






"""simple docstring"""



import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    DeiTForImageClassification,
						    DeiTForImageClassificationWithTeacher,
						    DeiTForMaskedImageModeling,
						    DeiTModel,
						)
						from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor







class    lowercase__			:


			def __init__(      self     :  Optional[Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  Tuple=13   ,       snake_case__     :  str=30   ,       snake_case__     :  Tuple=2   ,       snake_case__     :  List[Any]=3   ,       snake_case__     :  List[Any]=True   ,       snake_case__     :  List[str]=True   ,       snake_case__     :  List[Any]=32   ,       snake_case__     :  Tuple=5   ,       snake_case__     :  Optional[Any]=4   ,       snake_case__     :  Dict=37   ,       snake_case__     :  List[Any]="gelu"   ,       snake_case__     :  Optional[int]=0.1   ,       snake_case__     :  Dict=0.1   ,       snake_case__     :  str=10   ,       snake_case__     :  List[Any]=0.02   ,       snake_case__     :  str=3   ,       snake_case__     :  Optional[int]=None   ,       snake_case__     :  Tuple=2   ,       ):
										lowerCamelCase_       :							Union[str, Any]									=parent
										lowerCamelCase_       :							List[Any]									=batch_size
										lowerCamelCase_       :							List[Any]									=image_size
										lowerCamelCase_       :							List[Any]									=patch_size
										lowerCamelCase_       :							List[str]									=num_channels
										lowerCamelCase_       :							List[Any]									=is_training
										lowerCamelCase_       :							int									=use_labels
										lowerCamelCase_       :							Any									=hidden_size
										lowerCamelCase_       :							int									=num_hidden_layers
										lowerCamelCase_       :							str									=num_attention_heads
										lowerCamelCase_       :							str									=intermediate_size
										lowerCamelCase_       :							Optional[Any]									=hidden_act
										lowerCamelCase_       :							Optional[Any]									=hidden_dropout_prob
										lowerCamelCase_       :							int									=attention_probs_dropout_prob
										lowerCamelCase_       :							Any									=type_sequence_label_size
										lowerCamelCase_       :							Optional[Any]									=initializer_range
										lowerCamelCase_       :							int									=scope
										lowerCamelCase_       :							Dict									=encoder_stride

										# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
										lowerCamelCase_       :							Any									=(image_size // patch_size) ** 2
										lowerCamelCase_       :							Dict									=num_patches + 2


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							List[str]									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowerCamelCase_       :							str									=None
										if self.use_labels:
																	lowerCamelCase_       :							int									=ids_tensor([self.batch_size]   ,       self.type_sequence_label_size					)

										lowerCamelCase_       :							Dict									=self.get_config()

										return config, pixel_values, labels


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										return DeiTConfig(
										    image_size=self.image_size   ,       patch_size=self.patch_size   ,       num_channels=self.num_channels   ,       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   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       is_decoder=snake_case__   ,       initializer_range=self.initializer_range   ,       encoder_stride=self.encoder_stride   ,       )


			def 	UpperCAmelCase__						(      self     :  Tuple   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  Union[str, Any]					):
										lowerCamelCase_       :							str									=DeiTModel(config=snake_case__					)
										model.to(snake_case__					)
										model.eval()
										lowerCamelCase_       :							Optional[Any]									=model(snake_case__					)
										self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)					)


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Dict   ,       snake_case__     :  str   ,       snake_case__     :  Union[str, Any]					):
										lowerCamelCase_       :							str									=DeiTForMaskedImageModeling(config=snake_case__					)
										model.to(snake_case__					)
										model.eval()
										lowerCamelCase_       :							Any									=model(snake_case__					)
										self.parent.assertEqual(
										    result.reconstruction.shape   ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)					)

										# test greyscale images
										lowerCamelCase_       :							str									=1
										lowerCamelCase_       :							Tuple									=DeiTForMaskedImageModeling(snake_case__					)
										model.to(snake_case__					)
										model.eval()

										lowerCamelCase_       :							int									=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowerCamelCase_       :							Optional[Any]									=model(snake_case__					)
										self.parent.assertEqual(result.reconstruction.shape   ,       (self.batch_size, 1, self.image_size, self.image_size)					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]   ,       snake_case__     :  int   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  Tuple					):
										lowerCamelCase_       :							Dict									=self.type_sequence_label_size
										lowerCamelCase_       :							Tuple									=DeiTForImageClassification(snake_case__					)
										model.to(snake_case__					)
										model.eval()
										lowerCamelCase_       :							Any									=model(snake_case__   ,       labels=snake_case__					)
										self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										lowerCamelCase_       :							List[Any]									=1
										lowerCamelCase_       :							int									=DeiTForImageClassification(snake_case__					)
										model.to(snake_case__					)
										model.eval()

										lowerCamelCase_       :							List[Any]									=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowerCamelCase_       :							int									=model(snake_case__   ,       labels=snake_case__					)
										self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)					)


			def 	UpperCAmelCase__						(      self     :  int					):
										lowerCamelCase_       :							List[Any]									=self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase_    
										)							,						(
										    lowerCamelCase_    
										)							,						(
										    lowerCamelCase_    
										)							,						    
										)       :							Dict									=config_and_inputs
										lowerCamelCase_       :							Any									={"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class    lowercase__			(							lowercase_,   lowercase_,   unittest.TestCase   ):
			_UpperCAmelCase		:List[str]   			= (
			    (
			        DeiTModel,
			        DeiTForImageClassification,
			        DeiTForImageClassificationWithTeacher,
			        DeiTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase		:Optional[int]   			= (
			    {
			        '''feature-extraction''': DeiTModel,
			        '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCAmelCase		:Dict   			= False
			_UpperCAmelCase		:int   			= False
			_UpperCAmelCase		:List[Any]   			= False


			def 	UpperCAmelCase__						(      self     :  Dict					):
										lowerCamelCase_       :							Optional[int]									=DeiTModelTester(self					)
										lowerCamelCase_       :							Dict									=ConfigTester(self   ,       config_class=snake_case__   ,       has_text_modality=snake_case__   ,       hidden_size=37					)


			def 	UpperCAmelCase__						(      self     :  Dict					):
										self.config_tester.run_common_tests()


			@unittest.skip(reason="DeiT does not use inputs_embeds"					)
			def 	UpperCAmelCase__						(      self     :  Dict					):
										pass


			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										lowerCamelCase_							,						lowerCamelCase_       :							Any									=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowerCamelCase_       :							str									=model_class(snake_case__					)
																	self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)					)
																	lowerCamelCase_       :							Any									=model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case__   ,       nn.Linear					)					)


			def 	UpperCAmelCase__						(      self     :  Dict					):
										lowerCamelCase_							,						lowerCamelCase_       :							int									=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowerCamelCase_       :							Union[str, Any]									=model_class(snake_case__					)
																	lowerCamelCase_       :							Optional[Any]									=inspect.signature(model.forward					)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowerCamelCase_       :							Optional[Any]									=[*signature.parameters.keys()]

																	lowerCamelCase_       :							Optional[Any]									=["pixel_values"]
																	self.assertListEqual(arg_names[:1]   ,       snake_case__					)


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							str									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							Any									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__					)


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							Tuple									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*snake_case__					)


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  str   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  List[str]=False					):
										lowerCamelCase_       :							Tuple									=super()._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__					)

										if return_labels:
																	if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																								del inputs_dict["labels"]

										return inputs_dict


			def 	UpperCAmelCase__						(      self     :  Tuple					):
										if not self.model_tester.is_training:
																	return

										lowerCamelCase_							,						lowerCamelCase_       :							int									=self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase_       :							Any									=True

										for model_class in self.all_model_classes:
																	# DeiTForImageClassificationWithTeacher supports inference-only
																	if (
																	    model_class in get_values(snake_case__					)
																	    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																	):
																								continue
																	lowerCamelCase_       :							Optional[Any]									=model_class(snake_case__					)
																	model.to(snake_case__					)
																	model.train()
																	lowerCamelCase_       :							str									=self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__					)
																	lowerCamelCase_       :							Optional[Any]									=model(**snake_case__					).loss
																	loss.backward()


			def 	UpperCAmelCase__						(      self     :  Tuple					):
										lowerCamelCase_							,						lowerCamelCase_       :							str									=self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
																	return

										lowerCamelCase_       :							List[Any]									=False
										lowerCamelCase_       :							int									=True

										for model_class in self.all_model_classes:
																	if model_class in get_values(snake_case__					) or not model_class.supports_gradient_checkpointing:
																								continue
																	# DeiTForImageClassificationWithTeacher supports inference-only
																	if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																								continue
																	lowerCamelCase_       :							Optional[Any]									=model_class(snake_case__					)
																	model.gradient_checkpointing_enable()
																	model.to(snake_case__					)
																	model.train()
																	lowerCamelCase_       :							Optional[int]									=self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__					)
																	lowerCamelCase_       :							Tuple									=model(**snake_case__					).loss
																	loss.backward()


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_							,						lowerCamelCase_       :							str									=self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase_       :							Union[str, Any]									=[
										    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
										    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
										    {"title": "regression", "num_labels": 1, "dtype": torch.float},
										]

										for model_class in self.all_model_classes:
																	if (
																	    model_class
																	    not in [
																	        *get_values(snake_case__					),
																	        *get_values(snake_case__					),
																	    ]
																	    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																	):
																								continue

																	for problem_type in problem_types:
																								with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""					):
																															lowerCamelCase_       :							Optional[int]									=problem_type["title"]
																															lowerCamelCase_       :							List[Any]									=problem_type["num_labels"]

																															lowerCamelCase_       :							Union[str, Any]									=model_class(snake_case__					)
																															model.to(snake_case__					)
																															model.train()

																															lowerCamelCase_       :							List[str]									=self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__					)

																															if problem_type["num_labels"] > 1:
																																						lowerCamelCase_       :							List[str]									=inputs["labels"].unsqueeze(1					).repeat(1   ,       problem_type["num_labels"]					)

																															lowerCamelCase_       :							Optional[Any]									=inputs["labels"].to(problem_type["dtype"]					)

																															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																															# they have the same size." which is a symptom something in wrong for the regression problem.
																															# See https://github.com/huggingface/transformers/issues/11780
																															with warnings.catch_warnings(record=snake_case__					) as warning_list:
																																						lowerCamelCase_       :							Optional[int]									=model(**snake_case__					).loss
																															for w in warning_list:
																																						if "Using a target size that is different to the input size" in str(w.message					):
																																													raise ValueError(
																																													    F"""Something is going wrong in the regression problem: intercepted {w.message}"""					)

																															loss.backward()


			@slow
			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_       :							str									=DeiTModel.from_pretrained(snake_case__					)
																	self.assertIsNotNone(snake_case__					)


def  _snake_case				(     )				->	Any:
							lowerCamelCase_       :							Optional[int]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							return image







@require_torch
@require_vision
class    lowercase__			(							unittest.TestCase   ):


			@cached_property
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										return (
										    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"					)
										    if is_vision_available()
										    else None
										)


			@slow
			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Optional[int]									=DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"					).to(
										    snake_case__					)

										lowerCamelCase_       :							Any									=self.default_image_processor
										lowerCamelCase_       :							List[str]									=prepare_img()
										lowerCamelCase_       :							List[Any]									=image_processor(images=snake_case__   ,       return_tensors="pt"					).to(snake_case__					)

										# forward pass
										with torch.no_grad():
																	lowerCamelCase_       :							Any									=model(**snake_case__					)

										# verify the logits
										lowerCamelCase_       :							List[str]									=torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape   ,       snake_case__					)

										lowerCamelCase_       :							int									=torch.tensor([-1.0_266, 0.1_912, -1.2_861]					).to(snake_case__					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,       snake_case__   ,       atol=1E-4					)					)


			@slow
			@require_accelerate
			@require_torch_gpu
			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Optional[int]									=DeiTModel.from_pretrained(
										    "facebook/deit-base-distilled-patch16-224"   ,       torch_dtype=torch.floataa   ,       device_map="auto"					)
										lowerCamelCase_       :							str									=self.default_image_processor

										lowerCamelCase_       :							Optional[int]									=prepare_img()
										lowerCamelCase_       :							Tuple									=image_processor(images=snake_case__   ,       return_tensors="pt"					)
										lowerCamelCase_       :							Tuple									=inputs.pixel_values.to(snake_case__					)

										# forward pass to make sure inference works in fp16
										with torch.no_grad():
																	lowerCamelCase_       :							Optional[int]									=model(snake_case__					)






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


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







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    len(list(m.modules()       )       ) == 1 or isinstance(snake_case      ,   nn.Convad       ) or isinstance(snake_case      ,   nn.BatchNormad       )
											if has_not_submodules:
																		self.traced.append(snake_case       )




				def __call__(					self      ,   snake_case       ):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook       )       )
											self.module(snake_case       )
											[x.remove() for x in self.handles]
											return self




				@property
				def     a							(					self       ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case       : len(list(x.state_dict().keys()       )       ) > 0      ,   self.traced       )       )







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: int       			=			0
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.dest_skip      ,   snake_case       )       )

											if len(snake_case       ) != len(snake_case       ):
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(snake_case       )} operations while'''
																		    F''' destination module has {len(snake_case       )}.'''       )

											for dest_m, src_m in zip(snake_case      ,   snake_case       ):
																		dest_m.load_state_dict(src_m.state_dict()       )
																		if self.verbose == 1:
																									print(F'''Transfered from={src_m} to={dest_m}'''       )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														snake_case_							=    torch.randn((1, 3, 224, 224)      )
														module_transfer(UpperCamelCase__      )

							assert torch.allclose(from_model(UpperCamelCase__      )   ,							our_model(UpperCamelCase__      ).logits      ), "The model logits don't match the original one."

							snake_case_							=    F'''resnet{"-".join(name.split("resnet"      )      )}'''
							print(UpperCamelCase__      )

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add model'   ,							use_temp_dir=UpperCamelCase__   ,							)

														# we can use the convnext one
														snake_case_							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add image processor'   ,							use_temp_dir=UpperCamelCase__   ,							)

														print(F'''Pushed {checkpoint_name}'''      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							snake_case_							=    'imagenet-1k-id2label.json'
							snake_case_							=    1000
							snake_case_							=    (1, num_labels)

							snake_case_							=    'huggingface/label-files'
							snake_case_							=    num_labels
							snake_case_							=    json.load(open(hf_hub_download(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

							snake_case_							=    idalabel
							snake_case_							=    {v: k for k, v in idalabel.items()}

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							snake_case_							=    {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							}

							if model_name:
														convert_weight_and_push(UpperCamelCase__   ,							names_to_config[model_name]   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							return config, expected_shape


if __name__ == "__main__":
					_UpperCAmelCase		:   List[str]										=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
					        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					_UpperCAmelCase		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   Path										=       args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




