"""simple docstring"""



from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Tuple:


   '''simple docstring'''


   if is_torch_available():
      import torch

      if isinstance(UpperCAmelCase__,torch.Tensor					):
         return True

   return isinstance(UpperCAmelCase__,np.ndarray					)




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      int				)				->			Tuple:




    '''simple docstring'''






    a__     				=		fields(self				)

    # Safety and consistency checks
    if not len(_snake_case				):
       raise ValueError(F'''{self.__class__.__name__} has no fields.'''				)

    a__     				=		getattr(self					,   class_fields[0].name				)
    a__     				=		all(getattr(self					,   field.name				) is None for field in class_fields[1:]				)

    if other_fields_are_none and isinstance(_snake_case					,   _snake_case				):
       for key, value in first_field.items():
          a__     				=		value
    else:
       for field in class_fields:
          a__     				=		getattr(self					,   field.name				)
          if v is not None:
             a__     				=		v


 def __delitem__(   self		:      str					,   *_snake_case		:      List[Any]					,   **_snake_case		:      Dict				)				->			Tuple:




    '''simple docstring'''






    raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''				)


 def        _lowerCAmelCase				(   self		:      int					,   *_snake_case		:      Dict					,   **_snake_case		:      Optional[int]				)				->			List[Any]:




    '''simple docstring'''






    raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''				)


 def        _lowerCAmelCase				(   self		:      Optional[int]					,   *_snake_case		:      Tuple					,   **_snake_case		:      Any				)				->			int:




    '''simple docstring'''






    raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''				)


 def        _lowerCAmelCase				(   self		:      Tuple					,   *_snake_case		:      Any					,   **_snake_case		:      Dict				)				->			Dict:




    '''simple docstring'''






    raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''				)


 def __getitem__(   self		:      Dict					,   _snake_case		:      Optional[int]				)				->			Union[str, Any]:




    '''simple docstring'''






    if isinstance(_snake_case					,   _snake_case				):
       a__     				=		dict(self.items()				)
       return inner_dict[k]
    else:
       return self.to_tuple()[k]


 def __setattr__(   self		:      Optional[Any]					,   _snake_case		:      int					,   _snake_case		:      Any				)				->			int:




    '''simple docstring'''






    if name in self.keys() and value is not None:
       # Don't call self.__setitem__ to avoid recursion errors
       super().__setitem__(_snake_case					,   _snake_case				)
    super().__setattr__(_snake_case					,   _snake_case				)


 def __setitem__(   self		:      Dict					,   _snake_case		:      str					,   _snake_case		:      List[str]				)				->			List[str]:




    '''simple docstring'''






    super().__setitem__(_snake_case					,   _snake_case				)
    # Don't call self.__setattr__ to avoid recursion errors
    super().__setattr__(_snake_case					,   _snake_case				)



 def        _lowerCAmelCase				(   self		:      Dict				)				->			Tuple[Any]:




    '''simple docstring'''






    return tuple(self[k] for k in self.keys()				)



"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


