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()
  __lowerCAmelCase							:			List[str]				=logging.get_logger(__name__)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    ConvNextConfig()
  
      if "tiny" in checkpoint_url:
          A__       =    [3, 3, 9, 3]
          A__       =    [96, 1_92, 3_84, 7_68]
      if "small" in checkpoint_url:
          A__       =    [3, 3, 27, 3]
          A__       =    [96, 1_92, 3_84, 7_68]
      if "base" in checkpoint_url:
          A__       =    [3, 3, 27, 3]
          A__       =    [1_28, 2_56, 5_12, 10_24]
      if "large" in checkpoint_url:
          A__       =    [3, 3, 27, 3]
          A__       =    [1_92, 3_84, 7_68, 15_36]
      if "xlarge" in checkpoint_url:
          A__       =    [3, 3, 27, 3]
          A__       =    [2_56, 5_12, 10_24, 20_48]
  
      if "1k" in checkpoint_url:
          A__       =    10_00
          A__       =    '''imagenet-1k-id2label.json'''
          A__       =    (1, 10_00)
      else:
          A__       =    2_18_41
          A__       =    '''imagenet-22k-id2label.json'''
          A__       =    (1, 2_18_41)
  
      A__       =    '''huggingface/label-files'''
      A__       =    num_labels
      A__       =    json.load(open(hf_hub_download(_lowerCamelCase		, _lowerCamelCase		, repo_type="dataset" )		, "r" ) )
      A__       =    {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[92_05]
          del idalabel[1_50_27]
      A__       =    idalabel
      A__       =    {v: k for k, v in idalabel.items()}
      A__       =    hidden_sizes
      A__       =    depths
  
      return config, expected_shape
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[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 		UpperCamelCase       (							):
      A__       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      A__       =    Image.open(requests.get(_lowerCamelCase		, stream=_lowerCamelCase ).raw )
      return im
  
  
  
  
  
  @torch.no_grad()
  def 		UpperCamelCase       (							_lowerCamelCase      :      int		, _lowerCamelCase      :      Union[str, Any] ):
      A__       =    get_convnext_config(_lowerCamelCase )
      # load original state_dict from URL
      A__       =    torch.hub.load_state_dict_from_url(_lowerCamelCase )['''model''']
      # rename keys
      for key in state_dict.copy().keys():
          A__       =    state_dict.pop(_lowerCamelCase )
          A__       =    val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
          A__       =    state_dict.pop(_lowerCamelCase )
          if not key.startswith("classifier" ):
              A__       =    '''convnext.''' + key
          A__       =    val
  
      # load HuggingFace model
      A__       =    ConvNextForImageClassification(_lowerCamelCase )
      model.load_state_dict(_lowerCamelCase )
      model.eval()
  
      # Check outputs on an image, prepared by ConvNextImageProcessor
      A__       =    2_24 if '''224''' in checkpoint_url else 3_84
      A__       =    ConvNextImageProcessor(size=_lowerCamelCase )
      A__       =    image_processor(images=prepare_img()		, return_tensors="pt" ).pixel_values
  
      A__       =    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":
          A__       =    torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          A__       =    torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          A__       =    torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          A__       =    torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          A__       =    torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          A__       =    torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          A__       =    torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          A__       =    torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          A__       =    torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          A__       =    torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          A__       =    torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          A__       =    torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          A__       =    torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          A__       =    torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9] )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          A__       =    torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4] )
      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..." )
      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(_lowerCamelCase		, _lowerCamelCase )		, organization="nielsr"		, commit_message="Add model"		, )
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			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.",
    )
  
    __lowerCAmelCase							:			List[str]				=parser.parse_args()
    convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  __lowerCAmelCase							:			Dict				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
       warnings.warn(
           "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use ViltImageProcessor instead."		, lowercase_		, )
       super().__init__(*lowercase_		, **lowercase_							)
  
  
  
  