code: 
  
  
  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()
  lowercase_								=    logging.get_logger(__name__)
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[int]:
  						_a     =			ConvNextConfig()
  
  						if "tiny" in checkpoint_url:
  												_a     =			[3, 3, 9, 3]
  												_a     =			[96, 192, 384, 768]
  						if "small" in checkpoint_url:
  												_a     =			[3, 3, 27, 3]
  												_a     =			[96, 192, 384, 768]
  						if "base" in checkpoint_url:
  												_a     =			[3, 3, 27, 3]
  												_a     =			[128, 256, 512, 1024]
  						if "large" in checkpoint_url:
  												_a     =			[3, 3, 27, 3]
  												_a     =			[192, 384, 768, 1536]
  						if "xlarge" in checkpoint_url:
  												_a     =			[3, 3, 27, 3]
  												_a     =			[256, 512, 1024, 2048]
  
  						if "1k" in checkpoint_url:
  												_a     =			1000
  												_a     =			'imagenet-1k-id2label.json'
  												_a     =			(1, 1000)
  						else:
  												_a     =			21841
  												_a     =			'imagenet-22k-id2label.json'
  												_a     =			(1, 21841)
  
  						_a     =			'huggingface/label-files'
  						_a     =			num_labels
  						_a     =			json.load(open(hf_hub_download(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)							,     'r'				)				)
  						_a     =			{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[9205]
  												del idalabel[15027]
  						_a     =			idalabel
  						_a     =			{v: k for k, v in idalabel.items()}
  						_a     =			hidden_sizes
  						_a     =			depths
  
  						return config, expected_shape
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Union[str, Any]:
  						if "downsample_layers.0.0" in name:
  												_a     =			name.replace('downsample_layers.0.0'							,     'embeddings.patch_embeddings'				)
  						if "downsample_layers.0.1" in name:
  												_a     =			name.replace('downsample_layers.0.1'							,     'embeddings.norm'				)  # we rename to layernorm later on
  						if "downsample_layers.1.0" in name:
  												_a     =			name.replace('downsample_layers.1.0'							,     'stages.1.downsampling_layer.0'				)
  						if "downsample_layers.1.1" in name:
  												_a     =			name.replace('downsample_layers.1.1'							,     'stages.1.downsampling_layer.1'				)
  						if "downsample_layers.2.0" in name:
  												_a     =			name.replace('downsample_layers.2.0'							,     'stages.2.downsampling_layer.0'				)
  						if "downsample_layers.2.1" in name:
  												_a     =			name.replace('downsample_layers.2.1'							,     'stages.2.downsampling_layer.1'				)
  						if "downsample_layers.3.0" in name:
  												_a     =			name.replace('downsample_layers.3.0'							,     'stages.3.downsampling_layer.0'				)
  						if "downsample_layers.3.1" in name:
  												_a     =			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.
  												_a     =			name[: len('stages.0'				)] + '.layers' + name[len('stages.0'				) :]
  						if "stages" in name:
  												_a     =			name.replace('stages'							,     'encoder.stages'				)
  						if "norm" in name:
  												_a     =			name.replace('norm'							,     'layernorm'				)
  						if "gamma" in name:
  												_a     =			name.replace('gamma'							,     'layer_scale_parameter'				)
  						if "head" in name:
  												_a     =			name.replace('head'							,     'classifier'				)
  
  						return name
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  int:
  						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)
  						return im
  
  
  @torch.no_grad()
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Optional[int]:
  						_a ,	_a     =			get_convnext_config(_UpperCAmelCase				)
  						# load original state_dict from URL
  						_a     =			torch.hub.load_state_dict_from_url(_UpperCAmelCase				)['model']
  						# rename keys
  						for key in state_dict.copy().keys():
  												_a     =			state_dict.pop(_UpperCAmelCase				)
  												_a     =			val
  						# add prefix to all keys expect classifier head
  						for key in state_dict.copy().keys():
  												_a     =			state_dict.pop(_UpperCAmelCase				)
  												if not key.startswith('classifier'				):
  																		_a     =			'convnext.' + key
  												_a     =			val
  
  						# load HuggingFace model
  						_a     =			ConvNextForImageClassification(_UpperCAmelCase				)
  						model.load_state_dict(_UpperCAmelCase				)
  						model.eval()
  
  						# Check outputs on an image, prepared by ConvNextImageProcessor
  						_a     =			224 if '224' in checkpoint_url else 384
  						_a     =			ConvNextImageProcessor(size=_UpperCAmelCase				)
  						_a     =			image_processor(images=prepare_img()							,     return_tensors='pt'				).pixel_values
  
  						_a     =			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":
  												_a     =			torch.tensor([-0.1210, -0.6605, 0.1918]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  												_a     =			torch.tensor([-0.4473, -0.1847, -0.6365]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  												_a     =			torch.tensor([0.4525, 0.7539, 0.0308]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  												_a     =			torch.tensor([0.3561, 0.6350, -0.0384]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  												_a     =			torch.tensor([0.4174, -0.0989, 0.1489]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  												_a     =			torch.tensor([0.2513, -0.1349, -0.1613]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  												_a     =			torch.tensor([1.2980, 0.3631, -0.1198]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  												_a     =			torch.tensor([1.2963, 0.1227, 0.1723]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  												_a     =			torch.tensor([1.7956, 0.8390, 0.2820]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  												_a     =			torch.tensor([-0.2822, -0.0502, -0.0878]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  												_a     =			torch.tensor([-0.5672, -0.0730, -0.4348]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  												_a     =			torch.tensor([0.2681, 0.2365, 0.6246]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  												_a     =			torch.tensor([-0.2642, 0.3931, 0.5116]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  												_a     =			torch.tensor([-0.6677, -0.1873, -0.8379]				)
  						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  												_a     =			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...'				)
  						_a     =			'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__":
  		lowercase_								=    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.',
  		)
  
  		lowercase_								=    parser.parse_args()
  		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
style_context: 
  
  
  from math import pi
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						return degree / (180 / pi)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  
  
  