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 SCREAMING_SNAKE_CASE					(  lowercase_:						Tuple		,    lowercase_:						Optional[Any]		,    lowercase_:						int					):
       # Construct configuration depending on size
       _UpperCamelCase  :							Tuple	    =	{'small': (512, 8, 24), 'medium': (1024, 8, 36), 'large': (1536, 16, 48)}
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	MODELS[model_size]  # set model hyperparameters
       _UpperCamelCase  :							str	    =	ImageGPTConfig(n_embd=lowercase_		,    n_layer=lowercase_		,    n_head=lowercase_					)
       _UpperCamelCase  :							Dict	    =	ImageGPTForCausalLM(lowercase_					)

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

       # Save pytorch-model
       _UpperCamelCase  :							Optional[int]	    =	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       _UpperCamelCase  :							Optional[Any]	    =	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__":
 UpperCAmelCase      		=					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.'''
 )
 UpperCAmelCase      		=					parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )











class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   int      ):
		if (difference := index - (len(self.sequence      ) - 2)) >= 1:
			for _ in range(__lowerCamelCase      ):
				self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
		return self.sequence[:index]

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""simple docstring"""



	print(
	    """Fibonacci Series Using Dynamic Programming\n"""      ,     """Enter the index of the Fibonacci number you want to calculate """      ,     """in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,     sep=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

	while True:
		UpperCamelCase	:str		=      input(""">> """   )
		if prompt in {"exit", "quit"}:
			break

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
