

from __future__ import annotations

import sys



class     lowercase_       :



      def __init__(     self :							Any     ,   A__ :							str     ,   A__ :							int						)       ->	int:
        _snake_case		=				letter
        _snake_case		=				freq
        _snake_case		=				{}







      def __repr__(     self :							Optional[int]						)       ->	List[str]:
        return f"""{self.letter}:{self.freq}"""



class     lowercase_       :



      def __init__(     self :							Tuple     ,   A__ :							int     ,   A__ :							Letter | TreeNode     ,   A__ :							Letter | TreeNode						)       ->	Optional[Any]:
        _snake_case		=				freq
        _snake_case		=				left
        _snake_case		=				right



def  snake_case_(_UpperCamelCase	)				->      list[Letter]:



  """simple docstring"""






  _snake_case		=				{}
  with open(__lowercase	) as f:
    while True:
      _snake_case		=				f.read(1	)
      if not c:
        break
      _snake_case		=				chars[c] + 1 if c in chars else 1
  return sorted((Letter(__lowercase					,			__lowercase	) for c, f in chars.items())					,			key=lambda _UpperCamelCase	: x.freq	)



def  snake_case_(_UpperCamelCase	)				->      Letter | TreeNode:



  """simple docstring"""






  _snake_case		=				letters  # type: ignore
  while len(__lowercase	) > 1:
    _snake_case		=				response.pop(0	)
    _snake_case		=				response.pop(0	)
    _snake_case		=				left.freq + right.freq
    _snake_case		=				TreeNode(__lowercase					,			__lowercase					,			__lowercase	)
    response.append(__lowercase	)
    response.sort(key=lambda _UpperCamelCase	: x.freq	)
  return response[0]



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      list[Letter]:



  """simple docstring"""






  if isinstance(__lowercase					,			__lowercase	):
    _snake_case		=				bitstring
    return [root]
  _snake_case		=				root  # type: ignore
  _snake_case		=				[]
  letters += traverse_tree(treenode.left					,			bitstring + '''0'''	)
  letters += traverse_tree(treenode.right					,			bitstring + '''1'''	)
  return letters



def  snake_case_(_UpperCamelCase	)				->      None:



  """simple docstring"""






  _snake_case		=				parse_file(__lowercase	)
  _snake_case		=				build_tree(__lowercase	)
  _snake_case		=				{
      k: v for letter in traverse_tree(__lowercase					,			''''''	) for k, v in letter.bitstring.items()
  }
  print(F"""Huffman Coding  of {file_path}: """	)
  with open(__lowercase	) as f:
    while True:
      _snake_case		=				f.read(1	)
      if not c:
        break
      print(letters[c]					,			end=''' '''	)
  print()


if __name__ == "__main__":
 # pass the file path to the huffman function
 huffman(sys.argv[1])






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)