# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings

import torch
import torch.nn as nn

from mmcv import ConfigDict, deprecated_api_warning
from mmcv.cnn import Linear, build_activation_layer, build_norm_layer
from mmcv.runner.base_module import BaseModule, ModuleList, Sequential
from mmcv.utils import build_from_cfg
from .drop import build_dropout
from .registry import (ATTENTION, FEEDFORWARD_NETWORK, POSITIONAL_ENCODING,
                       TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE)

# Avoid BC-breaking of importing MultiScaleDeformableAttention from this file
try:
    from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention  # noqa F401
    warnings.warn(
        ImportWarning(
            '``MultiScaleDeformableAttention`` has been moved to '
            '``mmcv.ops.multi_scale_deform_attn``, please change original path '  # noqa E501
            '``from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention`` '  # noqa E501
            'to ``from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention`` '  # noqa E501
        ))

except ImportError:
    # warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '
    #               '``mmcv.ops.multi_scale_deform_attn``, '
    #               'You should install ``mmcv-full`` if you need this module. ')
    pass


def build_positional_encoding(cfg, default_args=None):
    """Builder for Position Encoding."""
    return build_from_cfg(cfg, POSITIONAL_ENCODING, default_args)


def build_attention(cfg, default_args=None):
    """Builder for attention."""
    return build_from_cfg(cfg, ATTENTION, default_args)


def build_feedforward_network(cfg, default_args=None):
    """Builder for feed-forward network (FFN)."""
    return build_from_cfg(cfg, FEEDFORWARD_NETWORK, default_args)


def build_transformer_layer(cfg, default_args=None):
    """Builder for transformer layer."""
    return build_from_cfg(cfg, TRANSFORMER_LAYER, default_args)


def build_transformer_layer_sequence(cfg, default_args=None):
    """Builder for transformer encoder and transformer decoder."""
    return build_from_cfg(cfg, TRANSFORMER_LAYER_SEQUENCE, default_args)


@ATTENTION.register_module()
class MultiheadAttention(BaseModule):
    """A wrapper for ``torch.nn.MultiheadAttention``.

    This module implements MultiheadAttention with identity connection,
    and positional encoding  is also passed as input.

    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        attn_drop (float): A Dropout layer on attn_output_weights.
            Default: 0.0.
        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
            Default: 0.0.
        dropout_layer (obj:`ConfigDict`): The dropout_layer used
            when adding the shortcut.
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
        batch_first (bool): When it is True,  Key, Query and Value are shape of
            (batch, n, embed_dim), otherwise (n, batch, embed_dim).
             Default to False.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 attn_drop=0.,
                 proj_drop=0.,
                 dropout_layer=dict(type='Dropout', drop_prob=0.),
                 init_cfg=None,
                 batch_first=False,
                 **kwargs):
        super(MultiheadAttention, self).__init__(init_cfg)
        if 'dropout' in kwargs:
            warnings.warn('The arguments `dropout` in MultiheadAttention '
                          'has been deprecated, now you can separately '
                          'set `attn_drop`(float), proj_drop(float), '
                          'and `dropout_layer`(dict) ')
            attn_drop = kwargs['dropout']
            dropout_layer['drop_prob'] = kwargs.pop('dropout')

        self.embed_dims = embed_dims
        self.num_heads = num_heads
        self.batch_first = batch_first

        self.attn = nn.MultiheadAttention(embed_dims, num_heads, attn_drop,
                                          **kwargs)

        self.proj_drop = nn.Dropout(proj_drop)
        self.dropout_layer = build_dropout(
            dropout_layer) if dropout_layer else nn.Identity()

    @deprecated_api_warning({'residual': 'identity'},
                            cls_name='MultiheadAttention')
    def forward(self,
                query,
                key=None,
                value=None,
                identity=None,
                query_pos=None,
                key_pos=None,
                attn_mask=None,
                key_padding_mask=None,
                **kwargs):
        """Forward function for `MultiheadAttention`.

        **kwargs allow passing a more general data flow when combining
        with other operations in `transformerlayer`.

        Args:
            query (Tensor): The input query with shape [num_queries, bs,
                embed_dims] if self.batch_first is False, else
                [bs, num_queries embed_dims].
            key (Tensor): The key tensor with shape [num_keys, bs,
                embed_dims] if self.batch_first is False, else
                [bs, num_keys, embed_dims] .
                If None, the ``query`` will be used. Defaults to None.
            value (Tensor): The value tensor with same shape as `key`.
                Same in `nn.MultiheadAttention.forward`. Defaults to None.
                If None, the `key` will be used.
            identity (Tensor): This tensor, with the same shape as x,
                will be used for the identity link.
                If None, `x` will be used. Defaults to None.
            query_pos (Tensor): The positional encoding for query, with
                the same shape as `x`. If not None, it will
                be added to `x` before forward function. Defaults to None.
            key_pos (Tensor): The positional encoding for `key`, with the
                same shape as `key`. Defaults to None. If not None, it will
                be added to `key` before forward function. If None, and
                `query_pos` has the same shape as `key`, then `query_pos`
                will be used for `key_pos`. Defaults to None.
            attn_mask (Tensor): ByteTensor mask with shape [num_queries,
                num_keys]. Same in `nn.MultiheadAttention.forward`.
                Defaults to None.
            key_padding_mask (Tensor): ByteTensor with shape [bs, num_keys].
                Defaults to None.

        Returns:
            Tensor: forwarded results with shape
                [num_queries, bs, embed_dims]
                if self.batch_first is False, else
                [bs, num_queries embed_dims].
        """

        if key is None:
            key = query
        if value is None:
            value = key
        if identity is None:
            identity = query
        if key_pos is None:
            if query_pos is not None:
                # use query_pos if key_pos is not available
                if query_pos.shape == key.shape:
                    key_pos = query_pos
                else:
                    warnings.warn(f'position encoding of key is'
                                  f'missing in {self.__class__.__name__}.')
        if query_pos is not None:
            query = query + query_pos
        if key_pos is not None:
            key = key + key_pos

        # Because the dataflow('key', 'query', 'value') of
        # ``torch.nn.MultiheadAttention`` is (num_query, batch,
        # embed_dims), We should adjust the shape of dataflow from
        # batch_first (batch, num_query, embed_dims) to num_query_first
        # (num_query ,batch, embed_dims), and recover ``attn_output``
        # from num_query_first to batch_first.
        if self.batch_first:
            query = query.transpose(0, 1)
            key = key.transpose(0, 1)
            value = value.transpose(0, 1)

        out = self.attn(
            query=query,
            key=key,
            value=value,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask)[0]

        if self.batch_first:
            out = out.transpose(0, 1)

        return identity + self.dropout_layer(self.proj_drop(out))


@FEEDFORWARD_NETWORK.register_module()
class FFN(BaseModule):
    """Implements feed-forward networks (FFNs) with identity connection.

    Args:
        embed_dims (int): The feature dimension. Same as
            `MultiheadAttention`. Defaults: 256.
        feedforward_channels (int): The hidden dimension of FFNs.
            Defaults: 1024.
        num_fcs (int, optional): The number of fully-connected layers in
            FFNs. Default: 2.
        act_cfg (dict, optional): The activation config for FFNs.
            Default: dict(type='ReLU')
        ffn_drop (float, optional): Probability of an element to be
            zeroed in FFN. Default 0.0.
        add_identity (bool, optional): Whether to add the
            identity connection. Default: `True`.
        dropout_layer (obj:`ConfigDict`): The dropout_layer used
            when adding the shortcut.
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
    """

    @deprecated_api_warning(
        {
            'dropout': 'ffn_drop',
            'add_residual': 'add_identity'
        },
        cls_name='FFN')
    def __init__(self,
                 embed_dims=256,
                 feedforward_channels=1024,
                 num_fcs=2,
                 act_cfg=dict(type='ReLU', inplace=True),
                 ffn_drop=0.,
                 dropout_layer=None,
                 add_identity=True,
                 init_cfg=None,
                 **kwargs):
        super(FFN, self).__init__(init_cfg)
        assert num_fcs >= 2, 'num_fcs should be no less ' \
            f'than 2. got {num_fcs}.'
        self.embed_dims = embed_dims
        self.feedforward_channels = feedforward_channels
        self.num_fcs = num_fcs
        self.act_cfg = act_cfg
        self.activate = build_activation_layer(act_cfg)

        layers = []
        in_channels = embed_dims
        for _ in range(num_fcs - 1):
            layers.append(
                Sequential(
                    Linear(in_channels, feedforward_channels), self.activate,
                    nn.Dropout(ffn_drop)))
            in_channels = feedforward_channels
        layers.append(Linear(feedforward_channels, embed_dims))
        layers.append(nn.Dropout(ffn_drop))
        self.layers = Sequential(*layers)
        self.dropout_layer = build_dropout(
            dropout_layer) if dropout_layer else torch.nn.Identity()
        self.add_identity = add_identity

    @deprecated_api_warning({'residual': 'identity'}, cls_name='FFN')
    def forward(self, x, identity=None):
        """Forward function for `FFN`.

        The function would add x to the output tensor if residue is None.
        """
        out = self.layers(x)
        if not self.add_identity:
            return self.dropout_layer(out)
        if identity is None:
            identity = x
        return identity + self.dropout_layer(out)


@TRANSFORMER_LAYER.register_module()
class BaseTransformerLayer(BaseModule):
    """Base `TransformerLayer` for vision transformer.

    It can be built from `mmcv.ConfigDict` and support more flexible
    customization, for example, using any number of `FFN or LN ` and
    use different kinds of `attention` by specifying a list of `ConfigDict`
    named `attn_cfgs`. It is worth mentioning that it supports `prenorm`
    when you specifying `norm` as the first element of `operation_order`.
    More details about the `prenorm`: `On Layer Normalization in the
    Transformer Architecture <https://arxiv.org/abs/2002.04745>`_ .

    Args:
        attn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )):
            Configs for `self_attention` or `cross_attention` modules,
            The order of the configs in the list should be consistent with
            corresponding attentions in operation_order.
            If it is a dict, all of the attention modules in operation_order
            will be built with this config. Default: None.
        ffn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )):
            Configs for FFN, The order of the configs in the list should be
            consistent with corresponding ffn in operation_order.
            If it is a dict, all of the attention modules in operation_order
            will be built with this config.
        operation_order (tuple[str]): The execution order of operation
            in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
            Support `prenorm` when you specifying first element as `norm`.
            Default：None.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN').
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
        batch_first (bool): Key, Query and Value are shape
            of (batch, n, embed_dim)
            or (n, batch, embed_dim). Default to False.
    """

    def __init__(self,
                 attn_cfgs=None,
                 ffn_cfgs=dict(
                     type='FFN',
                     embed_dims=256,
                     feedforward_channels=1024,
                     num_fcs=2,
                     ffn_drop=0.,
                     act_cfg=dict(type='ReLU', inplace=True),
                 ),
                 operation_order=None,
                 norm_cfg=dict(type='LN'),
                 init_cfg=None,
                 batch_first=False,
                 **kwargs):

        deprecated_args = dict(
            feedforward_channels='feedforward_channels',
            ffn_dropout='ffn_drop',
            ffn_num_fcs='num_fcs')
        for ori_name, new_name in deprecated_args.items():
            if ori_name in kwargs:
                warnings.warn(
                    f'The arguments `{ori_name}` in BaseTransformerLayer '
                    f'has been deprecated, now you should set `{new_name}` '
                    f'and other FFN related arguments '
                    f'to a dict named `ffn_cfgs`. ')
                ffn_cfgs[new_name] = kwargs[ori_name]

        super(BaseTransformerLayer, self).__init__(init_cfg)

        self.batch_first = batch_first

        assert set(operation_order) & set(
            ['self_attn', 'norm', 'ffn', 'cross_attn']) == \
            set(operation_order), f'The operation_order of' \
            f' {self.__class__.__name__} should ' \
            f'contains all four operation type ' \
            f"{['self_attn', 'norm', 'ffn', 'cross_attn']}"

        num_attn = operation_order.count('self_attn') + operation_order.count(
            'cross_attn')
        if isinstance(attn_cfgs, dict):
            attn_cfgs = [copy.deepcopy(attn_cfgs) for _ in range(num_attn)]
        else:
            assert num_attn == len(attn_cfgs), f'The length ' \
                f'of attn_cfg {num_attn} is ' \
                f'not consistent with the number of attention' \
                f'in operation_order {operation_order}.'

        self.num_attn = num_attn
        self.operation_order = operation_order
        self.norm_cfg = norm_cfg
        self.pre_norm = operation_order[0] == 'norm'
        self.attentions = ModuleList()

        index = 0
        for operation_name in operation_order:
            if operation_name in ['self_attn', 'cross_attn']:
                if 'batch_first' in attn_cfgs[index]:
                    assert self.batch_first == attn_cfgs[index]['batch_first']
                else:
                    attn_cfgs[index]['batch_first'] = self.batch_first
                attention = build_attention(attn_cfgs[index])
                # Some custom attentions used as `self_attn`
                # or `cross_attn` can have different behavior.
                attention.operation_name = operation_name
                self.attentions.append(attention)
                index += 1

        self.embed_dims = self.attentions[0].embed_dims

        self.ffns = ModuleList()
        num_ffns = operation_order.count('ffn')
        if isinstance(ffn_cfgs, dict):
            ffn_cfgs = ConfigDict(ffn_cfgs)
        if isinstance(ffn_cfgs, dict):
            ffn_cfgs = [copy.deepcopy(ffn_cfgs) for _ in range(num_ffns)]
        assert len(ffn_cfgs) == num_ffns
        for ffn_index in range(num_ffns):
            if 'embed_dims' not in ffn_cfgs[ffn_index]:
                ffn_cfgs['embed_dims'] = self.embed_dims
            else:
                assert ffn_cfgs[ffn_index]['embed_dims'] == self.embed_dims
            self.ffns.append(
                build_feedforward_network(ffn_cfgs[ffn_index],
                                          dict(type='FFN')))

        self.norms = ModuleList()
        num_norms = operation_order.count('norm')
        for _ in range(num_norms):
            self.norms.append(build_norm_layer(norm_cfg, self.embed_dims)[1])

    def forward(self,
                query,
                key=None,
                value=None,
                query_pos=None,
                key_pos=None,
                attn_masks=None,
                query_key_padding_mask=None,
                key_padding_mask=None,
                **kwargs):
        """Forward function for `TransformerDecoderLayer`.

        **kwargs contains some specific arguments of attentions.

        Args:
            query (Tensor): The input query with shape
                [num_queries, bs, embed_dims] if
                self.batch_first is False, else
                [bs, num_queries embed_dims].
            key (Tensor): The key tensor with shape [num_keys, bs,
                embed_dims] if self.batch_first is False, else
                [bs, num_keys, embed_dims] .
            value (Tensor): The value tensor with same shape as `key`.
            query_pos (Tensor): The positional encoding for `query`.
                Default: None.
            key_pos (Tensor): The positional encoding for `key`.
                Default: None.
            attn_masks (List[Tensor] | None): 2D Tensor used in
                calculation of corresponding attention. The length of
                it should equal to the number of `attention` in
                `operation_order`. Default: None.
            query_key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_queries]. Only used in `self_attn` layer.
                Defaults to None.
            key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_keys]. Default: None.

        Returns:
            Tensor: forwarded results with shape [num_queries, bs, embed_dims].
        """

        norm_index = 0
        attn_index = 0
        ffn_index = 0
        identity = query
        if attn_masks is None:
            attn_masks = [None for _ in range(self.num_attn)]
        elif isinstance(attn_masks, torch.Tensor):
            attn_masks = [
                copy.deepcopy(attn_masks) for _ in range(self.num_attn)
            ]
            warnings.warn(f'Use same attn_mask in all attentions in '
                          f'{self.__class__.__name__} ')
        else:
            assert len(attn_masks) == self.num_attn, f'The length of ' \
                        f'attn_masks {len(attn_masks)} must be equal ' \
                        f'to the number of attention in ' \
                        f'operation_order {self.num_attn}'

        for layer in self.operation_order:
            if layer == 'self_attn':
                temp_key = temp_value = query
                query = self.attentions[attn_index](
                    query,
                    temp_key,
                    temp_value,
                    identity if self.pre_norm else None,
                    query_pos=query_pos,
                    key_pos=query_pos,
                    attn_mask=attn_masks[attn_index],
                    key_padding_mask=query_key_padding_mask,
                    **kwargs)
                attn_index += 1
                identity = query

            elif layer == 'norm':
                query = self.norms[norm_index](query)
                norm_index += 1

            elif layer == 'cross_attn':
                query = self.attentions[attn_index](
                    query,
                    key,
                    value,
                    identity if self.pre_norm else None,
                    query_pos=query_pos,
                    key_pos=key_pos,
                    attn_mask=attn_masks[attn_index],
                    key_padding_mask=key_padding_mask,
                    **kwargs)
                attn_index += 1
                identity = query

            elif layer == 'ffn':
                query = self.ffns[ffn_index](
                    query, identity if self.pre_norm else None)
                ffn_index += 1

        return query


@TRANSFORMER_LAYER_SEQUENCE.register_module()
class TransformerLayerSequence(BaseModule):
    """Base class for TransformerEncoder and TransformerDecoder in vision
    transformer.

    As base-class of Encoder and Decoder in vision transformer.
    Support customization such as specifying different kind
    of `transformer_layer` in `transformer_coder`.

    Args:
        transformerlayer (list[obj:`mmcv.ConfigDict`] |
            obj:`mmcv.ConfigDict`): Config of transformerlayer
            in TransformerCoder. If it is obj:`mmcv.ConfigDict`,
             it would be repeated `num_layer` times to a
             list[`mmcv.ConfigDict`]. Default: None.
        num_layers (int): The number of `TransformerLayer`. Default: None.
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
    """

    def __init__(self, transformerlayers=None, num_layers=None, init_cfg=None):
        super(TransformerLayerSequence, self).__init__(init_cfg)
        if isinstance(transformerlayers, dict):
            transformerlayers = [
                copy.deepcopy(transformerlayers) for _ in range(num_layers)
            ]
        else:
            assert isinstance(transformerlayers, list) and \
                   len(transformerlayers) == num_layers
        self.num_layers = num_layers
        self.layers = ModuleList()
        for i in range(num_layers):
            self.layers.append(build_transformer_layer(transformerlayers[i]))
        self.embed_dims = self.layers[0].embed_dims
        self.pre_norm = self.layers[0].pre_norm

    def forward(self,
                query,
                key,
                value,
                query_pos=None,
                key_pos=None,
                attn_masks=None,
                query_key_padding_mask=None,
                key_padding_mask=None,
                **kwargs):
        """Forward function for `TransformerCoder`.

        Args:
            query (Tensor): Input query with shape
                `(num_queries, bs, embed_dims)`.
            key (Tensor): The key tensor with shape
                `(num_keys, bs, embed_dims)`.
            value (Tensor): The value tensor with shape
                `(num_keys, bs, embed_dims)`.
            query_pos (Tensor): The positional encoding for `query`.
                Default: None.
            key_pos (Tensor): The positional encoding for `key`.
                Default: None.
            attn_masks (List[Tensor], optional): Each element is 2D Tensor
                which is used in calculation of corresponding attention in
                operation_order. Default: None.
            query_key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_queries]. Only used in self-attention
                Default: None.
            key_padding_mask (Tensor): ByteTensor for `query`, with
                shape [bs, num_keys]. Default: None.

        Returns:
            Tensor:  results with shape [num_queries, bs, embed_dims].
        """
        for layer in self.layers:
            query = layer(
                query,
                key,
                value,
                query_pos=query_pos,
                key_pos=key_pos,
                attn_masks=attn_masks,
                query_key_padding_mask=query_key_padding_mask,
                key_padding_mask=key_padding_mask,
                **kwargs)
        return query
