# coding=utf-8
# Copyright 2023 Google Research, Inc. 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
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""" PyTorch EfficientNet model."""


import math
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 ACT2FN
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    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_efficientnet import EfficientNetConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "EfficientNetConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "google/efficientnet-b7"
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "google/efficientnet-b7"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


from ..deprecated._archive_maps import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa: F401, E402


EFFICIENTNET_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 ([`EfficientNetConfig`]): 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.
"""

EFFICIENTNET_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
            [`AutoImageProcessor.__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.
"""


def round_filters(config: EfficientNetConfig, num_channels: int):
    r"""
    Round number of filters based on depth multiplier.
    """
    divisor = config.depth_divisor
    num_channels *= config.width_coefficient
    new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)

    # Make sure that round down does not go down by more than 10%.
    if new_dim < 0.9 * num_channels:
        new_dim += divisor

    return int(new_dim)


def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True):
    r"""
    Utility function to get the tuple padding value for the depthwise convolution.

    Args:
        kernel_size (`int` or `tuple`):
            Kernel size of the convolution layers.
        adjust (`bool`, *optional*, defaults to `True`):
            Adjusts padding value to apply to right and bottom sides of the input.
    """
    if isinstance(kernel_size, int):
        kernel_size = (kernel_size, kernel_size)

    correct = (kernel_size[0] // 2, kernel_size[1] // 2)
    if adjust:
        return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
    else:
        return (correct[1], correct[1], correct[0], correct[0])


class EfficientNetEmbeddings(nn.Module):
    r"""
    A module that corresponds to the stem module of the original work.
    """

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

        self.out_dim = round_filters(config, 32)
        self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
        self.convolution = nn.Conv2d(
            config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False
        )
        self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
        self.activation = ACT2FN[config.hidden_act]

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        features = self.padding(pixel_values)
        features = self.convolution(features)
        features = self.batchnorm(features)
        features = self.activation(features)

        return features


class EfficientNetDepthwiseConv2d(nn.Conv2d):
    def __init__(
        self,
        in_channels,
        depth_multiplier=1,
        kernel_size=3,
        stride=1,
        padding=0,
        dilation=1,
        bias=True,
        padding_mode="zeros",
    ):
        out_channels = in_channels * depth_multiplier
        super().__init__(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=in_channels,
            bias=bias,
            padding_mode=padding_mode,
        )


class EfficientNetExpansionLayer(nn.Module):
    r"""
    This corresponds to the expansion phase of each block in the original implementation.
    """

    def __init__(self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int):
        super().__init__()
        self.expand_conv = nn.Conv2d(
            in_channels=in_dim,
            out_channels=out_dim,
            kernel_size=1,
            padding="same",
            bias=False,
        )
        self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
        self.expand_act = ACT2FN[config.hidden_act]

    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        # Expand phase
        hidden_states = self.expand_conv(hidden_states)
        hidden_states = self.expand_bn(hidden_states)
        hidden_states = self.expand_act(hidden_states)

        return hidden_states


class EfficientNetDepthwiseLayer(nn.Module):
    r"""
    This corresponds to the depthwise convolution phase of each block in the original implementation.
    """

    def __init__(
        self,
        config: EfficientNetConfig,
        in_dim: int,
        stride: int,
        kernel_size: int,
        adjust_padding: bool,
    ):
        super().__init__()
        self.stride = stride
        conv_pad = "valid" if self.stride == 2 else "same"
        padding = correct_pad(kernel_size, adjust=adjust_padding)

        self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
        self.depthwise_conv = EfficientNetDepthwiseConv2d(
            in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False
        )
        self.depthwise_norm = nn.BatchNorm2d(
            num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
        )
        self.depthwise_act = ACT2FN[config.hidden_act]

    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        # Depthwise convolution
        if self.stride == 2:
            hidden_states = self.depthwise_conv_pad(hidden_states)

        hidden_states = self.depthwise_conv(hidden_states)
        hidden_states = self.depthwise_norm(hidden_states)
        hidden_states = self.depthwise_act(hidden_states)

        return hidden_states


class EfficientNetSqueezeExciteLayer(nn.Module):
    r"""
    This corresponds to the Squeeze and Excitement phase of each block in the original implementation.
    """

    def __init__(self, config: EfficientNetConfig, in_dim: int, expand_dim: int, expand: bool = False):
        super().__init__()
        self.dim = expand_dim if expand else in_dim
        self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))

        self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
        self.reduce = nn.Conv2d(
            in_channels=self.dim,
            out_channels=self.dim_se,
            kernel_size=1,
            padding="same",
        )
        self.expand = nn.Conv2d(
            in_channels=self.dim_se,
            out_channels=self.dim,
            kernel_size=1,
            padding="same",
        )
        self.act_reduce = ACT2FN[config.hidden_act]
        self.act_expand = nn.Sigmoid()

    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        inputs = hidden_states
        hidden_states = self.squeeze(hidden_states)
        hidden_states = self.reduce(hidden_states)
        hidden_states = self.act_reduce(hidden_states)

        hidden_states = self.expand(hidden_states)
        hidden_states = self.act_expand(hidden_states)
        hidden_states = torch.mul(inputs, hidden_states)

        return hidden_states


class EfficientNetFinalBlockLayer(nn.Module):
    r"""
    This corresponds to the final phase of each block in the original implementation.
    """

    def __init__(
        self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool
    ):
        super().__init__()
        self.apply_dropout = stride == 1 and not id_skip
        self.project_conv = nn.Conv2d(
            in_channels=in_dim,
            out_channels=out_dim,
            kernel_size=1,
            padding="same",
            bias=False,
        )
        self.project_bn = nn.BatchNorm2d(
            num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
        )
        self.dropout = nn.Dropout(p=drop_rate)

    def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
        hidden_states = self.project_conv(hidden_states)
        hidden_states = self.project_bn(hidden_states)

        if self.apply_dropout:
            hidden_states = self.dropout(hidden_states)
            hidden_states = hidden_states + embeddings

        return hidden_states


class EfficientNetBlock(nn.Module):
    r"""
    This corresponds to the expansion and depthwise convolution phase of each block in the original implementation.

    Args:
        config ([`EfficientNetConfig`]):
            Model configuration class.
        in_dim (`int`):
            Number of input channels.
        out_dim (`int`):
            Number of output channels.
        stride (`int`):
            Stride size to be used in convolution layers.
        expand_ratio (`int`):
            Expand ratio to set the output dimensions for the expansion and squeeze-excite layers.
        kernel_size (`int`):
            Kernel size for the depthwise convolution layer.
        drop_rate (`float`):
            Dropout rate to be used in the final phase of each block.
        id_skip (`bool`):
            Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase
            of each block. Set to `True` for the first block of each stage.
        adjust_padding (`bool`):
            Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution
            operation, set to `True` for inputs with odd input sizes.
    """

    def __init__(
        self,
        config: EfficientNetConfig,
        in_dim: int,
        out_dim: int,
        stride: int,
        expand_ratio: int,
        kernel_size: int,
        drop_rate: float,
        id_skip: bool,
        adjust_padding: bool,
    ):
        super().__init__()
        self.expand_ratio = expand_ratio
        self.expand = True if self.expand_ratio != 1 else False
        expand_in_dim = in_dim * expand_ratio

        if self.expand:
            self.expansion = EfficientNetExpansionLayer(
                config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride
            )

        self.depthwise_conv = EfficientNetDepthwiseLayer(
            config=config,
            in_dim=expand_in_dim if self.expand else in_dim,
            stride=stride,
            kernel_size=kernel_size,
            adjust_padding=adjust_padding,
        )
        self.squeeze_excite = EfficientNetSqueezeExciteLayer(
            config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand
        )
        self.projection = EfficientNetFinalBlockLayer(
            config=config,
            in_dim=expand_in_dim if self.expand else in_dim,
            out_dim=out_dim,
            stride=stride,
            drop_rate=drop_rate,
            id_skip=id_skip,
        )

    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        embeddings = hidden_states
        # Expansion and depthwise convolution phase
        if self.expand_ratio != 1:
            hidden_states = self.expansion(hidden_states)
        hidden_states = self.depthwise_conv(hidden_states)

        # Squeeze and excite phase
        hidden_states = self.squeeze_excite(hidden_states)
        hidden_states = self.projection(embeddings, hidden_states)
        return hidden_states


class EfficientNetEncoder(nn.Module):
    r"""
    Forward propogates the embeddings through each EfficientNet block.

    Args:
        config ([`EfficientNetConfig`]):
            Model configuration class.
    """

    def __init__(self, config: EfficientNetConfig):
        super().__init__()
        self.config = config
        self.depth_coefficient = config.depth_coefficient

        def round_repeats(repeats):
            # Round number of block repeats based on depth multiplier.
            return int(math.ceil(self.depth_coefficient * repeats))

        num_base_blocks = len(config.in_channels)
        num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)

        curr_block_num = 0
        blocks = []
        for i in range(num_base_blocks):
            in_dim = round_filters(config, config.in_channels[i])
            out_dim = round_filters(config, config.out_channels[i])
            stride = config.strides[i]
            kernel_size = config.kernel_sizes[i]
            expand_ratio = config.expand_ratios[i]

            for j in range(round_repeats(config.num_block_repeats[i])):
                id_skip = True if j == 0 else False
                stride = 1 if j > 0 else stride
                in_dim = out_dim if j > 0 else in_dim
                adjust_padding = False if curr_block_num in config.depthwise_padding else True
                drop_rate = config.drop_connect_rate * curr_block_num / num_blocks

                block = EfficientNetBlock(
                    config=config,
                    in_dim=in_dim,
                    out_dim=out_dim,
                    stride=stride,
                    kernel_size=kernel_size,
                    expand_ratio=expand_ratio,
                    drop_rate=drop_rate,
                    id_skip=id_skip,
                    adjust_padding=adjust_padding,
                )
                blocks.append(block)
                curr_block_num += 1

        self.blocks = nn.ModuleList(blocks)
        self.top_conv = nn.Conv2d(
            in_channels=out_dim,
            out_channels=round_filters(config, 1280),
            kernel_size=1,
            padding="same",
            bias=False,
        )
        self.top_bn = nn.BatchNorm2d(
            num_features=config.hidden_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
        )
        self.top_activation = ACT2FN[config.hidden_act]

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = True,
    ) -> BaseModelOutputWithNoAttention:
        all_hidden_states = (hidden_states,) if output_hidden_states else None

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

        hidden_states = self.top_conv(hidden_states)
        hidden_states = self.top_bn(hidden_states)
        hidden_states = self.top_activation(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 EfficientNetPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = EfficientNetConfig
    base_model_prefix = "efficientnet"
    main_input_name = "pixel_values"
    _no_split_modules = []

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


@add_start_docstrings(
    "The bare EfficientNet model outputting raw features without any specific head on top.",
    EFFICIENTNET_START_DOCSTRING,
)
class EfficientNetModel(EfficientNetPreTrainedModel):
    def __init__(self, config: EfficientNetConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = EfficientNetEmbeddings(config)
        self.encoder = EfficientNetEncoder(config)

        # Final pooling layer
        if config.pooling_type == "mean":
            self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True)
        elif config.pooling_type == "max":
            self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True)
        else:
            raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}")

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

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

        encoder_outputs = self.encoder(
            embedding_output,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        # Apply pooling
        last_hidden_state = encoder_outputs[0]
        pooled_output = self.pooler(last_hidden_state)
        # Reshape (batch_size, 1280, 1 , 1) -> (batch_size, 1280)
        pooled_output = pooled_output.reshape(pooled_output.shape[:2])

        if not return_dict:
            return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
        )


@add_start_docstrings(
    """
    EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g.
    for ImageNet.
    """,
    EFFICIENTNET_START_DOCSTRING,
)
class EfficientNetForImageClassification(EfficientNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.efficientnet = EfficientNetModel(config)
        # Classifier head
        self.dropout = nn.Dropout(p=config.dropout_rate)
        self.classifier = nn.Linear(config.hidden_dim, 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(EFFICIENTNET_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: torch.FloatTensor = None,
        labels: Optional[torch.LongTensor] = 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

        outputs = self.efficientnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)

        pooled_output = outputs.pooler_output if return_dict else outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        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[2:]
            return ((loss,) + output) if loss is not None else output

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