'''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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     # Construct configuration depending on size
     lowercase_       :							int  					=       {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
     lowercase_					,lowercase_					,lowercase_       :							Optional[Any]  					=       MODELS[model_size]  # set model hyperparameters
     lowercase_       :							Any  					=       ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_	,							n_layer=SCREAMING_SNAKE_CASE_	,							n_head=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Tuple  					=       ImageGPTForCausalLM(SCREAMING_SNAKE_CASE_				)

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

     # Save pytorch-model
     lowercase_       :							str  					=       pytorch_dump_folder_path + '/' + WEIGHTS_NAME
     lowercase_       :							Tuple  					=       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__":
 _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
 )





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




