





import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


UpperCAmelCase_ 							=       """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

UpperCAmelCase_ 							=       """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

UpperCAmelCase_ 							=       """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,					_KWARGS_DESCRIPTION   )
class      UpperCamelCase_						(					datasets.Metric   ):




					def 		lowerCAmelCase     (						self					)						->							str:
										if version.parse(scb.__version__					) < version.parse('1.4.12'					):
															raise ImportWarning(
															    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
															    'You can install it with `pip install "sacrebleu>=1.4.12"`.'					)
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,  citation=_CITATION			,  homepage='http://www.cs.umd.edu/~snover/tercom/'			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
										        {
										            'predictions': datasets.Value('string'			,  id='sequence'					),
										            'references': datasets.Sequence(datasets.Value('string'			,  id='sequence'					)			,  id='references'					),
										        }					)			,  codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']			,  reference_urls=[
										        'https://github.com/jhclark/tercom',
										    ]			,  )






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = False			,  )						->							Optional[Any]:
										_snake_case													=			len(references[0]					)
										if any(len(lowerCAmelCase_					) != references_per_prediction for refs in references					):
															raise ValueError('Sacrebleu requires the same number of references for each prediction'					)
										_snake_case													=			[[refs[i] for refs in references] for i in range(lowerCAmelCase_					)]

										_snake_case													=			TER(
										    normalized=lowerCAmelCase_			,  no_punct=lowerCAmelCase_			,  asian_support=lowerCAmelCase_			,  case_sensitive=lowerCAmelCase_			,  )
										_snake_case													=			sb_ter.corpus_score(lowerCAmelCase_			,  lowerCAmelCase_					)

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}










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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

# General docstring
UpperCAmelCase_ 							=       """ResNetConfig"""

# Base docstring
UpperCAmelCase_ 							=       """microsoft/resnet-50"""
UpperCAmelCase_ 							=       [1, 2048, 7, 7]

# Image classification docstring
UpperCAmelCase_ 							=       """microsoft/resnet-50"""
UpperCAmelCase_ 							=       """tiger cat"""

UpperCAmelCase_ 							=       [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = "relu"					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Convad(
										    lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=lowerCAmelCase_			,  stride=lowerCAmelCase_			,  padding=kernel_size // 2			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.BatchNormad(lowerCAmelCase_					)
										_snake_case													=			ACTaFN[activation] if activation is not None else nn.Identity()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			self.convolution(lowerCAmelCase_					)
										_snake_case													=			self.normalization(lowerCAmelCase_					)
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							Dict:
										super().__init__()
										_snake_case													=			ResNetConvLayer(
										    config.num_channels			,  config.embedding_size			,  kernel_size=7			,  stride=2			,  activation=config.hidden_act					)
										_snake_case													=			nn.MaxPoolad(kernel_size=3			,  stride=2			,  padding=1					)
										_snake_case													=			config.num_channels






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			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.'					)
										_snake_case													=			self.embedder(lowerCAmelCase_					)
										_snake_case													=			self.pooler(lowerCAmelCase_					)
										return embedding





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 2					)						->							List[Any]:
										super().__init__()
										_snake_case													=			nn.Convad(lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=1			,  stride=lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.BatchNormad(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			self.convolution(lowerCAmelCase_					)
										_snake_case													=			self.normalization(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							str:
										_snake_case													=			hidden_state
										_snake_case													=			self.layer(lowerCAmelCase_					)
										_snake_case													=			self.shortcut(lowerCAmelCase_					)
										hidden_state += residual
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			hidden_state
										_snake_case													=			self.layer(lowerCAmelCase_					)
										_snake_case													=			self.shortcut(lowerCAmelCase_					)
										hidden_state += residual
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 2			,  lowerCAmelCase_ = 2			,  )						->							Tuple:
										super().__init__()

										_snake_case													=			ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			input
										for layer in self.layers:
															_snake_case													=			layer(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							int:
										super().__init__()
										_snake_case													=			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(
										        lowerCAmelCase_			,  config.embedding_size			,  config.hidden_sizes[0]			,  stride=2 if config.downsample_in_first_stage else 1			,  depth=config.depths[0]			,  )					)
										_snake_case													=			zip(config.hidden_sizes			,  config.hidden_sizes[1:]					)
										for (in_channels, out_channels), depth in zip(lowerCAmelCase_			,  config.depths[1:]					):
															self.stages.append(ResNetStage(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  depth=lowerCAmelCase_					)					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = True					)						->							BaseModelOutputWithNoAttention:
										_snake_case													=			() if output_hidden_states else None

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

															_snake_case													=			stage_module(lowerCAmelCase_					)

										if output_hidden_states:
															_snake_case													=			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=lowerCAmelCase_			,  hidden_states=lowerCAmelCase_			,  )





class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		ResNetConfig
					lowerCAmelCase_							=		'''resnet'''
					lowerCAmelCase_							=		'''pixel_values'''
					lowerCAmelCase_							=		True




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Dict:
										if isinstance(lowerCAmelCase_			,  nn.Convad					):
															nn.init.kaiming_normal_(module.weight			,  mode='fan_out'			,  nonlinearity='relu'					)
										elif isinstance(lowerCAmelCase_			,  (nn.BatchNormad, nn.GroupNorm)					):
															nn.init.constant_(module.weight			,  1					)
															nn.init.constant_(module.bias			,  0					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=False					)						->							List[Any]:
										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			value





UpperCAmelCase_ 							=       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 ([`ResNetConfig`]): 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.
"""

UpperCAmelCase_ 							=       R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__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 ResNet model outputting raw features without any specific head on top.'''							,					_lowerCamelCase							,					)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							int:
										super().__init__(lowerCAmelCase_					)
										_snake_case													=			config
										_snake_case													=			ResNetEmbeddings(lowerCAmelCase_					)
										_snake_case													=			ResNetEncoder(lowerCAmelCase_					)
										_snake_case													=			nn.AdaptiveAvgPoolad((1, 1)					)
										# Initialize weights and apply final processing
										self.post_init()






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC			,  output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC			,  modality='vision'			,  expected_output=_EXPECTED_OUTPUT_SHAPE			,  )
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None					)						->							BaseModelOutputWithPoolingAndNoAttention:
										_snake_case													=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict

										_snake_case													=			self.embedder(lowerCAmelCase_					)

										_snake_case													=			self.encoder(
										    lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			encoder_outputs[0]

										_snake_case													=			self.pooler(lowerCAmelCase_					)

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

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





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




					def __init__(						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__(lowerCAmelCase_					)
										_snake_case													=			config.num_labels
										_snake_case													=			ResNetModel(lowerCAmelCase_					)
										# classification head
										_snake_case													=			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(lowerCAmelCase_					)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT			,  output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC			,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,  )
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  )						->							ImageClassifierOutputWithNoAttention:
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict

										_snake_case													=			self.resnet(lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

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

										_snake_case													=			self.classifier(lowerCAmelCase_					)

										_snake_case													=			None

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

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

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





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''							,					_lowerCamelCase							,					)
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							List[Any]:
										super().__init__(lowerCAmelCase_					)
										super()._init_backbone(lowerCAmelCase_					)

										_snake_case													=			[config.embedding_size] + config.hidden_sizes
										_snake_case													=			ResNetEmbeddings(lowerCAmelCase_					)
										_snake_case													=			ResNetEncoder(lowerCAmelCase_					)

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






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@replace_return_docstrings(output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC					)
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None					)						->							BackboneOutput:
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict
										_snake_case													=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)

										_snake_case													=			self.embedder(lowerCAmelCase_					)

										_snake_case													=			self.encoder(lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			outputs.hidden_states

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

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

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



