
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     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Union[str, Any]					):
       # Construct configuration depending on size
       snake_case :     Any           =   {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
       snake_case       ,      snake_case       ,      snake_case :     int           =   MODELS[model_size]  # set model hyperparameters
       snake_case :     Tuple           =   ImageGPTConfig(n_embd=__lowerCamelCase       ,      n_layer=__lowerCamelCase       ,      n_head=__lowerCamelCase					)
       snake_case :     List[str]           =   ImageGPTForCausalLM(__lowerCamelCase					)

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

       # Save pytorch-model
       snake_case :     Dict           =   pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       snake_case :     str           =   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							= 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."""
   )
   __lowerCamelCase							= parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       if target == 0:
              answer.append(path.copy()					)
       else:
              for index in range(__lowerCamelCase       ,      len(__lowerCamelCase					)					):
                     if target >= candidates[index]:
                            path.append(candidates[index]					)
                            backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      target - candidates[index]       ,      __lowerCamelCase					)
                            path.pop(len(__lowerCamelCase					) - 1					)


def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


def     UpperCamelCase      (					):
       print(combination_sum([-8, 2.3, 0]       ,      1					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





