code: 
  
  
  import argparse
  import json
  import os
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  A_														=      logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       ConvNextVaConfig()
  
  					if "atto" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [2, 2, 6, 2]
  										SCREAMING_SNAKE_CASE_	      =       [40, 80, 160, 320]
  					if "femto" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [2, 2, 6, 2]
  										SCREAMING_SNAKE_CASE_	      =       [48, 96, 192, 384]
  					if "pico" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [2, 2, 6, 2]
  										SCREAMING_SNAKE_CASE_	      =       [64, 128, 256, 512]
  					if "nano" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [2, 2, 8, 2]
  										SCREAMING_SNAKE_CASE_	      =       [80, 160, 320, 640]
  					if "tiny" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [3, 3, 9, 3]
  										SCREAMING_SNAKE_CASE_	      =       [96, 192, 384, 768]
  					if "base" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [3, 3, 27, 3]
  										SCREAMING_SNAKE_CASE_	      =       [128, 256, 512, 1024]
  					if "large" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [3, 3, 27, 3]
  										SCREAMING_SNAKE_CASE_	      =       [192, 384, 768, 1536]
  					if "huge" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       [3, 3, 27, 3]
  										SCREAMING_SNAKE_CASE_	      =       [352, 704, 1408, 2816]
  
  					SCREAMING_SNAKE_CASE_	      =       1000
  					SCREAMING_SNAKE_CASE_	      =       '''imagenet-1k-id2label.json'''
  					SCREAMING_SNAKE_CASE_	      =       (1, 1000)
  
  					SCREAMING_SNAKE_CASE_	      =       '''huggingface/label-files'''
  					SCREAMING_SNAKE_CASE_	      =       num_labels
  					SCREAMING_SNAKE_CASE_	      =       json.load(open(hf_hub_download(UpperCAmelCase				,UpperCAmelCase				,repo_type='''dataset'''      )				,'''r'''      )      )
  					SCREAMING_SNAKE_CASE_	      =       {int(UpperCAmelCase      ): v for k, v in idalabel.items()}
  
  					SCREAMING_SNAKE_CASE_	      =       idalabel
  					SCREAMING_SNAKE_CASE_	      =       {v: k for k, v in idalabel.items()}
  					SCREAMING_SNAKE_CASE_	      =       hidden_sizes
  					SCREAMING_SNAKE_CASE_	      =       depths
  
  					return config, expected_shape
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if "downsample_layers.0.0" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.0.0'''				,'''embeddings.patch_embeddings'''      )
  					if "downsample_layers.0.1" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.0.1'''				,'''embeddings.norm'''      )  # we rename to layernorm later on
  					if "downsample_layers.1.0" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.1.0'''				,'''stages.1.downsampling_layer.0'''      )
  					if "downsample_layers.1.1" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.1.1'''				,'''stages.1.downsampling_layer.1'''      )
  					if "downsample_layers.2.0" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.2.0'''				,'''stages.2.downsampling_layer.0'''      )
  					if "downsample_layers.2.1" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.2.1'''				,'''stages.2.downsampling_layer.1'''      )
  					if "downsample_layers.3.0" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''downsample_layers.3.0'''				,'''stages.3.downsampling_layer.0'''      )
  					if "downsample_layers.3.1" in name:
  										SCREAMING_SNAKE_CASE_	      =       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.
  										SCREAMING_SNAKE_CASE_	      =       name[: len('''stages.0'''      )] + '''.layers''' + name[len('''stages.0'''      ) :]
  					if "gamma" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''gamma'''				,'''weight'''      )
  					if "beta" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''beta'''				,'''bias'''      )
  					if "stages" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''stages'''				,'''encoder.stages'''      )
  					if "norm" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''norm'''				,'''layernorm'''      )
  					if "head" in name:
  										SCREAMING_SNAKE_CASE_	      =       name.replace('''head'''				,'''classifier'''      )
  
  					return name
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  					SCREAMING_SNAKE_CASE_	      =       Image.open(requests.get(UpperCAmelCase				,stream=UpperCAmelCase      ).raw      )
  					return im
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if "224" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       224
  										SCREAMING_SNAKE_CASE_	      =       224 / 256
  					elif "384" in checkpoint_url:
  										SCREAMING_SNAKE_CASE_	      =       384
  										SCREAMING_SNAKE_CASE_	      =       None
  					else:
  										SCREAMING_SNAKE_CASE_	      =       512
  										SCREAMING_SNAKE_CASE_	      =       None
  
  					return ConvNextImageProcessor(
  					    size=UpperCAmelCase				,crop_pct=UpperCAmelCase				,image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]				,image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]				,resample=PILImageResampling.BICUBIC				,)
  
  
  
  
  
  
  
  @torch.no_grad()
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )->		int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					print('''Downloading original model from checkpoint...'''      )
  					# define ConvNeXTV2 configuration based on URL
  					SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       get_convnextva_config(UpperCAmelCase      )
  					# load original state_dict from URL
  					SCREAMING_SNAKE_CASE_	      =       torch.hub.load_state_dict_from_url(UpperCAmelCase      )['''model''']
  
  					print('''Converting model parameters...'''      )
  					# rename keys
  					for key in state_dict.copy().keys():
  										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(UpperCAmelCase      )
  										SCREAMING_SNAKE_CASE_	      =       val
  					# add prefix to all keys expect classifier head
  					for key in state_dict.copy().keys():
  										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(UpperCAmelCase      )
  										if not key.startswith('''classifier'''      ):
  															SCREAMING_SNAKE_CASE_	      =       '''convnextv2.''' + key
  										SCREAMING_SNAKE_CASE_	      =       val
  
  					# load HuggingFace model
  					SCREAMING_SNAKE_CASE_	      =       ConvNextVaForImageClassification(UpperCAmelCase      )
  					model.load_state_dict(UpperCAmelCase      )
  					model.eval()
  
  					# Check outputs on an image, prepared by ConvNextImageProcessor
  					SCREAMING_SNAKE_CASE_	      =       convert_preprocessor(UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       preprocessor(images=prepare_img()				,return_tensors='''pt'''      )
  					SCREAMING_SNAKE_CASE_	      =       model(**UpperCAmelCase      ).logits
  
  					# note: the logits below were obtained without center cropping
  					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]      )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]      )
  					else:
  										raise ValueError(f'''Unknown URL: {checkpoint_url}'''      )
  
  					assert torch.allclose(logits[0, :5]				,UpperCAmelCase				,atol=1E-3      )
  					assert logits.shape == expected_shape
  					print('''Model outputs match the original results!'''      )
  
  					if save_model:
  										print('''Saving model to local...'''      )
  										# Create folder to save model
  										if not os.path.isdir(UpperCAmelCase      ):
  															os.mkdir(UpperCAmelCase      )
  
  										model.save_pretrained(UpperCAmelCase      )
  										preprocessor.save_pretrained(UpperCAmelCase      )
  
  					SCREAMING_SNAKE_CASE_	      =       '''convnextv2'''
  					if "atto" in checkpoint_url:
  										model_name += "-atto"
  					if "femto" in checkpoint_url:
  										model_name += "-femto"
  					if "pico" in checkpoint_url:
  										model_name += "-pico"
  					if "nano" in checkpoint_url:
  										model_name += "-nano"
  					elif "tiny" in checkpoint_url:
  										model_name += "-tiny"
  					elif "base" in checkpoint_url:
  										model_name += "-base"
  					elif "large" in checkpoint_url:
  										model_name += "-large"
  					elif "huge" in checkpoint_url:
  										model_name += "-huge"
  					if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  										model_name += "-22k"
  					elif "22k" in checkpoint_url and "1k" in checkpoint_url:
  										model_name += "-22k-1k"
  					elif "1k" in checkpoint_url:
  										model_name += "-1k"
  					if "224" in checkpoint_url:
  										model_name += "-224"
  					elif "384" in checkpoint_url:
  										model_name += "-384"
  					elif "512" in checkpoint_url:
  										model_name += "-512"
  
  					if push_to_hub:
  										print(f'''Pushing {model_name} to the hub...'''      )
  										model.push_to_hub(UpperCAmelCase      )
  										preprocessor.push_to_hub(UpperCAmelCase      )
  
  
  if __name__ == "__main__":
  		A_														=      argparse.ArgumentParser()
  		# Required parameters
  		parser.add_argument(
  		    "--checkpoint_url",
  		    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
  		    type=str,
  		    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
  		)
  		parser.add_argument(
  		    "--pytorch_dump_folder_path",
  		    default="model",
  		    type=str,
  		    help="Path to the output PyTorch model directory.",
  		)
  		parser.add_argument("--save_model", action="store_true", help="Save model to local")
  		parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")
  
  		A_														=      parser.parse_args()
  		convert_convnextva_checkpoint(
  		    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  		)
  
  
  
style_context: 
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		bool:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if not isinstance(UpperCAmelCase				,UpperCAmelCase      ):
  										SCREAMING_SNAKE_CASE_	      =       f'''Input value of [number={number}] must be an integer'''
  										raise TypeError(UpperCAmelCase      )
  					if number < 0 or number % 2 == 1:
  										return False
  					SCREAMING_SNAKE_CASE_	      =       int(number**0.5      )
  					return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  