



"""simple docstring"""
import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def   A_    (     _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:int		=     {"""small""": (512, 8, 24), """medium""": (1024, 8, 36), """large""": (1536, 16, 48)}
						snake_case_,			snake_case_,			snake_case_					:Dict		=     MODELS[model_size]  # set model hyperparameters
						snake_case_					:List[str]		=     ImageGPTConfig(n_embd=_lowercase,   n_layer=_lowercase,   n_head=_lowercase      )
						snake_case_					:str		=     ImageGPTForCausalLM(_lowercase      )

						# Load weights from numpy
						load_tf_weights_in_imagegpt(_lowercase,   _lowercase,   _lowercase      )

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


if __name__ == "__main__":
						__a           =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--imagegpt_checkpoint_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the TensorFlow checkpoint path.",
						)
						parser.add_argument(
						    "--model_size",
						    default=None,
						    type=str,
						    required=True,
						    help="Size of the model (can be either 'small', 'medium' or 'large').",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						__a           =							parser.parse_args()
						convert_imagegpt_checkpoint_to_pytorch(
						    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
						)







import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


if __name__ == "__main__":
       print(solution())





