# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" PyTorch ConvNext model."""


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_convnext import ConvNextConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "ConvNextConfig"
_FEAT_EXTRACTOR_FOR_DOC = "ConvNextImageProcessor"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/convnext-tiny-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"

CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/convnext-tiny-224",
    # See all ConvNext models at https://huggingface.co/models?filter=convnext
]


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input, drop_prob: float = 0.0, training: bool = False):
    """
    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->ConvNext
class ConvNextDropPath(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 ConvNextLayerNorm(nn.Module):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError(f"Unsupported data format: {self.data_format}")
        self.normalized_shape = (normalized_shape,)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.data_format == "channels_last":
            x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            input_dtype = x.dtype
            x = x.float()
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = x.to(dtype=input_dtype)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x


class ConvNextEmbeddings(nn.Module):
    """This class is comparable to (and inspired by) the SwinEmbeddings class
    found in src/transformers/models/swin/modeling_swin.py.
    """

    def __init__(self, config):
        super().__init__()
        self.patch_embeddings = nn.Conv2d(
            config.num_channels, config.hidden_sizes[0], kernel_size=config.patch_size, stride=config.patch_size
        )
        self.layernorm = ConvNextLayerNorm(config.hidden_sizes[0], eps=1e-6, data_format="channels_first")
        self.num_channels = config.num_channels

    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
        num_channels = pixel_values.shape[1]
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        embeddings = self.patch_embeddings(pixel_values)
        embeddings = self.layernorm(embeddings)
        return embeddings


class ConvNextLayer(nn.Module):
    """This corresponds to the `Block` class in the original implementation.

    There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
    H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back

    The authors used (2) as they find it slightly faster in PyTorch.

    Args:
        config ([`ConvNextConfig`]): Model configuration class.
        dim (`int`): Number of input channels.
        drop_path (`float`): Stochastic depth rate. Default: 0.0.
    """

    def __init__(self, config, dim, drop_path=0):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
        self.layernorm = ConvNextLayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layers
        self.act = ACT2FN[config.hidden_act]
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.layer_scale_parameter = (
            nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            if config.layer_scale_init_value > 0
            else None
        )
        self.drop_path = ConvNextDropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        input = hidden_states
        x = self.dwconv(hidden_states)
        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
        x = self.layernorm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.layer_scale_parameter is not None:
            x = self.layer_scale_parameter * x
        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)
        return x


class ConvNextStage(nn.Module):
    """ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks.

    Args:
        config ([`ConvNextConfig`]): Model configuration class.
        in_channels (`int`): Number of input channels.
        out_channels (`int`): Number of output channels.
        depth (`int`): Number of residual blocks.
        drop_path_rates(`List[float]`): Stochastic depth rates for each layer.
    """

    def __init__(self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None):
        super().__init__()

        if in_channels != out_channels or stride > 1:
            self.downsampling_layer = nn.Sequential(
                ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"),
                nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride),
            )
        else:
            self.downsampling_layer = nn.Identity()
        drop_path_rates = drop_path_rates or [0.0] * depth
        self.layers = nn.Sequential(
            *[ConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j]) for j in range(depth)]
        )

    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        hidden_states = self.downsampling_layer(hidden_states)
        hidden_states = self.layers(hidden_states)
        return hidden_states


class ConvNextEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.stages = nn.ModuleList()
        drop_path_rates = [
            x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)
        ]
        prev_chs = config.hidden_sizes[0]
        for i in range(config.num_stages):
            out_chs = config.hidden_sizes[i]
            stage = ConvNextStage(
                config,
                in_channels=prev_chs,
                out_channels=out_chs,
                stride=2 if i > 0 else 1,
                depth=config.depths[i],
                drop_path_rates=drop_path_rates[i],
            )
            self.stages.append(stage)
            prev_chs = out_chs

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

        for i, layer_module in enumerate(self.stages):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

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

    config_class = ConvNextConfig
    base_model_prefix = "convnext"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    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)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, ConvNextModel):
            module.gradient_checkpointing = value


CONVNEXT_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 ([`ConvNextConfig`]): 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.
"""

CONVNEXT_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.
"""


@add_start_docstrings(
    "The bare ConvNext model outputting raw features without any specific head on top.",
    CONVNEXT_START_DOCSTRING,
)
class ConvNextModel(ConvNextPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.embeddings = ConvNextEmbeddings(config)
        self.encoder = ConvNextEncoder(config)

        # final layernorm layer
        self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)

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

    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        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,
        )

        last_hidden_state = encoder_outputs[0]

        # global average pooling, (N, C, H, W) -> (N, C)
        pooled_output = self.layernorm(last_hidden_state.mean([-2, -1]))

        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(
    """
    ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """,
    CONVNEXT_START_DOCSTRING,
)
class ConvNextForImageClassification(ConvNextPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.convnext = ConvNextModel(config)

        # Classifier head
        self.classifier = (
            nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
        )

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

    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        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.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)

        pooled_output = outputs.pooler_output if return_dict else outputs[1]

        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,
        )
