from hashlib import mda, shaaaa

lowerCAmelCase__  	=      (shaaaa, mda)
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_ = 8       ):
		_lowercase   =0b0
		_lowercase   =size


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.hash_(lowerCAmelCase_       )
		self.bitarray |= h


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.hash_(lowerCAmelCase_       )
		return (h & self.bitarray) == h


	def __contains__(   self       ,  lowerCAmelCase_       ):
		return self.exists(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =bin(lowerCAmelCase_       )[2:]
		return res.zfill(self.size       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self.format_bin(self.bitarray       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =0b0
		for func in HASH_FUNCTIONS:
			_lowercase   =(
			    int.from_bytes(func(value.encode()       ).digest()       ,  "little"       ) % self.size
			)
			res |= 2**position
		return res


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		return self.format_bin(self.hash_(lowerCAmelCase_       )       )


	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   =bin(self.bitarray       ).count("1"       )
		return (n_ones / self.size) ** len(lowerCAmelCase_       )





import argparse

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


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					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
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			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.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)