



'''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()
_UpperCamelCase							       =		logging.get_logger(__name__)

def  a_  (							_lowerCAmelCase )   ->   Union[str, Any]:
  __lowerCamelCase : Dict					 =				ConvNextConfig()

  if "tiny" in checkpoint_url:
    __lowerCamelCase : Optional[Any]					 =				[3, 3, 9, 3]
    __lowerCamelCase : Tuple					 =				[96, 192, 384, 768]
  if "small" in checkpoint_url:
    __lowerCamelCase : Dict					 =				[3, 3, 27, 3]
    __lowerCamelCase : Optional[int]					 =				[96, 192, 384, 768]
  if "base" in checkpoint_url:
    __lowerCamelCase : str					 =				[3, 3, 27, 3]
    __lowerCamelCase : Optional[Any]					 =				[128, 256, 512, 1024]
  if "large" in checkpoint_url:
    __lowerCamelCase : Any					 =				[3, 3, 27, 3]
    __lowerCamelCase : Dict					 =				[192, 384, 768, 1536]
  if "xlarge" in checkpoint_url:
    __lowerCamelCase : List[Any]					 =				[3, 3, 27, 3]
    __lowerCamelCase : Optional[Any]					 =				[256, 512, 1024, 2048]

  if "1k" in checkpoint_url:
    __lowerCamelCase : Optional[int]					 =				1000
    __lowerCamelCase : Dict					 =				'imagenet-1k-id2label.json'
    __lowerCamelCase : Optional[Any]					 =				(1, 1000)
  else:
    __lowerCamelCase : Optional[Any]					 =				21841
    __lowerCamelCase : Optional[Any]					 =				'imagenet-22k-id2label.json'
    __lowerCamelCase : Dict					 =				(1, 21841)

  __lowerCamelCase : Optional[int]					 =				'huggingface/label-files'
  __lowerCamelCase : Tuple					 =				num_labels
  __lowerCamelCase : str					 =				json.load(open(hf_hub_download(_lowerCAmelCase ,_lowerCAmelCase ,repo_type='dataset' ) ,'r' ) )
  __lowerCamelCase : Tuple					 =				{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[15027]
  __lowerCamelCase : Optional[int]					 =				idalabel
  __lowerCamelCase : Optional[Any]					 =				{v: k for k, v in idalabel.items()}
  __lowerCamelCase : List[Any]					 =				hidden_sizes
  __lowerCamelCase : Optional[int]					 =				depths

  return config, expected_shape

def  a_  (							_lowerCAmelCase )   ->   Dict:
  if "downsample_layers.0.0" in name:
    __lowerCamelCase : Optional[int]					 =				name.replace('downsample_layers.0.0' ,'embeddings.patch_embeddings' )
  if "downsample_layers.0.1" in name:
    __lowerCamelCase : Any					 =				name.replace('downsample_layers.0.1' ,'embeddings.norm' )  # we rename to layernorm later on
  if "downsample_layers.1.0" in name:
    __lowerCamelCase : List[Any]					 =				name.replace('downsample_layers.1.0' ,'stages.1.downsampling_layer.0' )
  if "downsample_layers.1.1" in name:
    __lowerCamelCase : int					 =				name.replace('downsample_layers.1.1' ,'stages.1.downsampling_layer.1' )
  if "downsample_layers.2.0" in name:
    __lowerCamelCase : Optional[Any]					 =				name.replace('downsample_layers.2.0' ,'stages.2.downsampling_layer.0' )
  if "downsample_layers.2.1" in name:
    __lowerCamelCase : str					 =				name.replace('downsample_layers.2.1' ,'stages.2.downsampling_layer.1' )
  if "downsample_layers.3.0" in name:
    __lowerCamelCase : List[Any]					 =				name.replace('downsample_layers.3.0' ,'stages.3.downsampling_layer.0' )
  if "downsample_layers.3.1" in name:
    __lowerCamelCase : List[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.
    __lowerCamelCase : Optional[int]					 =				name[: len('stages.0' )] + '.layers' + name[len('stages.0' ) :]
  if "stages" in name:
    __lowerCamelCase : Union[str, Any]					 =				name.replace('stages' ,'encoder.stages' )
  if "norm" in name:
    __lowerCamelCase : List[str]					 =				name.replace('norm' ,'layernorm' )
  if "gamma" in name:
    __lowerCamelCase : List[Any]					 =				name.replace('gamma' ,'layer_scale_parameter' )
  if "head" in name:
    __lowerCamelCase : Union[str, Any]					 =				name.replace('head' ,'classifier' )

  return name

def  a_  (							)   ->   Optional[Any]:
  __lowerCamelCase : str					 =				'http://images.cocodataset.org/val2017/000000039769.jpg'
  __lowerCamelCase : Tuple					 =				Image.open(requests.get(_lowerCAmelCase ,stream=_lowerCAmelCase ).raw )
  return im

@torch.no_grad()
def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   str:
  __lowerCamelCase ,__lowerCamelCase : int					 =				get_convnext_config(_lowerCAmelCase )
  # load original state_dict from URL
  __lowerCamelCase : int					 =				torch.hub.load_state_dict_from_url(_lowerCAmelCase )['model']
  # rename keys
  for key in state_dict.copy().keys():
    __lowerCamelCase : Any					 =				state_dict.pop(_lowerCAmelCase )
    __lowerCamelCase : int					 =				val
  # add prefix to all keys expect classifier head
  for key in state_dict.copy().keys():
    __lowerCamelCase : Tuple					 =				state_dict.pop(_lowerCAmelCase )
    if not key.startswith('classifier' ):
      __lowerCamelCase : str					 =				'convnext.' + key
    __lowerCamelCase : str					 =				val

  # load HuggingFace model
  __lowerCamelCase : int					 =				ConvNextForImageClassification(_lowerCAmelCase )
  model.load_state_dict(_lowerCAmelCase )
  model.eval()

  # Check outputs on an image, prepared by ConvNextImageProcessor
  __lowerCamelCase : Dict					 =				224 if '224' in checkpoint_url else 384
  __lowerCamelCase : Union[str, Any]					 =				ConvNextImageProcessor(size=_lowerCAmelCase )
  __lowerCamelCase : str					 =				image_processor(images=prepare_img() ,return_tensors='pt' ).pixel_values

  __lowerCamelCase : List[Any]					 =				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":
    __lowerCamelCase : Any					 =				torch.tensor([-0.1210, -0.6605, 0.1918] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
    __lowerCamelCase : Any					 =				torch.tensor([-0.4473, -0.1847, -0.6365] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
    __lowerCamelCase : Dict					 =				torch.tensor([0.4525, 0.7539, 0.0308] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
    __lowerCamelCase : Any					 =				torch.tensor([0.3561, 0.6350, -0.0384] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
    __lowerCamelCase : str					 =				torch.tensor([0.4174, -0.0989, 0.1489] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
    __lowerCamelCase : Any					 =				torch.tensor([0.2513, -0.1349, -0.1613] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
    __lowerCamelCase : Optional[Any]					 =				torch.tensor([1.2980, 0.3631, -0.1198] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
    __lowerCamelCase : Union[str, Any]					 =				torch.tensor([1.2963, 0.1227, 0.1723] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
    __lowerCamelCase : List[str]					 =				torch.tensor([1.7956, 0.8390, 0.2820] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
    __lowerCamelCase : Union[str, Any]					 =				torch.tensor([-0.2822, -0.0502, -0.0878] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
    __lowerCamelCase : Dict					 =				torch.tensor([-0.5672, -0.0730, -0.4348] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
    __lowerCamelCase : int					 =				torch.tensor([0.2681, 0.2365, 0.6246] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
    __lowerCamelCase : 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":
    __lowerCamelCase : Optional[Any]					 =				torch.tensor([-0.6677, -0.1873, -0.8379] )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
    __lowerCamelCase : Optional[Any]					 =				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...' )
  __lowerCamelCase : int					 =				'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__":
   _UpperCamelCase							       =		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.',
   )

   _UpperCamelCase							       =		parser.parse_args()
   convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase							       =		{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizerFast']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez import BarthezTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez_fast import BarthezTokenizerFast

else:
   import sys

   _UpperCamelCase							       =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






