


import math
import sys




def     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							Any:
     '''simple docstring'''





     lowercase_       :Any     =       {'''0''': '''0''', '''1''': '''1'''}
     lowercase_    ,      lowercase_       :Optional[Any]     =       '''''', ''''''
     lowercase_       :Any     =       len(_UpperCAmelCase		)

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

          lowercase_       :Tuple     =       lexicon[curr_string]
          result += last_match_id
          lowercase_       :Any     =       last_match_id + '''0'''

          if math.loga(_UpperCAmelCase		).is_integer():
               lowercase_       :Any     =       {}
               for curr_key in list(_UpperCAmelCase		):
                    lowercase_       :Dict     =       lexicon.pop(_UpperCAmelCase		)
               lowercase_       :List[str]     =       new_lex

          lowercase_       :List[str]     =       last_match_id + '''1'''
          index += 1
          lowercase_       :Union[str, Any]     =       ''''''
     return result




def     UpperCamelCase    (      _a       ,		_a		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :int     =       8
     try:
          with open(_UpperCAmelCase       ,		'''wb'''		) as opened_file:
               lowercase_       :Union[str, 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     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





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

     lowercase_       :Optional[Any]     =       data_bits[counter:]
     lowercase_       :str     =       data_bits[counter + 1 :]
     return data_bits




def     UpperCamelCase    (      _a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Dict     =       read_file_binary(_UpperCAmelCase		)
     lowercase_       :List[Any]     =       remove_prefix(_UpperCAmelCase		)
     lowercase_       :List[str]     =       decompress_data(_UpperCAmelCase		)
     write_file_binary(_UpperCAmelCase       ,		_UpperCAmelCase		)


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





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     if openai_config_file == "":
          lowercase_       :str     =       OpenAIGPTConfig()
     else:
          lowercase_       :int     =       OpenAIGPTConfig.from_json_file(_a		)
     lowercase_       :int     =       OpenAIGPTModel(_a		)

     # Load weights from numpy
     load_tf_weights_in_openai_gpt(_a       ,		_a       ,		_a		)

     # Save pytorch-model
     lowercase_       :Optional[int]     =       pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     lowercase_       :List[str]     =       pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     print(f"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)
     print(f"Save configuration file to {pytorch_config_dump_path}"		)
     with open(_a       ,		'''w'''       ,		encoding='''utf-8'''		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		List[str]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--openai_checkpoint_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the TensorFlow checkpoint path.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--openai_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained OpenAI model. \n"
           "This specifies the model architecture."
       ),
   )
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				parser.parse_args()
   convert_openai_checkpoint_to_pytorch(
       args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
   )

