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_     :     Tuple    		=						logging.get_logger(__name__)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      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 _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      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 _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					='http://images.cocodataset.org/val2017/000000039769.jpg'
      a					=Image.open(requests.get(lowerCAmelCase__ ,					stream=lowerCAmelCase__	).raw	)
      return im
  @torch.no_grad()
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      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([-0.1210, -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] ,					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_     :     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[Any]    		=						parser.parse_args()
         convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=TaConfig.from_pretrained(lowercase	)
      a					=FlaxTaForConditionalGeneration(config=lowercase	)
      a					=checkpoints.load_tax_checkpoint(lowercase	)
  
      a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
      # Encoder
      for layer_index in range(config.num_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
          if split_mlp_wi:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_attention_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
          a					=tax_mlp_layer_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_encoder_rel_embedding
  
      # Assigning
      a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      a					=tax_encoder_norm
  
      # Decoder
      for layer_index in range(config.num_decoder_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]
  
          # Encoder-Decoder-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
          # MLP
          if split_mlp_wi:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_pre_attention_layer_norm
  
          a					=tax_enc_dec_attention_key
          a					=tax_enc_dec_attention_out
          a					=tax_enc_dec_attention_query
          a					=tax_enc_dec_attention_value
  
          a					=tax_cross_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
  
          a					=txa_mlp_layer_norm
  
      # Decoder Normalization
      a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      a					=txa_decoder_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_decoder_rel_embedding
  
      # Token Embeddings
      a					=tax_model['''target''']['''token_embedder''']['''embedding''']
      a					=txa_token_embeddings
  
      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
          a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
      flax_model.save_pretrained(lowercase	)
      print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
         )
         parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
         parser.add_argument(
             """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
         )
         lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)