"""simple docstring"""
import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  # Construct model
  if gpta_config_file == "":
    a__:					Union[str, Any]      							=     GPTaConfig()
  else:
    a__:					Tuple      							=     GPTaConfig.from_json_file(_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     GPTaModel(_SCREAMING_SNAKE_CASE       )

  # Load weights from numpy
  load_tf_weights_in_gpta(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--gpt2_checkpoint_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(
     '--gpt2_config_file',
     default='',
     type=str,
     help=(
         'An optional config json file corresponding to the pre-trained OpenAI model. \n'
         'This specifies the model architecture.'
     ),
 )
 lowercase__ 	=     parser.parse_args()
 convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  if n == 0:
    return 0
  a__:					List[Any]      							=     float('-inf'       )
  for i in range(1		,					n + 1       ):
    a__:					Optional[Any]      							=     max(
        _SCREAMING_SNAKE_CASE		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE       )       )

  return max_revue







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a__:					Dict      							=     float('-inf'       )
    for i in range(1		,					n + 1       ):
      a__:					Optional[Any]      							=     max(
          _SCREAMING_SNAKE_CASE		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					)

    a__:					Optional[int]      							=     max_revenue

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
  # length 0.
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  a__:					Tuple      							=     0

  for i in range(1		,					n + 1       ):
    a__:					List[str]      							=     max_rev[i]
    for j in range(1		,					i + 1       ):
      a__:					Tuple      							=     max(_SCREAMING_SNAKE_CASE		,					prices[j - 1] + max_rev[i - j]       )

    a__:					Union[str, Any]      							=     max_revenue_i

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  if n < 0:
    a__:					Optional[int]      							=     F'n must be greater than or equal to 0. Got n = {n}'
    raise ValueError(_SCREAMING_SNAKE_CASE       )

  if n > len(_SCREAMING_SNAKE_CASE       ):
    a__:					List[str]      							=     (
        'Each integral piece of rod must have a corresponding price. '
        F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE       )}'
    )
    raise ValueError(_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					int      							=     [6, 10, 12, 15, 20, 23]
  a__:					Union[str, Any]      							=     len(_SCREAMING_SNAKE_CASE       )

  # the best revenue comes from cutting the rod into 6 pieces, each
  # of length 1 resulting in a revenue of 6 * 6 = 36.
  a__:					Any      							=     36

  a__:					Optional[int]      							=     top_down_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     bottom_up_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  assert expected_max_revenue == max_rev_top_down
  assert max_rev_top_down == max_rev_bottom_up
  assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
 main()


