

"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def 						A							(  snake_case__     ):




			'''simple docstring'''


			if "model" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""model."""		,					""""""     )
			if "norm1" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""norm1"""		,					"""attention.output.LayerNorm"""     )
			if "norm2" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""norm2"""		,					"""output.LayerNorm"""     )
			if "norm" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""norm"""		,					"""LayerNorm"""     )
			if "transformer" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.split("""."""     )[0].split("""_"""     )[-1]
						SCREAMING_SNAKE_CASE__										=   orig_key.replace(f"""transformer_{layer_num}"""		,					f"""encoder.layer.{layer_num}"""     )
			if "mha.attn" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""mha.attn"""		,					"""attention.self"""     )
			if "mha" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""mha"""		,					"""attention"""     )
			if "W_q" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""W_q"""		,					"""self.query"""     )
			if "W_k" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""W_k"""		,					"""self.key"""     )
			if "W_v" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""W_v"""		,					"""self.value"""     )
			if "ff1" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""ff1"""		,					"""intermediate.dense"""     )
			if "ff2" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""ff2"""		,					"""output.dense"""     )
			if "ff" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""ff"""		,					"""output.dense"""     )
			if "mlm_class" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""mlm.mlm_class"""		,					"""cls.predictions.decoder"""     )
			if "mlm" in orig_key:
						SCREAMING_SNAKE_CASE__										=   orig_key.replace("""mlm"""		,					"""cls.predictions.transform"""     )
			if "cls" not in orig_key:
						SCREAMING_SNAKE_CASE__										=   '''yoso.''' + orig_key

			return orig_key

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE__										=   orig_state_dict.pop(_UpperCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									SCREAMING_SNAKE_CASE__										=   val

			SCREAMING_SNAKE_CASE__										=   orig_state_dict['''cls.predictions.decoder.bias''']
			SCREAMING_SNAKE_CASE__										=   torch.arange(_UpperCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict

def 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   torch.load(_UpperCamelCase		,					map_location="""cpu"""     )['''model_state_dict''']
			SCREAMING_SNAKE_CASE__										=   YosoConfig.from_json_file(_UpperCamelCase     )
			SCREAMING_SNAKE_CASE__										=   YosoForMaskedLM(_UpperCamelCase     )

			SCREAMING_SNAKE_CASE__										=   convert_checkpoint_helper(config.max_position_embeddings		,					_UpperCamelCase     )

			print(model.load_state_dict(_UpperCamelCase     )     )
			model.eval()
			model.save_pretrained(_UpperCamelCase     )

			print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )


if __name__ == "__main__":
						A_				:      int           =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The json file for YOSO model config.",
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						A_				:      Any           =			parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""




import math
import sys




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  ''''''
  try:
    with open(_UpperCamelCase			,					'''rb'''    ) as binary_file:
      _lowercase:      Dict   			=  binary_file.read()
    for dat in data:
      _lowercase:      List[str]   			=  f'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  {'''0''': '''0''', '''1''': '''1'''}
  _lowercase			,					_lowercase:      Optional[int]   			=  '''''', ''''''
  _lowercase:      Dict   			=  len(_UpperCamelCase    )

  for i in range(len(_UpperCamelCase    )    ):
    curr_string += data_bits[i]
    if curr_string not in lexicon:
      continue

    _lowercase:      Any   			=  lexicon[curr_string]
    result += last_match_id
    _lowercase:      Optional[int]   			=  last_match_id + '''0'''

    if math.loga(_UpperCamelCase    ).is_integer():
      _lowercase:      str   			=  {}
      for curr_key in list(_UpperCamelCase    ):
        _lowercase:      List[Any]   			=  lexicon.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  new_lex

    _lowercase:      List[Any]   			=  last_match_id + '''1'''
    index += 1
    _lowercase:      List[Any]   			=  ''''''
  return result




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  8
  try:
    with open(_UpperCamelCase			,					'''wb'''    ) as opened_file:
      _lowercase:      List[Any]   			=  [
          to_write[i : i + byte_length]
          for i in range(0			,					len(_UpperCamelCase    )			,					_UpperCamelCase    )
      ]

      if len(result_byte_array[-1]    ) % byte_length == 0:
        result_byte_array.append('''10000000'''    )
      else:
        result_byte_array[-1] += "1" + "0" * (
            byte_length - len(result_byte_array[-1]    ) - 1
        )

      for elem in result_byte_array[:-1]:
        opened_file.write(int(_UpperCamelCase			,					2    ).to_bytes(1			,					byteorder='''big'''    )    )
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  _lowercase:      List[str]   			=  data_bits[counter:]
  _lowercase:      Union[str, Any]   			=  data_bits[counter + 1 :]
  return data_bits




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  read_file_binary(_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  remove_prefix(_UpperCamelCase    )
  _lowercase:      Dict   			=  decompress_data(_UpperCamelCase    )
  write_file_binary(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])
