



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase									=   logging.get_logger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      List[Any]:
		_a     :      List[str]        =							[]
		for i in range(config.num_hidden_layers  ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")  )
				rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")  )
				rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")  )
				rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")  )
				rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")  )
				rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")  )
				rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")  )
				rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")  )
				rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")  )
				rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")  )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('cls_token', 'vit.embeddings.cls_token'),
		        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
		        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
		        ('pos_embed', 'vit.embeddings.position_embeddings'),
		    ]  )

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('norm.weight', 'layernorm.weight'),
				        ('norm.bias', 'layernorm.bias'),
				        ('pre_logits.fc.weight', 'pooler.dense.weight'),
				        ('pre_logits.fc.bias', 'pooler.dense.bias'),
				    ]  )

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				_a     :      Dict        =							[(pair[0], pair[1][4:]) if pair[1].startswith('vit'  ) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('norm.weight', 'vit.layernorm.weight'),
				        ('norm.bias', 'vit.layernorm.bias'),
				        ('head.weight', 'classifier.weight'),
				        ('head.bias', 'classifier.bias'),
				    ]  )

		return rename_keys



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Dict:
		for i in range(config.num_hidden_layers  ):
				if base_model:
						_a     :      Union[str, Any]        =							''
				else:
						_a     :      Optional[Any]        =							'vit.'
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				_a     :      Dict        =							state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""  )
				_a     :      Tuple        =							state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""  )
				# next, add query, keys and values (in that order) to the state dict
				_a     :      Union[str, Any]        =							in_proj_weight[
				    : config.hidden_size, :
				]
				_a     :      Union[str, Any]        =							in_proj_bias[: config.hidden_size]
				_a     :      Union[str, Any]        =							in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_a     :      int        =							in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_a     :      Dict        =							in_proj_weight[
				    -config.hidden_size :, :
				]
				_a     :      Optional[Any]        =							in_proj_bias[-config.hidden_size :]



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		_a     :      int        =							['head.weight', 'head.bias']
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase_						,			lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      Tuple        =							dct.pop(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							val



def      __lowerCamelCase     (      )			->      Any:
		_a     :      Tuple        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a     :      List[Any]        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      Any        =							ViTConfig()
		_a     :      List[Any]        =							False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				_a     :      List[Any]        =							True
				_a     :      str        =							int(vit_name[-12:-10]  )
				_a     :      Any        =							int(vit_name[-9:-6]  )
		else:
				_a     :      str        =							1000
				_a     :      List[Any]        =							'huggingface/label-files'
				_a     :      int        =							'imagenet-1k-id2label.json'
				_a     :      Any        =							json.load(open(hf_hub_download(lowerCAmelCase_						,			lowerCAmelCase_						,			repo_type='dataset'  )						,			'r'  )  )
				_a     :      Optional[int]        =							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
				_a     :      str        =							idalabel
				_a     :      int        =							{v: k for k, v in idalabel.items()}
				_a     :      List[Any]        =							int(vit_name[-6:-4]  )
				_a     :      Optional[int]        =							int(vit_name[-3:]  )
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('tiny'  ):
						_a     :      Optional[int]        =							192
						_a     :      Dict        =							768
						_a     :      List[Any]        =							12
						_a     :      Union[str, Any]        =							3
				elif vit_name[9:].startswith('small'  ):
						_a     :      Optional[Any]        =							384
						_a     :      str        =							1536
						_a     :      str        =							12
						_a     :      Union[str, Any]        =							6
				else:
						pass
		else:
				if vit_name[4:].startswith('small'  ):
						_a     :      int        =							768
						_a     :      str        =							2304
						_a     :      List[str]        =							8
						_a     :      Optional[int]        =							8
				elif vit_name[4:].startswith('base'  ):
						pass
				elif vit_name[4:].startswith('large'  ):
						_a     :      Any        =							1024
						_a     :      Optional[int]        =							4096
						_a     :      Union[str, Any]        =							24
						_a     :      Any        =							16
				elif vit_name[4:].startswith('huge'  ):
						_a     :      str        =							1280
						_a     :      Dict        =							5120
						_a     :      str        =							32
						_a     :      str        =							16

    # load original model from timm
		_a     :      Union[str, Any]        =							timm.create_model(lowerCAmelCase_						,			pretrained=lowerCAmelCase_  )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		_a     :      str        =							timm_model.state_dict()
		if base_model:
				remove_classification_head_(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							create_rename_keys(lowerCAmelCase_						,			lowerCAmelCase_  )
		for src, dest in rename_keys:
				rename_key(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		read_in_q_k_v(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				_a     :      Union[str, Any]        =							ViTModel(lowerCAmelCase_  ).eval()
		else:
				_a     :      str        =							ViTForImageClassification(lowerCAmelCase_  ).eval()
		model.load_state_dict(lowerCAmelCase_  )

		# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
		if "deit" in vit_name:
				_a     :      Any        =							DeiTImageProcessor(size=config.image_size  )
		else:
				_a     :      Union[str, Any]        =							ViTImageProcessor(size=config.image_size  )
		_a     :      List[str]        =							image_processor(images=prepare_img()						,			return_tensors='pt'  )
		_a     :      str        =							encoding['pixel_values']
		_a     :      List[str]        =							model(lowerCAmelCase_  )

		if base_model:
				_a     :      Optional[Any]        =							timm_model.forward_features(lowerCAmelCase_  )
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(lowerCAmelCase_						,			outputs.pooler_output						,			atol=1E-3  )
		else:
				_a     :      Union[str, Any]        =							timm_model(lowerCAmelCase_  )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(lowerCAmelCase_						,			outputs.logits						,			atol=1E-3  )

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""  )
		model.save_pretrained(lowerCAmelCase_  )
		print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
		image_processor.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--vit_name''',
							    default='''vit_base_patch16_224''',
							    type=str,
							    help='''Name of the ViT timm model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							__lowerCAmelCase									=   parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 	__magic_name__						:







							def __init__(							self					:       List[str]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       List[str]=13	,_UpperCAmelCase					:       Any=32	,_UpperCAmelCase					:       Union[str, Any]=3	,_UpperCAmelCase					:       Optional[int]=4	,_UpperCAmelCase					:       Optional[Any]=[10, 20, 30, 40]	,_UpperCAmelCase					:       Tuple=[2, 2, 3, 2]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=37	,_UpperCAmelCase					:       Optional[int]="gelu"	,_UpperCAmelCase					:       Optional[Any]=10	,_UpperCAmelCase					:       Tuple=0.02	,_UpperCAmelCase					:       Any=["stage2", "stage3", "stage4"]	,_UpperCAmelCase					:       Any=[2, 3, 4]	,_UpperCAmelCase					:       Tuple=None	,):
									_a     :      Optional[Any]        =							parent
									_a     :      List[Any]        =							batch_size
									_a     :      str        =							image_size
									_a     :      Union[str, Any]        =							num_channels
									_a     :      List[Any]        =							num_stages
									_a     :      Dict        =							hidden_sizes
									_a     :      int        =							depths
									_a     :      Tuple        =							is_training
									_a     :      List[str]        =							use_labels
									_a     :      Dict        =							intermediate_size
									_a     :      int        =							hidden_act
									_a     :      int        =							num_labels
									_a     :      Any        =							initializer_range
									_a     :      Tuple        =							out_features
									_a     :      int        =							out_indices
									_a     :      List[Any]        =							scope







							def 							__lowercase			(							self					:       Dict		):
									_a     :      List[Any]        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a     :      Union[str, Any]        =							None
									if self.use_labels:
											_a     :      Tuple        =							ids_tensor([self.batch_size]	,self.num_labels		)

									_a     :      str        =							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase			(							self					:       Any		):
									return ConvNextVaConfig(
									    num_channels=self.num_channels	,hidden_sizes=self.hidden_sizes	,depths=self.depths	,num_stages=self.num_stages	,hidden_act=self.hidden_act	,is_decoder=_UpperCAmelCase	,initializer_range=self.initializer_range	,out_features=self.out_features	,out_indices=self.out_indices	,num_labels=self.num_labels	,)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Optional[Any]        =							ConvNextVaModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Any        =							model(_UpperCAmelCase		)
									# 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 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       int		):
									_a     :      List[Any]        =							ConvNextVaForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      List[str]        =							model(_UpperCAmelCase	,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Optional[int]        =							ConvNextVaBackbone(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Dict        =							model(_UpperCAmelCase		)

									# verify hidden states
									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     :      Tuple        =							None
									_a     :      List[Any]        =							ConvNextVaBackbone(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      List[str]        =							model(_UpperCAmelCase		)

									# 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 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Any        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Union[str, Any]        =							config_and_inputs
									_a     :      Any        =							{'pixel_values': pixel_values}
									return config, inputs_dict







							def 							__lowercase			(							self					:       str		):
									_a     :      Tuple        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Tuple        =							config_and_inputs
									_a     :      List[Any]        =							{'pixel_values': pixel_values, 'labels': labels}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					(
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase       :    str   				=					(
							    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    int   				=					False
							lowerCAmelCase       :    str   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False
							lowerCAmelCase       :    List[str]   				=					False
							lowerCAmelCase       :    Optional[int]   				=					False







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      str        =							ConvNextVaModelTester(self		)
									_a     :      Tuple        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									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 							__lowercase			(							self					:       str		):
									return







							@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       List[Any]		):
									pass







							@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'		)
							def 							__lowercase			(							self					:       Optional[int]		):
									pass







							@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'		)
							def 							__lowercase			(							self					:       Any		):
									pass







							def 							__lowercase			(							self					:       List[str]		):
									if not self.model_tester.is_training:
											return

									for model_class in self.all_model_classes:
											_a       ,					_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs_with_labels()
											_a     :      Any        =							True

											if model_class.__name__ in [
											    *get_values(_UpperCAmelCase		),
											    *get_values(_UpperCAmelCase		),
											]:
													continue

											_a     :      Optional[Any]        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a     :      str        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      Optional[int]        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       str		):
									if not self.model_tester.is_training:
											return

									for model_class in self.all_model_classes:
											_a       ,					_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs_with_labels()
											_a     :      Optional[int]        =							False
											_a     :      Tuple        =							True

											if (
											    model_class.__name__
											    in [*get_values(_UpperCAmelCase		), *get_values(_UpperCAmelCase		)]
											    or not model_class.supports_gradient_checkpointing
											):
													continue

											_a     :      Tuple        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a     :      Any        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      List[Any]        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       List[Any]		):
									_a       ,					_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      int        =							model_class(_UpperCAmelCase		)
											_a     :      Optional[Any]        =							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a     :      Dict        =							[*signature.parameters.keys()]

											_a     :      int        =							['pixel_values']
											self.assertListEqual(arg_names[:1]	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any		):
									def check_hidden_states_output(_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Dict		):
											_a     :      Union[str, Any]        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()

											with torch.no_grad():
													_a     :      List[Any]        =							model(**self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase		)		)

											_a     :      Any        =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											_a     :      str        =							self.model_tester.num_stages
											self.assertEqual(len(_UpperCAmelCase		)	,expected_num_stages + 1		)

											# ConvNextV2'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     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      int        =							True
											check_hidden_states_output(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_a     :      Optional[Any]        =							True

											check_hidden_states_output(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       int		):
									for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a     :      Any        =							ConvNextVaModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      List[Any]:
		_a     :      List[Any]        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							@cached_property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'		) if is_vision_available() else None







							@slow
							def 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'		).to(_UpperCAmelCase		)

									_a     :      Optional[int]        =							self.default_image_processor
									_a     :      str        =							prepare_img()
									_a     :      str        =							preprocessor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      Dict        =							model(**_UpperCAmelCase		)

									# verify the logits
									_a     :      Optional[Any]        =							torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							torch.tensor([0.99_96, 0.19_66, -0.43_86]		).to(_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,_UpperCAmelCase	,atol=1E-4		)		)




