"""simple docstring"""



from .number_theory.prime_numbers import next_prime

class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 def __init__(   self		:      Any					,   _snake_case		:      int					,   _snake_case		:      int | None = None					,   _snake_case		:      float | None = None					,   )				->			None:




    '''simple docstring'''






    a__     				=		size_table
    a__     				=		[None] * self.size_table
    a__     				=		0.75 if lim_charge is None else lim_charge
    a__     				=		1 if charge_factor is None else charge_factor
    a__     				=		[]
    a__     				=		{}


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




    '''simple docstring'''






    return self._keys


 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			Dict:




    '''simple docstring'''






    return sum(1 for slot in self.values if slot is not None				) / (
        self.size_table * self.charge_factor
    )


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




    '''simple docstring'''






    return key % self.size_table


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




    '''simple docstring'''






    print(F'''step {step_ord}'''				)
    print(list(range(len(self.values				)				)				)				)
    print(self.values				)


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




    '''simple docstring'''






    a__     				=		1
    a__     				=		values
    for value in values:
       self.insert_data(_snake_case				)
       self._step_by_step(_snake_case				)
       i += 1


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




    '''simple docstring'''






    a__     				=		data
    a__     				=		data


 def        _lowerCAmelCase				(   self		:      List[str]					,   _snake_case		:      str					,   _snake_case		:      Union[str, Any]=None				)				->			List[str]:




    '''simple docstring'''






    a__     				=		self.hash_function(key + 1				)

    while self.values[new_key] is not None and self.values[new_key] != key:
       if self.values.count(_snake_case				) > 0:
          a__     				=		self.hash_function(new_key + 1				)
       else:
          a__     				=		None
          break

    return new_key


 def        _lowerCAmelCase				(   self		:      Dict				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		[value for value in self.values if value is not None]
    a__     				=		next_prime(self.size_table					,   factor=2				)
    self._keys.clear()
    a__     				=		[None] * self.size_table  # hell's pointers D: don't DRY ;/
    for value in survivor_values:
       self.insert_data(_snake_case				)



 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		self.hash_function(_snake_case				)

    if self.values[key] is None:
       self._set_value(_snake_case					,   _snake_case				)

    elif self.values[key] == data:
       pass

    else:
       a__     				=		self._collision_resolution(_snake_case					,   _snake_case				)
       if collision_resolution is not None:
          self._set_value(_snake_case					,   _snake_case				)
       else:
          self.rehashing()
          self.insert_data(_snake_case				)



"""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)


