






"""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 _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any]   ):

						'''simple docstring'''






						if gpta_config_file == "":
												__magic_name__            =     GPTaConfig()
						else:
												__magic_name__            =     GPTaConfig.from_json_file(__lowerCamelCase   )
						__magic_name__            =     GPTaModel(__lowerCamelCase   )

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

						# Save pytorch-model
						__magic_name__            =     pytorch_dump_folder_path + "/" + WEIGHTS_NAME
						__magic_name__            =     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__":
			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"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



