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()
  snake_case_    						=				logging.get_logger(__name__)
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					lowercase__					:     str   = ConvNextVaConfig()
  
  					if "atto" in checkpoint_url:
  										lowercase__					:     Union[str, Any]   = [2, 2, 6, 2]
  										lowercase__					:     Tuple   = [40, 80, 160, 320]
  					if "femto" in checkpoint_url:
  										lowercase__					:     List[str]   = [2, 2, 6, 2]
  										lowercase__					:     Dict   = [48, 96, 192, 384]
  					if "pico" in checkpoint_url:
  										lowercase__					:     int   = [2, 2, 6, 2]
  										lowercase__					:     int   = [64, 128, 256, 512]
  					if "nano" in checkpoint_url:
  										lowercase__					:     Union[str, Any]   = [2, 2, 8, 2]
  										lowercase__					:     Dict   = [80, 160, 320, 640]
  					if "tiny" in checkpoint_url:
  										lowercase__					:     Optional[Any]   = [3, 3, 9, 3]
  										lowercase__					:     List[Any]   = [96, 192, 384, 768]
  					if "base" in checkpoint_url:
  										lowercase__					:     str   = [3, 3, 27, 3]
  										lowercase__					:     int   = [128, 256, 512, 1_024]
  					if "large" in checkpoint_url:
  										lowercase__					:     Optional[Any]   = [3, 3, 27, 3]
  										lowercase__					:     Union[str, Any]   = [192, 384, 768, 1_536]
  					if "huge" in checkpoint_url:
  										lowercase__					:     Optional[int]   = [3, 3, 27, 3]
  										lowercase__					:     int   = [352, 704, 1_408, 2_816]
  
  					lowercase__					:     List[Any]   = 1_000
  					lowercase__					:     str   = '''imagenet-1k-id2label.json'''
  					lowercase__					:     Optional[Any]   = (1, 1_000)
  
  					lowercase__					:     Optional[int]   = '''huggingface/label-files'''
  					lowercase__					:     Any   = num_labels
  					lowercase__					:     Dict   = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__			,  repo_type='dataset'    )			,  'r'    )    )
  					lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
  
  					lowercase__					:     Optional[int]   = idalabel
  					lowercase__					:     Optional[Any]   = {v: k for k, v in idalabel.items()}
  					lowercase__					:     Dict   = hidden_sizes
  					lowercase__					:     Optional[int]   = depths
  
  					return config, expected_shape
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					if "downsample_layers.0.0" in name:
  										lowercase__					:     Any   = name.replace('downsample_layers.0.0'			,  'embeddings.patch_embeddings'    )
  					if "downsample_layers.0.1" in name:
  										lowercase__					:     List[Any]   = name.replace('downsample_layers.0.1'			,  'embeddings.norm'    )  # we rename to layernorm later on
  					if "downsample_layers.1.0" in name:
  										lowercase__					:     str   = name.replace('downsample_layers.1.0'			,  'stages.1.downsampling_layer.0'    )
  					if "downsample_layers.1.1" in name:
  										lowercase__					:     Union[str, Any]   = name.replace('downsample_layers.1.1'			,  'stages.1.downsampling_layer.1'    )
  					if "downsample_layers.2.0" in name:
  										lowercase__					:     List[str]   = name.replace('downsample_layers.2.0'			,  'stages.2.downsampling_layer.0'    )
  					if "downsample_layers.2.1" in name:
  										lowercase__					:     Union[str, Any]   = name.replace('downsample_layers.2.1'			,  'stages.2.downsampling_layer.1'    )
  					if "downsample_layers.3.0" in name:
  										lowercase__					:     Any   = name.replace('downsample_layers.3.0'			,  'stages.3.downsampling_layer.0'    )
  					if "downsample_layers.3.1" in name:
  										lowercase__					:     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.
  										lowercase__					:     List[Any]   = name[: len('stages.0'    )] + '''.layers''' + name[len('stages.0'    ) :]
  					if "gamma" in name:
  										lowercase__					:     Union[str, Any]   = name.replace('gamma'			,  'weight'    )
  					if "beta" in name:
  										lowercase__					:     Optional[int]   = name.replace('beta'			,  'bias'    )
  					if "stages" in name:
  										lowercase__					:     Union[str, Any]   = name.replace('stages'			,  'encoder.stages'    )
  					if "norm" in name:
  										lowercase__					:     List[str]   = name.replace('norm'			,  'layernorm'    )
  					if "head" in name:
  										lowercase__					:     List[Any]   = name.replace('head'			,  'classifier'    )
  
  					return name
  
  def 			snake_case__    (					):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					lowercase__					:     Any   = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  					lowercase__					:     Union[str, Any]   = Image.open(requests.get(SCREAMING_SNAKE_CASE__			,  stream=SCREAMING_SNAKE_CASE__    ).raw    )
  					return im
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					if "224" in checkpoint_url:
  										lowercase__					:     Optional[int]   = 224
  										lowercase__					:     List[str]   = 224 / 256
  					elif "384" in checkpoint_url:
  										lowercase__					:     int   = 384
  										lowercase__					:     Tuple   = None
  					else:
  										lowercase__					:     Tuple   = 512
  										lowercase__					:     Tuple   = None
  
  					return ConvNextImageProcessor(
  					    size=SCREAMING_SNAKE_CASE__			,  crop_pct=SCREAMING_SNAKE_CASE__			,  image_mean=[0.485, 0.456, 0.406]			,  image_std=[0.229, 0.224, 0.225]			,  resample=PILImageResampling.BICUBIC			,  )
  
  @torch.no_grad()
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					print('Downloading original model from checkpoint...'    )
  					# define ConvNeXTV2 configuration based on URL
  					lowercase__					:     Dict   = get_convnextva_config(SCREAMING_SNAKE_CASE__    )
  					# load original state_dict from URL
  					lowercase__					:     Optional[Any]   = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__    )['''model''']
  
  					print('Converting model parameters...'    )
  					# rename keys
  					for key in state_dict.copy().keys():
  										lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE__    )
  										lowercase__					:     str   = val
  					# add prefix to all keys expect classifier head
  					for key in state_dict.copy().keys():
  										lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE__    )
  										if not key.startswith('classifier'    ):
  															lowercase__					:     Tuple   = '''convnextv2.''' + key
  										lowercase__					:     List[Any]   = val
  
  					# load HuggingFace model
  					lowercase__					:     Any   = ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE__    )
  					model.load_state_dict(SCREAMING_SNAKE_CASE__    )
  					model.eval()
  
  					# Check outputs on an image, prepared by ConvNextImageProcessor
  					lowercase__					:     Union[str, Any]   = convert_preprocessor(SCREAMING_SNAKE_CASE__    )
  					lowercase__					:     Any   = preprocessor(images=prepare_img()			,  return_tensors='pt'    )
  					lowercase__					:     Dict   = model(**SCREAMING_SNAKE_CASE__    ).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":
  										lowercase__					:     Tuple   = torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  										lowercase__					:     Dict   = torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  										lowercase__					:     Optional[Any]   = torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  										lowercase__					:     Dict   = torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  										lowercase__					:     List[str]   = torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  										lowercase__					:     Tuple   = torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  										lowercase__					:     Tuple   = torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  										lowercase__					:     Union[str, Any]   = torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  										lowercase__					:     Dict   = torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  										lowercase__					:     Optional[Any]   = torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  										lowercase__					:     Any   = torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  										lowercase__					:     Dict   = torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  										lowercase__					:     Union[str, Any]   = torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  										lowercase__					:     List[str]   = torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  										lowercase__					:     Optional[Any]   = torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  										lowercase__					:     Any   = torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  										lowercase__					:     Any   = torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]    )
  					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  										lowercase__					:     List[str]   = torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]    )
  					else:
  										raise ValueError(f"""Unknown URL: {checkpoint_url}"""    )
  
  					assert torch.allclose(logits[0, :5]			,  SCREAMING_SNAKE_CASE__			,  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(SCREAMING_SNAKE_CASE__    ):
  															os.mkdir(SCREAMING_SNAKE_CASE__    )
  
  										model.save_pretrained(SCREAMING_SNAKE_CASE__    )
  										preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__    )
  
  					lowercase__					:     Optional[Any]   = '''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(SCREAMING_SNAKE_CASE__    )
  										preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__    )
  
  
  if __name__ == "__main__":
  							snake_case_    						=				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''')
  
  							snake_case_    						=				parser.parse_args()
  							convert_convnextva_checkpoint(
  							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  							)
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  