






import argparse

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


logging.set_verbosity_info()





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			BertConfig.from_pretrained(
	    '''bert-large-cased''',  vocab_size=snake_case_,  max_position_embeddings=5_1_2,  is_decoder=snake_case_,  add_cross_attention=snake_case_,  )
	a		=			bert_config.to_dict()
	del bert_config_dict["type_vocab_size"]
	a		=			BertGenerationConfig(**snake_case_  )
	if is_encoder:
		a		=			BertGenerationEncoder(snake_case_  )
	else:
		a		=			BertGenerationDecoder(snake_case_  )
	print(f"""Building PyTorch model from configuration: {config}"""  )

	# Load weights from tf checkpoint
	load_tf_weights_in_bert_generation(
	    snake_case_,  snake_case_,  model_class='''bert''',  is_encoder_named_decoder=snake_case_,  is_encoder=snake_case_,  )

	# Save pytorch-model
	print(f"""Save PyTorch model and config to {pytorch_dump_path}"""  )
	model.save_pretrained(snake_case_  )


if __name__ == "__main__":
				UpperCamelCase__						:      str								=					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=50_358, type=int, help="""Vocab size of model""")
				UpperCamelCase__						:      Optional[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,
				)













from __future__ import annotations

import sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



								return F"""{self.letter}:{self.freq}"""







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





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


	"""simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



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





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


	"""simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			parse_file(snake_case_  )
	a		=			build_tree(snake_case_  )
	a		=			{
	    k: v for letter in traverse_tree(snake_case_,  ''''''  ) for k, v in letter.bitstring.items()
	}
	print(f"""Huffman Coding  of {file_path}: """  )
	with open(snake_case_  ) as f:
		while True:
			a		=			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])





