
import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SqueezeBertForMaskedLM,
						    SqueezeBertForMultipleChoice,
						    SqueezeBertForQuestionAnswering,
						    SqueezeBertForSequenceClassification,
						    SqueezeBertForTokenClassification,
						    SqueezeBertModel,
						)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     str=1_3			,				SCREAMING_SNAKE_CASE_    :     Tuple=7			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=9_9			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=3_2			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Dict=6_4			,				SCREAMING_SNAKE_CASE_    :     Any="gelu"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     Any=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0.02			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     Dict=4			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=2			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     Dict=2			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				)			->	Dict:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   is_training
					lowercase_	    =   use_input_mask
					lowercase_	    =   use_token_type_ids
					lowercase_	    =   use_labels
					lowercase_	    =   vocab_size
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   type_vocab_size
					lowercase_	    =   type_sequence_label_size
					lowercase_	    =   initializer_range
					lowercase_	    =   num_labels
					lowercase_	    =   num_choices
					lowercase_	    =   scope
					lowercase_	    =   q_groups
					lowercase_	    =   k_groups
					lowercase_	    =   v_groups
					lowercase_	    =   post_attention_groups
					lowercase_	    =   intermediate_groups
					lowercase_	    =   output_groups
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

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

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

					lowercase_	    =   self.get_config()

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 		_lowercase    (			self    :     List[Any]			)			->	Optional[int]:
					return SqueezeBertConfig(
					    embedding_size=self.hidden_size			,				vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				attention_probs_dropout_prob=self.hidden_dropout_prob			,				attention_dropout=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				initializer_range=self.initializer_range			,				q_groups=self.q_groups			,				k_groups=self.k_groups			,				v_groups=self.v_groups			,				post_attention_groups=self.post_attention_groups			,				intermediate_groups=self.intermediate_groups			,				output_groups=self.output_groups			,				)
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   SqueezeBertModel(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Dict:
					lowercase_	    =   SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[Any]:
					lowercase_	    =   SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				start_positions=SCREAMING_SNAKE_CASE_			,				end_positions=SCREAMING_SNAKE_CASE_			)
					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 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Optional[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Optional[int]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Optional[Any]:
					lowercase_	    =   self.num_choices
					lowercase_	    =   SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   input_ids.unsqueeze(1			).expand(-1			,				self.num_choices			,				-1			).contiguous()
					lowercase_	    =   input_mask.unsqueeze(1			).expand(-1			,				self.num_choices			,				-1			).contiguous()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			,				)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_choices)			)




	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					lowercase_	    =   self.prepare_config_and_inputs()
					((lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_))	    =   config_and_inputs
					lowercase_	    =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

@require_torch
class 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Any       	=      (
	    (
	        SqueezeBertModel,
	        SqueezeBertForMaskedLM,
	        SqueezeBertForMultipleChoice,
	        SqueezeBertForQuestionAnswering,
	        SqueezeBertForSequenceClassification,
	        SqueezeBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	a     :Tuple       	=      (
	    {
	        'feature-extraction': SqueezeBertModel,
	        'fill-mask': SqueezeBertForMaskedLM,
	        'question-answering': SqueezeBertForQuestionAnswering,
	        'text-classification': SqueezeBertForSequenceClassification,
	        'token-classification': SqueezeBertForTokenClassification,
	        'zero-shot': SqueezeBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a     :Union[str, Any]       	=      False
	a     :Union[str, Any]       	=      True
	a     :Optional[Any]       	=      False
	def 		_lowercase    (			self    :     Optional[int]			)			->	Tuple:
					lowercase_	    =   SqueezeBertModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				dim=3_7			)
	def 		_lowercase    (			self    :     int			)			->	Tuple:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Any:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	Dict:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Dict:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_			)




	@slow
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Dict:
					for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_	    =   SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

@require_sentencepiece
@require_tokenizers
@require_torch
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@slow
	def 		_lowercase    (			self    :     Dict			)			->	Dict:
					lowercase_	    =   SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''			)

					lowercase_	    =   torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)[0]
					lowercase_	    =   torch.Size((1, 3)			)
					self.assertEqual(output.shape			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.tensor([[0.64_01, -0.03_49, -0.60_41]]			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				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


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					,   )
