# coding=utf-8
# Copyright 2023 MBZUAI and The HuggingFace Inc. team. 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.
""" PyTorch SwiftFormer model."""


import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2CLS
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)
from .configuration_swiftformer import SwiftFormerConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "SwiftFormerConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "MBZUAI/swiftformer-xs"
_EXPECTED_OUTPUT_SHAPE = [1, 220, 7, 7]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "MBZUAI/swiftformer-xs"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "MBZUAI/swiftformer-xs",
    # See all SwiftFormer models at https://huggingface.co/models?filter=swiftformer
]


class SwiftFormerPatchEmbedding(nn.Module):
    """
    Patch Embedding Layer constructed of two 2D convolutional layers.

    Input: tensor of shape `[batch_size, in_channels, height, width]`

    Output: tensor of shape `[batch_size, out_channels, height/4, width/4]`
    """

    def __init__(self, config: SwiftFormerConfig):
        super().__init__()

        in_chs = config.num_channels
        out_chs = config.embed_dims[0]
        self.patch_embedding = nn.Sequential(
            nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(out_chs // 2, eps=config.batch_norm_eps),
            nn.ReLU(),
            nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(out_chs, eps=config.batch_norm_eps),
            nn.ReLU(),
        )

    def forward(self, x):
        return self.patch_embedding(x)


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Swiftformer
class SwiftFormerDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return "p={}".format(self.drop_prob)


class SwiftFormerEmbeddings(nn.Module):
    """
    Embeddings layer consisting of a single 2D convolutional and batch normalization layer.

    Input: tensor of shape `[batch_size, channels, height, width]`

    Output: tensor of shape `[batch_size, channels, height/stride, width/stride]`
    """

    def __init__(self, config: SwiftFormerConfig, index: int):
        super().__init__()

        patch_size = config.down_patch_size
        stride = config.down_stride
        padding = config.down_pad
        embed_dims = config.embed_dims

        in_chans = embed_dims[index]
        embed_dim = embed_dims[index + 1]

        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride)
        padding = padding if isinstance(padding, collections.abc.Iterable) else (padding, padding)

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
        self.norm = nn.BatchNorm2d(embed_dim, eps=config.batch_norm_eps)

    def forward(self, x):
        x = self.proj(x)
        x = self.norm(x)
        return x


class SwiftFormerConvEncoder(nn.Module):
    """
    `SwiftFormerConvEncoder` with 3*3 and 1*1 convolutions.

    Input: tensor of shape `[batch_size, channels, height, width]`

    Output: tensor of shape `[batch_size, channels, height, width]`
    """

    def __init__(self, config: SwiftFormerConfig, dim: int):
        super().__init__()
        hidden_dim = int(config.mlp_ratio * dim)

        self.depth_wise_conv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)
        self.norm = nn.BatchNorm2d(dim, eps=config.batch_norm_eps)
        self.point_wise_conv1 = nn.Conv2d(dim, hidden_dim, kernel_size=1)
        self.act = nn.GELU()
        self.point_wise_conv2 = nn.Conv2d(hidden_dim, dim, kernel_size=1)
        self.drop_path = nn.Identity()
        self.layer_scale = nn.Parameter(torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)

    def forward(self, x):
        input = x
        x = self.depth_wise_conv(x)
        x = self.norm(x)
        x = self.point_wise_conv1(x)
        x = self.act(x)
        x = self.point_wise_conv2(x)
        x = input + self.drop_path(self.layer_scale * x)
        return x


class SwiftFormerMlp(nn.Module):
    """
    MLP layer with 1*1 convolutions.

    Input: tensor of shape `[batch_size, channels, height, width]`

    Output: tensor of shape `[batch_size, channels, height, width]`
    """

    def __init__(self, config: SwiftFormerConfig, in_features: int):
        super().__init__()
        hidden_features = int(in_features * config.mlp_ratio)
        self.norm1 = nn.BatchNorm2d(in_features, eps=config.batch_norm_eps)
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
        act_layer = ACT2CLS[config.hidden_act]
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, in_features, 1)
        self.drop = nn.Dropout(p=0.0)

    def forward(self, x):
        x = self.norm1(x)
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class SwiftFormerEfficientAdditiveAttention(nn.Module):
    """
    Efficient Additive Attention module for SwiftFormer.

    Input: tensor of shape `[batch_size, channels, height, width]`

    Output: tensor of shape `[batch_size, channels, height, width]`
    """

    def __init__(self, config: SwiftFormerConfig, dim: int = 512):
        super().__init__()

        self.to_query = nn.Linear(dim, dim)
        self.to_key = nn.Linear(dim, dim)

        self.w_g = nn.Parameter(torch.randn(dim, 1))
        self.scale_factor = dim**-0.5
        self.proj = nn.Linear(dim, dim)
        self.final = nn.Linear(dim, dim)

    def forward(self, x):
        query = self.to_query(x)
        key = self.to_key(x)

        query = torch.nn.functional.normalize(query, dim=-1)
        key = torch.nn.functional.normalize(key, dim=-1)

        query_weight = query @ self.w_g
        scaled_query_weight = query_weight * self.scale_factor
        scaled_query_weight = scaled_query_weight.softmax(dim=-1)

        global_queries = torch.sum(scaled_query_weight * query, dim=1)
        global_queries = global_queries.unsqueeze(1).repeat(1, key.shape[1], 1)

        out = self.proj(global_queries * key) + query
        out = self.final(out)

        return out


class SwiftFormerLocalRepresentation(nn.Module):
    """
    Local Representation module for SwiftFormer that is implemented by 3*3 depth-wise and point-wise convolutions.

    Input: tensor of shape `[batch_size, channels, height, width]`

    Output: tensor of shape `[batch_size, channels, height, width]`
    """

    def __init__(self, config: SwiftFormerConfig, dim: int):
        super().__init__()

        self.depth_wise_conv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)
        self.norm = nn.BatchNorm2d(dim, eps=config.batch_norm_eps)
        self.point_wise_conv1 = nn.Conv2d(dim, dim, kernel_size=1)
        self.act = nn.GELU()
        self.point_wise_conv2 = nn.Conv2d(dim, dim, kernel_size=1)
        self.drop_path = nn.Identity()
        self.layer_scale = nn.Parameter(torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)

    def forward(self, x):
        input = x
        x = self.depth_wise_conv(x)
        x = self.norm(x)
        x = self.point_wise_conv1(x)
        x = self.act(x)
        x = self.point_wise_conv2(x)
        x = input + self.drop_path(self.layer_scale * x)
        return x


class SwiftFormerEncoderBlock(nn.Module):
    """
    SwiftFormer Encoder Block for SwiftFormer. It consists of (1) Local representation module, (2)
    SwiftFormerEfficientAdditiveAttention, and (3) MLP block.

    Input: tensor of shape `[batch_size, channels, height, width]`

    Output: tensor of shape `[batch_size, channels,height, width]`
    """

    def __init__(self, config: SwiftFormerConfig, dim: int, drop_path: float = 0.0) -> None:
        super().__init__()

        layer_scale_init_value = config.layer_scale_init_value
        use_layer_scale = config.use_layer_scale

        self.local_representation = SwiftFormerLocalRepresentation(config, dim=dim)
        self.attn = SwiftFormerEfficientAdditiveAttention(config, dim=dim)
        self.linear = SwiftFormerMlp(config, in_features=dim)
        self.drop_path = SwiftFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True
            )
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True
            )

    def forward(self, x):
        x = self.local_representation(x)
        batch_size, channels, height, width = x.shape
        if self.use_layer_scale:
            x = x + self.drop_path(
                self.layer_scale_1
                * self.attn(x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels))
                .reshape(batch_size, height, width, channels)
                .permute(0, 3, 1, 2)
            )
            x = x + self.drop_path(self.layer_scale_2 * self.linear(x))

        else:
            x = x + self.drop_path(
                self.attn(x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels))
                .reshape(batch_size, height, width, channels)
                .permute(0, 3, 1, 2)
            )
            x = x + self.drop_path(self.linear(x))
        return x


class SwiftFormerStage(nn.Module):
    """
    A Swiftformer stage consisting of a series of `SwiftFormerConvEncoder` blocks and a final
    `SwiftFormerEncoderBlock`.

    Input: tensor in shape `[batch_size, channels, height, width]`

    Output: tensor in shape `[batch_size, channels, height, width]`
    """

    def __init__(self, config: SwiftFormerConfig, index: int) -> None:
        super().__init__()

        layer_depths = config.depths
        dim = config.embed_dims[index]
        depth = layer_depths[index]

        blocks = []
        for block_idx in range(depth):
            block_dpr = config.drop_path_rate * (block_idx + sum(layer_depths[:index])) / (sum(layer_depths) - 1)

            if depth - block_idx <= 1:
                blocks.append(SwiftFormerEncoderBlock(config, dim=dim, drop_path=block_dpr))
            else:
                blocks.append(SwiftFormerConvEncoder(config, dim=dim))

        self.blocks = nn.ModuleList(blocks)

    def forward(self, input):
        for block in self.blocks:
            input = block(input)
        return input


class SwiftFormerEncoder(nn.Module):
    def __init__(self, config: SwiftFormerConfig) -> None:
        super().__init__()
        self.config = config

        embed_dims = config.embed_dims
        downsamples = config.downsamples
        layer_depths = config.depths

        # Transformer model
        network = []
        for i in range(len(layer_depths)):
            stage = SwiftFormerStage(config=config, index=i)
            network.append(stage)
            if i >= len(layer_depths) - 1:
                break
            if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
                # downsampling between two stages
                network.append(SwiftFormerEmbeddings(config, index=i))
        self.network = nn.ModuleList(network)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithNoAttention]:
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        all_hidden_states = (hidden_states,) if output_hidden_states else None

        for block in self.network:
            hidden_states = block(hidden_states)
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)

        return BaseModelOutputWithNoAttention(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
        )


class SwiftFormerPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = SwiftFormerConfig
    base_model_prefix = "swiftformer"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Conv2d, nn.Linear)):
            nn.init.trunc_normal_(module.weight, std=0.02)
            if module.bias is not None:
                nn.init.constant_(module.bias, 0)
        elif isinstance(module, (nn.LayerNorm)):
            nn.init.constant_(module.bias, 0)
            nn.init.constant_(module.weight, 1.0)


SWIFTFORMER_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`SwiftFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

SWIFTFORMER_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
            for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare SwiftFormer Model transformer outputting raw hidden-states without any specific head on top.",
    SWIFTFORMER_START_DOCSTRING,
)
class SwiftFormerModel(SwiftFormerPreTrainedModel):
    def __init__(self, config: SwiftFormerConfig):
        super().__init__(config)
        self.config = config

        self.patch_embed = SwiftFormerPatchEmbedding(config)
        self.encoder = SwiftFormerEncoder(config)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(SWIFTFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithNoAttention,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
        r""" """

        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        embedding_output = self.patch_embed(pixel_values)
        encoder_outputs = self.encoder(
            embedding_output,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return tuple(v for v in encoder_outputs if v is not None)

        return BaseModelOutputWithNoAttention(
            last_hidden_state=encoder_outputs.last_hidden_state,
            hidden_states=encoder_outputs.hidden_states,
        )


@add_start_docstrings(
    """
    SwiftFormer Model transformer with an image classification head on top (e.g. for ImageNet).
    """,
    SWIFTFORMER_START_DOCSTRING,
)
class SwiftFormerForImageClassification(SwiftFormerPreTrainedModel):
    def __init__(self, config: SwiftFormerConfig) -> None:
        super().__init__(config)

        embed_dims = config.embed_dims

        self.num_labels = config.num_labels
        self.swiftformer = SwiftFormerModel(config)

        # Classifier head
        self.norm = nn.BatchNorm2d(embed_dims[-1], eps=config.batch_norm_eps)
        self.head = nn.Linear(embed_dims[-1], self.num_labels) if self.num_labels > 0 else nn.Identity()
        self.dist_head = nn.Linear(embed_dims[-1], self.num_labels) if self.num_labels > 0 else nn.Identity()

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(SWIFTFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=ImageClassifierOutputWithNoAttention,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # run base model
        outputs = self.swiftformer(
            pixel_values,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs.last_hidden_state if return_dict else outputs[0]

        # run classification head
        sequence_output = self.norm(sequence_output)
        sequence_output = sequence_output.flatten(2).mean(-1)
        cls_out = self.head(sequence_output)
        distillation_out = self.dist_head(sequence_output)
        logits = (cls_out + distillation_out) / 2

        # calculate loss
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
        )
