


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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Union[str, Any]:
  # Construct model
  if openai_config_file == "":
    snake_case__						= OpenAIGPTConfig()
  else:
    snake_case__						= OpenAIGPTConfig.from_json_file(__lowerCAmelCase				)
  snake_case__						= OpenAIGPTModel(__lowerCAmelCase				)

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

  # Save pytorch-model
  snake_case__						= pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
  snake_case__						= pytorch_dump_folder_path + '''/''' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""				)
  torch.save(model.state_dict()							,   __lowerCAmelCase				)
  print(F"""Save configuration file to {pytorch_config_dump_path}"""				)
  with open(__lowerCAmelCase							,   '''w'''							,   encoding='''utf-8'''				) as f:
    f.write(config.to_json_string()				)


if __name__ == "__main__":
   lowerCamelCase__							:						Tuple							     =   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."""
       ),
   )
   lowerCamelCase__							:						Any							     =   parser.parse_args()
   convert_openai_checkpoint_to_pytorch(
       args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
   )



from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




