"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   :      List[Any]							=logging.get_logger(__name__)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->List[Any]:
    _lowerCamelCase	: int       =       OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('''module.encoder''' ):
            _lowerCamelCase	: Optional[Any]       =       key.replace('''module.encoder'''						,	'''glpn.encoder''' )
        if key.startswith('''module.decoder''' ):
            _lowerCamelCase	: List[str]       =       key.replace('''module.decoder'''						,	'''decoder.stages''' )
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            _lowerCamelCase	: Optional[Any]       =       key[key.find('''patch_embed''' ) + len('''patch_embed''' )]
            _lowerCamelCase	: Dict       =       key.replace(F'''patch_embed{idx}'''						,	F'''patch_embeddings.{int(A__ )-1}''' )
        if "norm" in key:
            _lowerCamelCase	: str       =       key.replace('''norm'''						,	'''layer_norm''' )
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            _lowerCamelCase	: Dict       =       key[key.find('''glpn.encoder.layer_norm''' ) + len('''glpn.encoder.layer_norm''' )]
            _lowerCamelCase	: Optional[int]       =       key.replace(F'''layer_norm{idx}'''						,	F'''layer_norm.{int(A__ )-1}''' )
        if "layer_norm1" in key:
            _lowerCamelCase	: Tuple       =       key.replace('''layer_norm1'''						,	'''layer_norm_1''' )
        if "layer_norm2" in key:
            _lowerCamelCase	: List[Any]       =       key.replace('''layer_norm2'''						,	'''layer_norm_2''' )
        if "block" in key:
            # replace for example block1 by block.0
            _lowerCamelCase	: List[str]       =       key[key.find('''block''' ) + len('''block''' )]
            _lowerCamelCase	: Optional[Any]       =       key.replace(F'''block{idx}'''						,	F'''block.{int(A__ )-1}''' )
        if "attn.q" in key:
            _lowerCamelCase	: List[str]       =       key.replace('''attn.q'''						,	'''attention.self.query''' )
        if "attn.proj" in key:
            _lowerCamelCase	: str       =       key.replace('''attn.proj'''						,	'''attention.output.dense''' )
        if "attn" in key:
            _lowerCamelCase	: Dict       =       key.replace('''attn'''						,	'''attention.self''' )
        if "fc1" in key:
            _lowerCamelCase	: str       =       key.replace('''fc1'''						,	'''dense1''' )
        if "fc2" in key:
            _lowerCamelCase	: List[Any]       =       key.replace('''fc2'''						,	'''dense2''' )
        if "linear_pred" in key:
            _lowerCamelCase	: Optional[int]       =       key.replace('''linear_pred'''						,	'''classifier''' )
        if "linear_fuse" in key:
            _lowerCamelCase	: Dict       =       key.replace('''linear_fuse.conv'''						,	'''linear_fuse''' )
            _lowerCamelCase	: int       =       key.replace('''linear_fuse.bn'''						,	'''batch_norm''' )
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            _lowerCamelCase	: List[Any]       =       key[key.find('''linear_c''' ) + len('''linear_c''' )]
            _lowerCamelCase	: Tuple       =       key.replace(F'''linear_c{idx}'''						,	F'''linear_c.{int(A__ )-1}''' )
        if "bot_conv" in key:
            _lowerCamelCase	: Any       =       key.replace('''bot_conv'''						,	'''0.convolution''' )
        if "skip_conv1" in key:
            _lowerCamelCase	: str       =       key.replace('''skip_conv1'''						,	'''1.convolution''' )
        if "skip_conv2" in key:
            _lowerCamelCase	: Optional[Any]       =       key.replace('''skip_conv2'''						,	'''2.convolution''' )
        if "fusion1" in key:
            _lowerCamelCase	: Optional[int]       =       key.replace('''fusion1'''						,	'''1.fusion''' )
        if "fusion2" in key:
            _lowerCamelCase	: List[Any]       =       key.replace('''fusion2'''						,	'''2.fusion''' )
        if "fusion3" in key:
            _lowerCamelCase	: Optional[int]       =       key.replace('''fusion3'''						,	'''3.fusion''' )
        if "fusion" in key and "conv" in key:
            _lowerCamelCase	: Dict       =       key.replace('''conv'''						,	'''convolutional_layer''' )
        if key.startswith('''module.last_layer_depth''' ):
            _lowerCamelCase	: Optional[int]       =       key.replace('''module.last_layer_depth'''						,	'''head.head''' )
        _lowerCamelCase	: Union[str, Any]       =       value

    return new_state_dict



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->str:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks ):
        for j in range(config.depths[i] ):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            _lowerCamelCase	: int       =       state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
            _lowerCamelCase	: Tuple       =       state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
            # next, add keys and values (in that order) to the state dict
            _lowerCamelCase	: int       =       kv_weight[
                : config.hidden_sizes[i], :
            ]
            _lowerCamelCase	: Any       =       kv_bias[: config.hidden_sizes[i]]
            _lowerCamelCase	: Tuple       =       kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _lowerCamelCase	: int       =       kv_bias[config.hidden_sizes[i] :]



def 					UpperCamelCase					(       )      ->int:
    _lowerCamelCase	: Optional[int]       =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _lowerCamelCase	: Optional[Any]       =       Image.open(requests.get(A__						,	stream=A__ ).raw )

    return image



@torch.no_grad()
def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_=False						,	SCREAMING_SNAKE_CASE_=None )      ->Tuple:
    _lowerCamelCase	: Tuple       =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,	decoder_hidden_size=64						,	depths=[3, 8, 27, 3] )

    # load image processor (only resize + rescale)
    _lowerCamelCase	: int       =       GLPNImageProcessor()

    # prepare image
    _lowerCamelCase	: Any       =       prepare_img()
    _lowerCamelCase	: Union[str, Any]       =       image_processor(images=A__						,	return_tensors='''pt''' ).pixel_values

    logger.info('''Converting model...''' )

    # load original state dict
    _lowerCamelCase	: Dict       =       torch.load(A__						,	map_location=torch.device('''cpu''' ) )

    # rename keys
    _lowerCamelCase	: Optional[int]       =       rename_keys(A__ )

    # key and value matrices need special treatment
    read_in_k_v(A__						,	A__ )

    # create HuggingFace model and load state dict
    _lowerCamelCase	: Union[str, Any]       =       GLPNForDepthEstimation(A__ )
    model.load_state_dict(A__ )
    model.eval()

    # forward pass
    _lowerCamelCase	: List[str]       =       model(A__ )
    _lowerCamelCase	: List[Any]       =       outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            _lowerCamelCase	: Dict       =       torch.tensor(
                [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
        elif "kitti" in model_name:
            _lowerCamelCase	: Optional[Any]       =       torch.tensor(
                [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
        else:
            raise ValueError(F'''Unknown model name: {model_name}''' )

        _lowerCamelCase	: str       =       torch.Size([1, 480, 640] )

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]						,	A__						,	atol=1e-4 )
        print('''Looks ok!''' )

    # finally, push to hub if required
    if push_to_hub:
        logger.info('''Pushing model and image processor to the hub...''' )
        model.push_to_hub(
            repo_path_or_name=Path(A__						,	A__ )						,	organization='''nielsr'''						,	commit_message='''Add model'''						,	use_temp_dir=A__						,	)
        image_processor.push_to_hub(
            repo_path_or_name=Path(A__						,	A__ )						,	organization='''nielsr'''						,	commit_message='''Add image processor'''						,	use_temp_dir=A__						,	)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__   :      List[str]							=argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path',
      default=None,
      type=str,
      help='Path to the original PyTorch checkpoint (.pth file).',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
  )
  parser.add_argument(
      '--model_name',
      default='glpn-kitti',
      type=str,
      help='Name of the model in case you\'re pushing to the hub.',
  )
  SCREAMING_SNAKE_CASE__   :      Dict							=parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




'''simple docstring'''
from typing import Optional

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

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    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,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowercase__											=logging.get_logger(__name__)

# General docstring
lowercase__											='ResNetConfig'

# Base docstring
lowercase__											='microsoft/resnet-50'
lowercase__											=[1, 20_48, 7, 7]

# Image classification docstring
lowercase__											='microsoft/resnet-50'
lowercase__											='tiger cat'

lowercase__											=[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 3					,   UpperCAmelCase = 1					,   UpperCAmelCase = "relu"	):
				super().__init__()
				a_      				=						nn.Convad(
				    UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=UpperCAmelCase					,   stride=UpperCAmelCase					,   padding=kernel_size // 2					,   bias=UpperCAmelCase	)
				a_      				=						nn.BatchNormad(UpperCAmelCase	)
				a_      				=						ACTaFN[activation] if activation is not None else nn.Identity()






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.convolution(UpperCAmelCase	)
				a_      				=						self.normalization(UpperCAmelCase	)
				a_      				=						self.activation(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__()
				a_      				=						ResNetConvLayer(
				    config.num_channels					,   config.embedding_size					,   kernel_size=7					,   stride=2					,   activation=config.hidden_act	)
				a_      				=						nn.MaxPoolad(kernel_size=3					,   stride=2					,   padding=1	)
				a_      				=						config.num_channels






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						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."""	)
				a_      				=						self.embedder(UpperCAmelCase	)
				a_      				=						self.pooler(UpperCAmelCase	)
				return embedding







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 2	):
				super().__init__()
				a_      				=						nn.Convad(UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=1					,   stride=UpperCAmelCase					,   bias=UpperCAmelCase	)
				a_      				=						nn.BatchNormad(UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.convolution(UpperCAmelCase	)
				a_      				=						self.normalization(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 1					,   UpperCAmelCase = "relu"	):
				super().__init__()
				a_      				=						in_channels != out_channels or stride != 1
				a_      				=						(
				    ResNetShortCut(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	) if should_apply_shortcut else nn.Identity()
				)
				a_      				=						nn.Sequential(
				    ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	)					,   ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   activation=UpperCAmelCase	)					,   )
				a_      				=						ACTaFN[activation]






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						hidden_state
				a_      				=						self.layer(UpperCAmelCase	)
				a_      				=						self.shortcut(UpperCAmelCase	)
				hidden_state += residual
				a_      				=						self.activation(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 1					,   UpperCAmelCase = "relu"					,   UpperCAmelCase = 4	):
				super().__init__()
				a_      				=						in_channels != out_channels or stride != 1
				a_      				=						out_channels // reduction
				a_      				=						(
				    ResNetShortCut(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	) if should_apply_shortcut else nn.Identity()
				)
				a_      				=						nn.Sequential(
				    ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=1	)					,   ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	)					,   ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=1					,   activation=UpperCAmelCase	)					,   )
				a_      				=						ACTaFN[activation]






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						hidden_state
				a_      				=						self.layer(UpperCAmelCase	)
				a_      				=						self.shortcut(UpperCAmelCase	)
				hidden_state += residual
				a_      				=						self.activation(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 2					,   UpperCAmelCase = 2					,   ):
				super().__init__()

				a_      				=						ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

				a_      				=						nn.Sequential(
				    # downsampling is done in the first layer with stride of 2
				    layer(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase					,   activation=config.hidden_act	)					,   *[layer(UpperCAmelCase					,   UpperCAmelCase					,   activation=config.hidden_act	) for _ in range(depth - 1	)]					,   )






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						input
				for layer in self.layers:
					a_      				=						layer(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__()
				a_      				=						nn.ModuleList([]	)
				# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
				self.stages.append(
				    ResNetStage(
				        UpperCAmelCase					,   config.embedding_size					,   config.hidden_sizes[0]					,   stride=2 if config.downsample_in_first_stage else 1					,   depth=config.depths[0]					,   )	)
				a_      				=						zip(config.hidden_sizes					,   config.hidden_sizes[1:]	)
				for (in_channels, out_channels), depth in zip(UpperCAmelCase					,   config.depths[1:]	):
					self.stages.append(ResNetStage(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   depth=UpperCAmelCase	)	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = False					,   UpperCAmelCase = True	):
				a_      				=						() if output_hidden_states else None

				for stage_module in self.stages:
					if output_hidden_states:
						a_      				=						hidden_states + (hidden_state,)

					a_      				=						stage_module(UpperCAmelCase	)

				if output_hidden_states:
					a_      				=						hidden_states + (hidden_state,)

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

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







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			List[Any]       =    ResNetConfig
			lowerCamelCase__	:			Dict       =    'resnet'
			lowerCamelCase__	:			Dict       =    'pixel_values'
			lowerCamelCase__	:			str       =    True




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if isinstance(UpperCAmelCase					,   nn.Convad	):
					nn.init.kaiming_normal_(module.weight					,   mode="""fan_out"""					,   nonlinearity="""relu"""	)
				elif isinstance(UpperCAmelCase					,   (nn.BatchNormad, nn.GroupNorm)	):
					nn.init.constant_(module.weight					,   1	)
					nn.init.constant_(module.bias					,   0	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=False	):
				if isinstance(UpperCAmelCase					,   UpperCAmelCase	):
					a_      				=						value







lowercase__											=r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowercase__											=r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'







@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'		,  UpperCamelCase__		,  )
class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase	)
				a_      				=						config
				a_      				=						ResNetEmbeddings(UpperCAmelCase	)
				a_      				=						ResNetEncoder(UpperCAmelCase	)
				a_      				=						nn.AdaptiveAvgPoolad((1, 1)	)
				# Initialize weights and apply final processing
				self.post_init()






			@add_start_docstrings_to_model_forward(UpperCAmelCase	)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC					,   output_type=UpperCAmelCase					,   config_class=_CONFIG_FOR_DOC					,   modality="""vision"""					,   expected_output=_EXPECTED_OUTPUT_SHAPE					,   )
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = None	):
				a_      				=						(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				a_      				=						return_dict if return_dict is not None else self.config.use_return_dict

				a_      				=						self.embedder(UpperCAmelCase	)

				a_      				=						self.encoder(
				    UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   return_dict=UpperCAmelCase	)

				a_      				=						encoder_outputs[0]

				a_      				=						self.pooler(UpperCAmelCase	)

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

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







@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '		,  UpperCamelCase__		,  )
class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase	)
				a_      				=						config.num_labels
				a_      				=						ResNetModel(UpperCAmelCase	)
				# classification head
				a_      				=						nn.Sequential(
				    nn.Flatten()					,   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(UpperCAmelCase	)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT					,   output_type=UpperCAmelCase					,   config_class=_CONFIG_FOR_DOC					,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,   )
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   ):
				a_      				=						return_dict if return_dict is not None else self.config.use_return_dict

				a_      				=						self.resnet(UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   return_dict=UpperCAmelCase	)

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

				a_      				=						self.classifier(UpperCAmelCase	)

				a_      				=						None

				if labels is not None:
					if self.config.problem_type is None:
						if self.num_labels == 1:
							a_      				=						"""regression"""
						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
							a_      				=						"""single_label_classification"""
						else:
							a_      				=						"""multi_label_classification"""
					if self.config.problem_type == "regression":
						a_      				=						MSELoss()
						if self.num_labels == 1:
							a_      				=						loss_fct(logits.squeeze()					,   labels.squeeze()	)
						else:
							a_      				=						loss_fct(UpperCAmelCase					,   UpperCAmelCase	)
					elif self.config.problem_type == "single_label_classification":
						a_      				=						CrossEntropyLoss()
						a_      				=						loss_fct(logits.view(-1					,   self.num_labels	)					,   labels.view(-1	)	)
					elif self.config.problem_type == "multi_label_classification":
						a_      				=						BCEWithLogitsLoss()
						a_      				=						loss_fct(UpperCAmelCase					,   UpperCAmelCase	)

				if not return_dict:
					a_      				=						(logits,) + outputs[2:]
					return (loss,) + output if loss is not None else output

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







@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '		,  UpperCamelCase__		,  )
class   a_ (      UpperCamelCase__		,  UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase	)
				super()._init_backbone(UpperCAmelCase	)

				a_      				=						[config.embedding_size] + config.hidden_sizes
				a_      				=						ResNetEmbeddings(UpperCAmelCase	)
				a_      				=						ResNetEncoder(UpperCAmelCase	)

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






			@add_start_docstrings_to_model_forward(UpperCAmelCase	)
			@replace_return_docstrings(output_type=UpperCAmelCase					,   config_class=_CONFIG_FOR_DOC	)
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = None	):
				a_      				=						return_dict if return_dict is not None else self.config.use_return_dict
				a_      				=						(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)

				a_      				=						self.embedder(UpperCAmelCase	)

				a_      				=						self.encoder(UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   return_dict=UpperCAmelCase	)

				a_      				=						outputs.hidden_states

				a_      				=						()
				for idx, stage in enumerate(self.stage_names	):
					if stage in self.out_features:
						feature_maps += (hidden_states[idx],)

				if not return_dict:
					a_      				=						(feature_maps,)
					if output_hidden_states:
						output += (outputs.hidden_states,)
					return output

				return BackboneOutput(
				    feature_maps=UpperCAmelCase					,   hidden_states=outputs.hidden_states if output_hidden_states else None					,   attentions=UpperCAmelCase					,   )
