    preprocess_cmd="n",
    postprocess_cmd="da",
    param_initializer=None,
    name="",
):
    """The encoder layers that can be stacked to form a deep encoder.
    This module consists of a multi-head (self) attention followed by
    position-wise feed-forward networks and both the two components companied
    with the post_process_layer to add residual connection, layer normalization
    and dropout.
    """
    attn_output = multi_head_attention(
        pre_process_layer(enc_input, preprocess_cmd, prepostprocess_dropout, name=name + "_pre_att"),
        None,
        None,
        attn_bias,
        d_key,
        d_value,
        d_model,
        n_head,
        attention_dropout,
        param_initializer=param_initializer,
        name=name + "_multi_head_att",
    )
    attn_output = post_process_layer(
        enc_input, attn_output, postprocess_cmd, prepostprocess_dropout, name=name + "_post_att"
    )
    ffd_output = positionwise_feed_forward(
        pre_process_layer(attn_output, preprocess_cmd, prepostprocess_dropout, name=name + "_pre_ffn"),
        d_inner_hid,
        d_model,
        relu_dropout,
        hidden_act,
        param_initializer=param_initializer,
        name=name + "_ffn",
    )
    return (
        post_process_layer(attn_output, ffd_output, postprocess_cmd, prepostprocess_dropout, name=name + "_post_ffn"),
        ffd_output,
    )


def encoder(
    enc_input,
    attn_bias,
    n_layer,
    n_head,
    d_key,
    d_value,
    d_model,
    d_inner_hid,
    prepostprocess_dropout,
    attention_dropout,
    relu_dropout,
    hidden_act,
    preprocess_cmd="n",
    postprocess_cmd="da",
    param_initializer=None,
    model_name="",
    name="",
):
    """
    The encoder is composed of a stack of identical layers returned by calling
    encoder_layer.
    """
    checkpoints = []
    for i in range(n_layer):
        enc_output, cp = encoder_layer(
            enc_input,
            attn_bias,
            n_head,
            d_key,
            d_value,
            d_model,
            d_inner_hid,
            prepostprocess_dropout,
            attention_dropout,
            relu_dropout,
            hidden_act,
            preprocess_cmd,
            postprocess_cmd,
            param_initializer=param_initializer,
            name=name + "_layer_" + str(i),
        )
        checkpoints.append(cp)
        enc_input = enc_output
    enc_output = pre_process_layer(
        enc_output, preprocess_cmd, prepostprocess_dropout, name=model_name + "post_encoder"
    )

    return enc_output, checkpoints
