"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.distilbert.modeling_tf_distilbert import (
				    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDistilBertForMaskedLM,
				    TFDistilBertForMultipleChoice,
				    TFDistilBertForQuestionAnswering,
				    TFDistilBertForSequenceClassification,
				    TFDistilBertForTokenClassification,
				    TFDistilBertModel,
				)
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,)    -> Tuple:
													A__		   =							parent
													A__		   =							13
													A__		   =							7
													A__		   =							True
													A__		   =							True
													A__		   =							False
													A__		   =							True
													A__		   =							99
													A__		   =							32
													A__		   =							2
													A__		   =							4
													A__		   =							37
													A__		   =							'gelu'
													A__		   =							0.1
													A__		   =							0.1
													A__		   =							5_12
													A__		   =							16
													A__		   =							2
													A__		   =							0.0_2
													A__		   =							3
													A__		   =							4
													A__		   =							None





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size   )

													A__		   =							None
													if self.use_input_mask:
																				A__		   =							random_attention_mask([self.batch_size, self.seq_length]   )

													A__		   =							None
													A__		   =							None
													A__		   =							None
													if self.use_labels:
																				A__		   =							ids_tensor([self.batch_size]						,self.type_sequence_label_size   )
																				A__		   =							ids_tensor([self.batch_size, self.seq_length]						,self.num_labels   )
																				A__		   =							ids_tensor([self.batch_size]						,self.num_choices   )

													A__		   =							DistilBertConfig(
													    vocab_size=self.vocab_size						,dim=self.hidden_size						,n_layers=self.num_hidden_layers						,n_heads=self.num_attention_heads						,hidden_dim=self.intermediate_size						,hidden_act=self.hidden_act						,dropout=self.hidden_dropout_prob						,attention_dropout=self.attention_probs_dropout_prob						,max_position_embeddings=self.max_position_embeddings						,initializer_range=self.initializer_range						,)

													return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							TFDistilBertModel(config=__UpperCAmelCase   )
													A__		   =							{'input_ids': input_ids, 'attention_mask': input_mask}

													A__		   =							model(__UpperCAmelCase   )

													A__		   =							[input_ids, input_mask]

													A__		   =							model(__UpperCAmelCase   )

													self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.seq_length, self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							TFDistilBertForMaskedLM(config=__UpperCAmelCase   )
													A__		   =							{'input_ids': input_ids, 'attention_mask': input_mask}
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.vocab_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[str]:
													A__		   =							TFDistilBertForQuestionAnswering(config=__UpperCAmelCase   )
													A__		   =							{
													    'input_ids': input_ids,
													    'attention_mask': input_mask,
													}
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.start_logits.shape						,(self.batch_size, self.seq_length)   )
													self.parent.assertEqual(result.end_logits.shape						,(self.batch_size, self.seq_length)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.num_labels
													A__		   =							TFDistilBertForSequenceClassification(__UpperCAmelCase   )
													A__		   =							{'input_ids': input_ids, 'attention_mask': input_mask}
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							self.num_choices
													A__		   =							TFDistilBertForMultipleChoice(__UpperCAmelCase   )
													A__		   =							tf.tile(tf.expand_dims(__UpperCAmelCase						,1   )						,(1, self.num_choices, 1)   )
													A__		   =							tf.tile(tf.expand_dims(__UpperCAmelCase						,1   )						,(1, self.num_choices, 1)   )
													A__		   =							{
													    'input_ids': multiple_choice_inputs_ids,
													    'attention_mask': multiple_choice_input_mask,
													}
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_choices)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[str]:
													A__		   =							self.num_labels
													A__		   =							TFDistilBertForTokenClassification(__UpperCAmelCase   )
													A__		   =							{'input_ids': input_ids, 'attention_mask': input_mask}
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.num_labels)   )





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							self.prepare_config_and_inputs()
													((A__)   ,     (A__)   ,     (A__)   ,     (A__)   ,     (A__)   ,     (A__))		   =							config_and_inputs
													A__		   =							{'input_ids': input_ids, 'attention_mask': input_mask}
													return config, inputs_dict






@require_tf
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Any  							=						(
						    (
						        TFDistilBertModel,
						        TFDistilBertForMaskedLM,
						        TFDistilBertForQuestionAnswering,
						        TFDistilBertForSequenceClassification,
						        TFDistilBertForTokenClassification,
						        TFDistilBertForMultipleChoice,
						    )
						    if is_tf_available()
						    else None
						)
						lowerCAmelCase__     :  Any  							=						(
						    {
						        'feature-extraction': TFDistilBertModel,
						        'fill-mask': TFDistilBertForMaskedLM,
						        'question-answering': TFDistilBertForQuestionAnswering,
						        'text-classification': TFDistilBertForSequenceClassification,
						        'token-classification': TFDistilBertForTokenClassification,
						        'zero-shot': TFDistilBertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCAmelCase__     :  Any  							=						False
						lowerCAmelCase__     :  Tuple  							=						False





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							TFDistilBertModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,dim=37   )





						def  snake_case__		(				self   )    -> Optional[int]:
													self.config_tester.run_common_tests()





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_model(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_masked_lm(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_question_answering(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_sequence_classification(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_multiple_choice(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_token_classification(*__UpperCAmelCase   )





						@slow
						def  snake_case__		(				self   )    -> int:
													for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]   ):
																				A__		   =							TFDistilBertModel.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )






@require_tf
class 			UpperCamelCase__(	unittest.TestCase       ):





						@slow
						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							TFDistilBertModel.from_pretrained('distilbert-base-uncased'   )
													A__		   =							tf.constant([[0, 1, 2, 3, 4, 5]]   )
													A__		   =							model(__UpperCAmelCase   )[0]

													A__		   =							[1, 6, 7_68]
													self.assertEqual(output.shape						,__UpperCAmelCase   )

													A__		   =							tf.constant(
													    [
													        [
													            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
													            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
													            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
													        ]
													    ]   )
													tf.debugging.assert_near(output[:, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )






"""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


__lowerCamelCase							 =     logging.get_logger(__name__)

# General docstring
__lowerCamelCase							 =     "ResNetConfig"

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

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

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





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 3						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"   )    -> Any:
													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  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							self.convolution(__UpperCAmelCase   )
													A__		   =							self.normalization(__UpperCAmelCase   )
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Any:
													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  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													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 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 2   )    -> Optional[Any]:
													super().__init__()
													A__		   =							nn.Convad(__UpperCAmelCase						,__UpperCAmelCase						,kernel_size=1						,stride=__UpperCAmelCase						,bias=__UpperCAmelCase   )
													A__		   =							nn.BatchNormad(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							self.convolution(__UpperCAmelCase   )
													A__		   =							self.normalization(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"   )    -> int:
													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  snake_case__		(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							hidden_state
													A__		   =							self.layer(__UpperCAmelCase   )
													A__		   =							self.shortcut(__UpperCAmelCase   )
													hidden_state += residual
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"						,__UpperCAmelCase = 4   )    -> int:
													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  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							hidden_state
													A__		   =							self.layer(__UpperCAmelCase   )
													A__		   =							self.shortcut(__UpperCAmelCase   )
													hidden_state += residual
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,)    -> Any:
													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  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							input
													for layer in self.layers:
																				A__		   =							layer(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = False						,__UpperCAmelCase = True   )    -> BaseModelOutputWithNoAttention:
													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 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  str  							=						ResNetConfig
						lowerCAmelCase__     :  str  							=						'resnet'
						lowerCAmelCase__     :  int  							=						'pixel_values'
						lowerCAmelCase__     :  Any  							=						True





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> List[Any]:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Any:
													if isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																				A__		   =							value






__lowerCamelCase							 =     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"

__lowerCamelCase							 =     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.'						,  __A						,  )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None   )    -> BaseModelOutputWithPoolingAndNoAttention:
													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    '						,  __A						,  )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Tuple:
													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  snake_case__		(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,)    -> ImageClassifierOutputWithNoAttention:
													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    '						,  __A						,  )
class 			UpperCamelCase__(	__A						,  __A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None   )    -> BackboneOutput:
													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						,)





