code: 
  '''simple docstring'''
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _UpperCAmelCase   :      List[str] 						=  logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      List[Any]:
  		_UpperCamelCase       : Any    =      ConvNextConfig()
  
  		if "tiny" in checkpoint_url:
  				_UpperCamelCase       : Any    =      [3, 3, 9, 3]
  				_UpperCamelCase       : List[Any]    =      [96, 1_92, 3_84, 7_68]
  		if "small" in checkpoint_url:
  				_UpperCamelCase       : List[Any]    =      [3, 3, 27, 3]
  				_UpperCamelCase       : int    =      [96, 1_92, 3_84, 7_68]
  		if "base" in checkpoint_url:
  				_UpperCamelCase       : int    =      [3, 3, 27, 3]
  				_UpperCamelCase       : Tuple    =      [1_28, 2_56, 5_12, 10_24]
  		if "large" in checkpoint_url:
  				_UpperCamelCase       : Tuple    =      [3, 3, 27, 3]
  				_UpperCamelCase       : str    =      [1_92, 3_84, 7_68, 15_36]
  		if "xlarge" in checkpoint_url:
  				_UpperCamelCase       : str    =      [3, 3, 27, 3]
  				_UpperCamelCase       : Tuple    =      [2_56, 5_12, 10_24, 20_48]
  
  		if "1k" in checkpoint_url:
  				_UpperCamelCase       : Union[str, Any]    =      10_00
  				_UpperCamelCase       : List[Any]    =      '''imagenet-1k-id2label.json'''
  				_UpperCamelCase       : Union[str, Any]    =      (1, 10_00)
  		else:
  				_UpperCamelCase       : Optional[Any]    =      2_18_41
  				_UpperCamelCase       : str    =      '''imagenet-22k-id2label.json'''
  				_UpperCamelCase       : Optional[int]    =      (1, 2_18_41)
  
  		_UpperCamelCase       : Any    =      '''huggingface/label-files'''
  		_UpperCamelCase       : Any    =      num_labels
  		_UpperCamelCase       : Tuple    =      json.load(open(hf_hub_download(UpperCamelCase			,UpperCamelCase			,repo_type='''dataset'''	)			,'''r'''	)	)
  		_UpperCamelCase       : int    =      {int(UpperCamelCase	): v for k, v in idalabel.items()}
  		if "1k" not in checkpoint_url:
  				# this dataset contains 21843 labels but the model only has 21841
  				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  				del idalabel[92_05]
  				del idalabel[1_50_27]
  		_UpperCamelCase       : str    =      idalabel
  		_UpperCamelCase       : List[str]    =      {v: k for k, v in idalabel.items()}
  		_UpperCamelCase       : Tuple    =      hidden_sizes
  		_UpperCamelCase       : List[str]    =      depths
  
  		return config, expected_shape
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Optional[Any]:
  		if "downsample_layers.0.0" in name:
  				_UpperCamelCase       : Any    =      name.replace('''downsample_layers.0.0'''			,'''embeddings.patch_embeddings'''	)
  		if "downsample_layers.0.1" in name:
  				_UpperCamelCase       : Any    =      name.replace('''downsample_layers.0.1'''			,'''embeddings.norm'''	)  # we rename to layernorm later on
  		if "downsample_layers.1.0" in name:
  				_UpperCamelCase       : int    =      name.replace('''downsample_layers.1.0'''			,'''stages.1.downsampling_layer.0'''	)
  		if "downsample_layers.1.1" in name:
  				_UpperCamelCase       : Optional[int]    =      name.replace('''downsample_layers.1.1'''			,'''stages.1.downsampling_layer.1'''	)
  		if "downsample_layers.2.0" in name:
  				_UpperCamelCase       : Optional[int]    =      name.replace('''downsample_layers.2.0'''			,'''stages.2.downsampling_layer.0'''	)
  		if "downsample_layers.2.1" in name:
  				_UpperCamelCase       : int    =      name.replace('''downsample_layers.2.1'''			,'''stages.2.downsampling_layer.1'''	)
  		if "downsample_layers.3.0" in name:
  				_UpperCamelCase       : Tuple    =      name.replace('''downsample_layers.3.0'''			,'''stages.3.downsampling_layer.0'''	)
  		if "downsample_layers.3.1" in name:
  				_UpperCamelCase       : Any    =      name.replace('''downsample_layers.3.1'''			,'''stages.3.downsampling_layer.1'''	)
  		if "stages" in name and "downsampling_layer" not in name:
  				# stages.0.0. for instance should be renamed to stages.0.layers.0.
  				_UpperCamelCase       : Dict    =      name[: len('''stages.0'''	)] + '''.layers''' + name[len('''stages.0'''	) :]
  		if "stages" in name:
  				_UpperCamelCase       : List[str]    =      name.replace('''stages'''			,'''encoder.stages'''	)
  		if "norm" in name:
  				_UpperCamelCase       : Union[str, Any]    =      name.replace('''norm'''			,'''layernorm'''	)
  		if "gamma" in name:
  				_UpperCamelCase       : Any    =      name.replace('''gamma'''			,'''layer_scale_parameter'''	)
  		if "head" in name:
  				_UpperCamelCase       : Optional[int]    =      name.replace('''head'''			,'''classifier'''	)
  
  		return name
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Any:
  		_UpperCamelCase       : Union[str, Any]    =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  		_UpperCamelCase       : Union[str, Any]    =      Image.open(requests.get(UpperCamelCase			,stream=UpperCamelCase	).raw	)
  		return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      List[Any]:
  		_UpperCamelCase,							_UpperCamelCase       : int    =      get_convnext_config(UpperCamelCase	)
  		# load original state_dict from URL
  		_UpperCamelCase       : Any    =      torch.hub.load_state_dict_from_url(UpperCamelCase	)['''model''']
  		# rename keys
  		for key in state_dict.copy().keys():
  				_UpperCamelCase       : List[str]    =      state_dict.pop(UpperCamelCase	)
  				_UpperCamelCase       : Tuple    =      val
  		# add prefix to all keys expect classifier head
  		for key in state_dict.copy().keys():
  				_UpperCamelCase       : List[Any]    =      state_dict.pop(UpperCamelCase	)
  				if not key.startswith('''classifier'''	):
  						_UpperCamelCase       : Dict    =      '''convnext.''' + key
  				_UpperCamelCase       : Optional[int]    =      val
  
  		# load HuggingFace model
  		_UpperCamelCase       : Dict    =      ConvNextForImageClassification(UpperCamelCase	)
  		model.load_state_dict(UpperCamelCase	)
  		model.eval()
  
  		# Check outputs on an image, prepared by ConvNextImageProcessor
  		_UpperCamelCase       : Optional[int]    =      2_24 if '''224''' in checkpoint_url else 3_84
  		_UpperCamelCase       : int    =      ConvNextImageProcessor(size=UpperCamelCase	)
  		_UpperCamelCase       : Union[str, Any]    =      image_processor(images=prepare_img()			,return_tensors='''pt'''	).pixel_values
  
  		_UpperCamelCase       : str    =      model(UpperCamelCase	).logits
  
  		# note: the logits below were obtained without center cropping
  		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
  				_UpperCamelCase       : Dict    =      torch.tensor([-0.1210, -0.6605, 0.1918]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  				_UpperCamelCase       : Union[str, Any]    =      torch.tensor([-0.4473, -0.1847, -0.6365]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  				_UpperCamelCase       : List[Any]    =      torch.tensor([0.4525, 0.7539, 0.0308]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  				_UpperCamelCase       : Optional[int]    =      torch.tensor([0.3561, 0.6350, -0.0384]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  				_UpperCamelCase       : int    =      torch.tensor([0.4174, -0.0989, 0.1489]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  				_UpperCamelCase       : List[Any]    =      torch.tensor([0.2513, -0.1349, -0.1613]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  				_UpperCamelCase       : List[Any]    =      torch.tensor([1.2980, 0.3631, -0.1198]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  				_UpperCamelCase       : str    =      torch.tensor([1.2963, 0.1227, 0.1723]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  				_UpperCamelCase       : Union[str, Any]    =      torch.tensor([1.7956, 0.8390, 0.2820]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  				_UpperCamelCase       : Tuple    =      torch.tensor([-0.2822, -0.0502, -0.0878]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  				_UpperCamelCase       : Tuple    =      torch.tensor([-0.5672, -0.0730, -0.4348]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  				_UpperCamelCase       : Union[str, Any]    =      torch.tensor([0.2681, 0.2365, 0.6246]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  				_UpperCamelCase       : Optional[int]    =      torch.tensor([-0.2642, 0.3931, 0.5116]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  				_UpperCamelCase       : Any    =      torch.tensor([-0.6677, -0.1873, -0.8379]	)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  				_UpperCamelCase       : str    =      torch.tensor([-0.7749, -0.2967, -0.6444]	)
  		else:
  				raise ValueError(f'''Unknown URL: {checkpoint_url}'''	)
  
  		assert torch.allclose(logits[0, :3]			,UpperCamelCase			,atol=1e-3	)
  		assert logits.shape == expected_shape
  
  		Path(UpperCamelCase	).mkdir(exist_ok=UpperCamelCase	)
  		print(f'''Saving model to {pytorch_dump_folder_path}'''	)
  		model.save_pretrained(UpperCamelCase	)
  		print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
  		image_processor.save_pretrained(UpperCamelCase	)
  
  		print('''Pushing model to the hub...'''	)
  		_UpperCamelCase       : Any    =      '''convnext'''
  		if "tiny" in checkpoint_url:
  				model_name += "-tiny"
  		elif "small" in checkpoint_url:
  				model_name += "-small"
  		elif "base" in checkpoint_url:
  				model_name += "-base"
  		elif "xlarge" in checkpoint_url:
  				model_name += "-xlarge"
  		elif "large" in checkpoint_url:
  				model_name += "-large"
  		if "224" in checkpoint_url:
  				model_name += "-224"
  		elif "384" in checkpoint_url:
  				model_name += "-384"
  		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  				model_name += "-22k"
  		if "22k" in checkpoint_url and "1k" in checkpoint_url:
  				model_name += "-22k-1k"
  
  		model.push_to_hub(
  		    repo_path_or_name=Path(UpperCamelCase			,UpperCamelCase	)			,organization='''nielsr'''			,commit_message='''Add model'''			,)
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      Optional[int] 						=  argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    """--checkpoint_url""",
  	    default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
  	    type=str,
  	    help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
  	)
  	parser.add_argument(
  	    """--pytorch_dump_folder_path""",
  	    default=None,
  	    type=str,
  	    required=True,
  	    help="""Path to the output PyTorch model directory.""",
  	)
  
  	_UpperCAmelCase   :      List[str] 						=  parser.parse_args()
  	convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import unittest
  
  from transformers.image_processing_utils import get_size_dict
  
  class 			UpperCAmelCase				(					unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def _lowercase  (    self     )				->    Dict:
  				# Test a dict with the wrong keys raises an error
  				_UpperCamelCase       : List[Any]    =      {'''wrong_key''': 224}
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(_snake_case     )
  
  				_UpperCamelCase       : Dict    =      {'''height''': 224}
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(_snake_case     )
  
  				_UpperCamelCase       : str    =      {'''width''': 224, '''shortest_edge''': 224}
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(_snake_case     )
  
  				# Test a dict with the correct keys is returned as is
  				_UpperCamelCase       : Any    =      {'''height''': 224, '''width''': 224}
  				_UpperCamelCase       : Optional[int]    =      get_size_dict(_snake_case     )
  				self.assertEqual(_snake_case  ,					_snake_case     )
  
  				_UpperCamelCase       : Optional[Any]    =      {'''shortest_edge''': 224}
  				_UpperCamelCase       : int    =      get_size_dict(_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224}     )
  
  				_UpperCamelCase       : int    =      {'''longest_edge''': 224, '''shortest_edge''': 224}
  				_UpperCamelCase       : Tuple    =      get_size_dict(_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''longest_edge''': 224, '''shortest_edge''': 224}     )
  
  				# Test a single int value which  represents (size, size)
  				_UpperCamelCase       : Any    =      get_size_dict(224     )
  				self.assertEqual(_snake_case  ,					{'''height''': 224, '''width''': 224}     )
  
  				# Test a single int value which represents the shortest edge
  				_UpperCamelCase       : List[Any]    =      get_size_dict(224  ,					default_to_square=_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224}     )
  
  				# Test a tuple of ints which represents (height, width)
  				_UpperCamelCase       : Any    =      get_size_dict((150, 200)     )
  				self.assertEqual(_snake_case  ,					{'''height''': 150, '''width''': 200}     )
  
  				# Test a tuple of ints which represents (width, height)
  				_UpperCamelCase       : List[str]    =      get_size_dict((150, 200)  ,					height_width_order=_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''height''': 200, '''width''': 150}     )
  
  				# Test an int representing the shortest edge and max_size which represents the longest edge
  				_UpperCamelCase       : Any    =      get_size_dict(224  ,					max_size=256  ,					default_to_square=_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224, '''longest_edge''': 256}     )
  
  				# Test int with default_to_square=True and max_size fails
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(224  ,					max_size=256  ,					default_to_square=_snake_case     )
  
  
  
  
  
  