






import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


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







@dataclass
class 				lowerCamelCase_     :
							SCREAMING_SNAKE_CASE_						     =			42
							SCREAMING_SNAKE_CASE_						     =			field(default_factory=a_     )
							SCREAMING_SNAKE_CASE_						     =			field(default_factory=a_     )
							SCREAMING_SNAKE_CASE_						     =			field(default_factory=a_     )
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					Tensor							,__lowerCamelCase		:					Tensor							,__lowerCamelCase		:					str					):





								'''simple docstring'''



								a		=			len(list(m.modules()					)					) == 1 or isinstance(__lowerCamelCase							,nn.Convad					) or isinstance(__lowerCamelCase							,nn.BatchNormad					)
								if has_not_submodules:
									self.traced.append(__lowerCamelCase					)
									a		=			m
							def __call__(						self		:					str							,__lowerCamelCase		:					Tensor					):





								'''simple docstring'''



								for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook							,name=__lowerCamelCase					)					)					)
								self.module(__lowerCamelCase					)
								[x.remove() for x in self.handles]
								return self




							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()					)					) > 0}







class 				lowerCamelCase_     (	nn.Module     ):
							def __init__(						self		:					List[str]							,__lowerCamelCase		:					nn.Module					):





								'''simple docstring'''



								super().__init__()

								a		=			[]
								# - get the stem
								feature_blocks.append(('''conv1''', model.stem)					)
								# - get all the feature blocks
								for k, v in model.trunk_output.named_children():
									assert k.startswith('''block'''					), F"""Unexpected layer name {k}"""
									a		=			len(__lowerCamelCase					) + 1
									feature_blocks.append((F"""res{block_index}""", v)					)

								a		=			nn.ModuleDict(__lowerCamelCase					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					Tensor					):





								'''simple docstring'''



								return get_trunk_forward_outputs(
								    __lowerCamelCase							,out_feat_keys=__lowerCamelCase							,feature_blocks=self._feature_blocks							,)







class 				lowerCamelCase_     (	a_     ):
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Dict[str, str]:


	"""simple docstring"""



	a		=			RegNetConfig(depths=[2, 7, 1_7, 1],  hidden_sizes=[8, 8, 8, 8],  groups_width=8  )
	if "in1k" in model_name:
		a		=			RegNetForImageClassification(snake_case_  )
	else:
		a		=			RegNetModel(snake_case_  )
	# create from model (with small weights)
	a		=			FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=2_7,  group_width=1_0_1_0,  w_a=1_7_4_4,  w_a=620.83,  w_m=2.52  )  )  )

	with torch.no_grad():
		a		=			from_model.eval()
		a		=			our_model.eval()

		a		=			torch.randn((1, 3, 3_2, 3_2)  )
		# trace both
		a		=			Tracker(snake_case_  )
		a		=			dest_tracker(snake_case_  ).parametrized

		pprint(dest_tracker.nameamodule  )
		a		=			Tracker(snake_case_  )
		a		=			src_tracker(snake_case_  ).parametrized

	# convert the keys -> module dict to keys -> params
	def to_params_dict(snake_case_  ):
		a		=			OrderedDict()
		for name, module in dict_with_modules.items():
			for param_name, param in module.state_dict().items():
				a		=			param
		return params_dict

	a		=			{}

	a		=			to_params_dict(snake_case_  )
	a		=			to_params_dict(snake_case_  )

	for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),  dst_state_dict.items()  ):
		a		=			dest_key
		logger.info(f"""{src_key} -> {dest_key}"""  )
	# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
	if "in1k" in model_name:
		a		=			'''classifier.1.weight'''
		a		=			'''classifier.1.bias'''

	return from_to_ours_keys





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_ = None,  snake_case_ = True  )   ->  List[Any]:


	"""simple docstring"""



	a		=			'''imagenet-1k-id2label.json'''
	a		=			1_0_0_0

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

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

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

	a		=			{
	    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1],  hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],  groups_width=1_0_1_0  ),
	    # finetuned on imagenet
	    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1],  hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],  groups_width=1_0_1_0  ),
	}

	# add seer weights logic
	def load_using_classy_vision(snake_case_  ) -> Tuple[Dict, Dict]:
		a		=			torch.hub.load_state_dict_from_url(snake_case_,  model_dir=str(snake_case_  ),  map_location='''cpu'''  )
		# check if we have a head, if yes add it
		a		=			files['''classy_state_dict''']['''base_model''']['''model''']
		return model_state_dict["trunk"], model_state_dict["heads"]

	a		=			{
	    '''regnet-y-10b-seer''': partial(
	        snake_case_,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''',  ),
	    '''regnet-y-10b-seer-in1k''': partial(
	        snake_case_,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''',  ),
	}

	a		=			get_from_to_our_keys(snake_case_  )

	if not (save_directory / f"""{model_name}.pth""").exists():
		logger.info('''Loading original state_dict.'''  )
		a	, a		=			names_to_from_model[model_name]()
		a		=			from_state_dict_trunk
		if "in1k" in model_name:
			# add the head
			a		=			{**from_state_dict_trunk, **from_state_dict_head}
		logger.info('''Done!'''  )

		a		=			{}

		a		=			list(from_state_dict.keys()  )
		a		=			r'''\.block.-part.'''
		# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
		for key in from_state_dict.keys():
			# remove the weird "block[0,1]-part" from the key
			a		=			re.sub(snake_case_,  '''''',  snake_case_  )
			# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
			a		=			from_to_ours_keys[src_key]
			# store the parameter with our key
			a		=			from_state_dict[key]
			not_used_keys.remove(snake_case_  )
		# check that all keys have been updated
		assert len(snake_case_  ) == 0, f"""Some keys where not used {",".join(snake_case_  )}"""

		logger.info(f"""The following keys were not used: {",".join(snake_case_  )}"""  )

		# save our state dict to disk
		torch.save(snake_case_,  save_directory / f"""{model_name}.pth"""  )

		del converted_state_dict
	else:
		logger.info('''The state_dict was already stored on disk.'''  )
	if push_to_hub:
		logger.info(f"""Token is {os.environ["HF_TOKEN"]}"""  )
		logger.info('''Loading our model.'''  )
		# create our model
		a		=			names_to_config[model_name]
		a		=			RegNetModel
		if "in1k" in model_name:
			a		=			RegNetForImageClassification
		a		=			our_model_func(snake_case_  )
		# place our model to the meta device (so remove all the weights)
		our_model.to(torch.device('''meta'''  )  )
		logger.info('''Loading state_dict in our model.'''  )
		# load state dict
		a		=			our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    snake_case_,  snake_case_,  [save_directory / f"""{model_name}.pth"""]  )
		logger.info('''Finally, pushing!'''  )
		# push it to hub
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / model_name,  commit_message='''Add model''',  output_dir=save_directory / model_name,  )
		a		=			3_8_4
		# we can use the convnext one
		a		=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''',  size=snake_case_  )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / model_name,  commit_message='''Add image processor''',  output_dir=save_directory / model_name,  )


if __name__ == "__main__":
				UpperCamelCase__						:      List[Any]								=					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 regnet* architecture,"""
				        """ currently: regnetx-*, regnety-*. 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__						:      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)













import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
				from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor







class 				lowerCamelCase_     :
							def __init__(						self		:					str							,__lowerCamelCase		:					Dict							,__lowerCamelCase		:					List[Any]=3							,__lowerCamelCase		:					Tuple=32							,__lowerCamelCase		:					List[str]=3							,__lowerCamelCase		:					Optional[Any]=10							,__lowerCamelCase		:					str=[10, 20, 30, 40]							,__lowerCamelCase		:					int=[1, 1, 2, 1]							,__lowerCamelCase		:					Any=True							,__lowerCamelCase		:					List[str]=True							,__lowerCamelCase		:					Union[str, Any]="relu"							,__lowerCamelCase		:					Optional[int]=3							,__lowerCamelCase		:					int=None							,__lowerCamelCase		:					List[Any]=["stage2", "stage3", "stage4"]							,__lowerCamelCase		:					int=[2, 3, 4]							,):





								'''simple docstring'''



								a		=			parent
								a		=			batch_size
								a		=			image_size
								a		=			num_channels
								a		=			embeddings_size
								a		=			hidden_sizes
								a		=			depths
								a		=			is_training
								a		=			use_labels
								a		=			hidden_act
								a		=			num_labels
								a		=			scope
								a		=			len(__lowerCamelCase					)
								a		=			out_features
								a		=			out_indices
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								a		=			None
								if self.use_labels:
									a		=			ids_tensor([self.batch_size]							,self.num_labels					)

								a		=			self.get_config()

								return config, pixel_values, labels
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								return ResNetConfig(
								    num_channels=self.num_channels							,embeddings_size=self.embeddings_size							,hidden_sizes=self.hidden_sizes							,depths=self.depths							,hidden_act=self.hidden_act							,num_labels=self.num_labels							,out_features=self.out_features							,out_indices=self.out_indices							,)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					Dict							,__lowerCamelCase		:					Optional[int]					):





								'''simple docstring'''



								a		=			ResNetModel(config=__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()
								a		=			model(__lowerCamelCase					)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape							,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					Optional[Any]							,__lowerCamelCase		:					Any							,__lowerCamelCase		:					List[str]					):





								'''simple docstring'''



								a		=			self.num_labels
								a		=			ResNetForImageClassification(__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()
								a		=			model(__lowerCamelCase							,labels=__lowerCamelCase					)
								self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					Optional[Any]							,__lowerCamelCase		:					int							,__lowerCamelCase		:					List[str]					):





								'''simple docstring'''



								a		=			ResNetBackbone(config=__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()
								a		=			model(__lowerCamelCase					)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps					)							,len(config.out_features					)					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)							,[self.batch_size, self.hidden_sizes[1], 4, 4]					)

								# verify channels
								self.parent.assertEqual(len(model.channels					)							,len(config.out_features					)					)
								self.parent.assertListEqual(model.channels							,config.hidden_sizes[1:]					)

								# verify backbone works with out_features=None
								a		=			None
								a		=			ResNetBackbone(config=__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()
								a		=			model(__lowerCamelCase					)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps					)							,1					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)							,[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

								# verify channels
								self.parent.assertEqual(len(model.channels					)							,1					)
								self.parent.assertListEqual(model.channels							,[config.hidden_sizes[-1]]					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a		=			self.prepare_config_and_inputs()
								a	, a	, a		=			config_and_inputs
								a		=			{'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 				lowerCamelCase_     (	a_ ,						a_ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			(
							    (
							        ResNetModel,
							        ResNetForImageClassification,
							        ResNetBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE_						     =			(
							    {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE_						     =			True
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								a		=			ResNetModelTester(self					)
								a		=			ConfigTester(self							,config_class=__lowerCamelCase							,has_text_modality=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								return
							@unittest.skip(reason='''ResNet does not use inputs_embeds'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								pass
							@unittest.skip(reason='''ResNet does not support input and output embeddings'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								pass
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a		=			model_class(__lowerCamelCase					)
									a		=			inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									a		=			[*signature.parameters.keys()]

									a		=			['''pixel_values''']
									self.assertListEqual(arg_names[:1]							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a		=			model_class(config=__lowerCamelCase					)
									for name, module in model.named_modules():
										if isinstance(__lowerCamelCase							,(nn.BatchNormad, nn.GroupNorm)					):
											self.assertTrue(
											    torch.all(module.weight == 1					)							,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,)
											self.assertTrue(
											    torch.all(module.bias == 0					)							,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								def check_hidden_states_output(__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Any					):
									a		=			model_class(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()

									with torch.no_grad():
										a		=			model(**self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase					)					)

									a		=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									a		=			self.model_tester.num_stages
									self.assertEqual(len(__lowerCamelCase					)							,expected_num_stages + 1					)

									# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]					)							,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,)

								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()
								a		=			['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
									for layer_type in layers_type:
										a		=			layer_type
										a		=			True
										check_hidden_states_output(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										a		=			True

										check_hidden_states_output(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
							@unittest.skip(reason='''ResNet does not use feedforward chunking'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								pass
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase					)




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a		=			ResNetModel.from_pretrained(__lowerCamelCase					)
									self.assertIsNotNone(__lowerCamelCase					)





def   SCREAMING_SNAKE_CASE__						(   )   ->  Optional[int]:


	"""simple docstring"""



	a		=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image







@require_torch
@require_vision
class 				lowerCamelCase_     (	unittest.TestCase     ):
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								return (
								    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								    if is_vision_available()
								    else None
								)




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(__lowerCamelCase					)

								a		=			self.default_image_processor
								a		=			prepare_img()
								a		=			image_processor(images=__lowerCamelCase							,return_tensors='''pt'''					).to(__lowerCamelCase					)

								# forward pass
								with torch.no_grad():
									a		=			model(**__lowerCamelCase					)

								# verify the logits
								a		=			torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape							,__lowerCamelCase					)

								a		=			torch.tensor([-11.1_069, -9.7_877, -8.3_777]					).to(__lowerCamelCase					)

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







@require_torch
class 				lowerCamelCase_     (	a_ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			(ResNetBackbone,) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			ResNetConfig
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								a		=			ResNetModelTester(self					)





