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()
  _a	 =							logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      Any      =					ConvNextConfig()
  
     if "tiny" in checkpoint_url:
        UpperCAmelCase_      :      Any      =					[3, 3, 9, 3]
        UpperCAmelCase_      :      List[Any]      =					[96, 192, 384, 768]
     if "small" in checkpoint_url:
        UpperCAmelCase_      :      List[Any]      =					[3, 3, 27, 3]
        UpperCAmelCase_      :      int      =					[96, 192, 384, 768]
     if "base" in checkpoint_url:
        UpperCAmelCase_      :      int      =					[3, 3, 27, 3]
        UpperCAmelCase_      :      Tuple      =					[128, 256, 512, 1024]
     if "large" in checkpoint_url:
        UpperCAmelCase_      :      Tuple      =					[3, 3, 27, 3]
        UpperCAmelCase_      :      str      =					[192, 384, 768, 1536]
     if "xlarge" in checkpoint_url:
        UpperCAmelCase_      :      str      =					[3, 3, 27, 3]
        UpperCAmelCase_      :      Tuple      =					[256, 512, 1024, 2048]
  
     if "1k" in checkpoint_url:
        UpperCAmelCase_      :      Union[str, Any]      =					1000
        UpperCAmelCase_      :      List[Any]      =					'''imagenet-1k-id2label.json'''
        UpperCAmelCase_      :      Union[str, Any]      =					(1, 1000)
     else:
        UpperCAmelCase_      :      Optional[Any]      =					2_1841
        UpperCAmelCase_      :      str      =					'''imagenet-22k-id2label.json'''
        UpperCAmelCase_      :      Optional[int]      =					(1, 2_1841)
  
     UpperCAmelCase_      :      Any      =					'''huggingface/label-files'''
     UpperCAmelCase_      :      Any      =					num_labels
     UpperCAmelCase_      :      Tuple      =					json.load(open(hf_hub_download(__lowerCamelCase,	__lowerCamelCase,	repo_type="dataset"						),	"r"						)						)
     UpperCAmelCase_      :      int      =					{int(__lowerCamelCase						): 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[1_5027]
     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 			__a  (	__lowerCamelCase						):
     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 			__a  (	):
     UpperCAmelCase_      :      Union[str, Any]      =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_      :      Union[str, Any]      =					Image.open(requests.get(__lowerCamelCase,	stream=__lowerCamelCase						).raw						)
     return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      int      =					get_convnext_config(__lowerCamelCase						)
     # load original state_dict from URL
     UpperCAmelCase_      :      Any      =					torch.hub.load_state_dict_from_url(__lowerCamelCase						)['''model''']
     # rename keys
     for key in state_dict.copy().keys():
        UpperCAmelCase_      :      List[str]      =					state_dict.pop(__lowerCamelCase						)
        UpperCAmelCase_      :      Tuple      =					val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
        UpperCAmelCase_      :      List[Any]      =					state_dict.pop(__lowerCamelCase						)
        if not key.startswith("classifier"						):
           UpperCAmelCase_      :      Dict      =					'''convnext.''' + key
        UpperCAmelCase_      :      Optional[int]      =					val
  
     # load HuggingFace model
     UpperCAmelCase_      :      Dict      =					ConvNextForImageClassification(__lowerCamelCase						)
     model.load_state_dict(__lowerCamelCase						)
     model.eval()
  
     # Check outputs on an image, prepared by ConvNextImageProcessor
     UpperCAmelCase_      :      Optional[int]      =					224 if '''224''' in checkpoint_url else 384
     UpperCAmelCase_      :      int      =					ConvNextImageProcessor(size=__lowerCamelCase						)
     UpperCAmelCase_      :      Union[str, Any]      =					image_processor(images=prepare_img(),	return_tensors="pt"						).pixel_values
  
     UpperCAmelCase_      :      str      =					model(__lowerCamelCase						).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],	__lowerCamelCase,	atol=1E-3						)
     assert logits.shape == expected_shape
  
     Path(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)
     print(f"""Saving model to {pytorch_dump_folder_path}"""						)
     model.save_pretrained(__lowerCamelCase						)
     print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
     image_processor.save_pretrained(__lowerCamelCase						)
  
     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(__lowerCamelCase,	__lowerCamelCase						),	organization="nielsr",	commit_message="Add model",	)
  
  
  if __name__ == "__main__":
      _a	 =							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.',
      )
  
      _a	 =							parser.parse_args()
      convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from hashlib import mda, shaaaa
  
  _a	 =							(shaaaa, mda)
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_ = 8						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       UpperCAmelCase_      :      Union[str, Any]      =					size
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.hash_(lowercase_						)
       self.bitarray |= h
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					self.hash_(lowercase_						)
       return (h & self.bitarray) == h
  
  
  
  
  
  
  
    def __contains__(	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.exists(lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					bin(lowercase_						)[2:]
       return res.zfill(self.size						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return self.format_bin(self.bitarray						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       for func in HASH_FUNCTIONS:
          UpperCAmelCase_      :      str      =					(
              int.from_bytes(func(value.encode()						).digest()       ,     "little"						) % self.size
          )
          res |= 2**position
       return res
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.format_bin(self.hash_(lowercase_						)						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					bin(self.bitarray						).count("1"						)
       return (n_ones / self.size) ** len(lowercase_						)
  
  
  