



'''simple docstring'''





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



				"""simple docstring"""





				if openai_config_file == "":
								lowerCAmelCase__			:  str											= OpenAIGPTConfig()
				else:
								lowerCAmelCase__			:  Union[str, Any]											= OpenAIGPTConfig.from_json_file(UpperCamelCase      )
				lowerCAmelCase__			:  Dict											= OpenAIGPTModel(UpperCamelCase      )

				# Load weights from numpy
				load_tf_weights_in_openai_gpt(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

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


if __name__ == "__main__":
	_lowerCAmelCase										=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--openai_checkpoint_folder_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(
	    '''--openai_config_file''',
	    default='''''',
	    type=str,
	    help=(
	        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
	        '''This specifies the model architecture.'''
	    ),
	)
	_lowerCAmelCase										=     parser.parse_args()
	convert_openai_checkpoint_to_pytorch(
	    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
	)







'''simple docstring'''





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase										=     10
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    for i in range(UpperCamelCase							,   UpperCamelCase      ):
        if array[i] == target:
            return i
    return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= 0
    lowerCAmelCase__			:  Optional[int]											= len(UpperCamelCase      )
    while left <= right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

        lowerCAmelCase__			:  Tuple											= (left + right) // 3 + 1
        lowerCAmelCase__			:  List[str]											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            lowerCAmelCase__			:  int											= one_third - 1
        elif array[two_third] < target:
            lowerCAmelCase__			:  Union[str, Any]											= two_third + 1

        else:
            lowerCAmelCase__			:  List[Any]											= one_third + 1
            lowerCAmelCase__			:  List[str]											= two_third - 1
    else:
        return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    if left < right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Dict											= (left + right) // 3 + 1
        lowerCAmelCase__			:  Tuple											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            return rec_ternary_search(UpperCamelCase							,   one_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
        elif array[two_third] < target:
            return rec_ternary_search(two_third + 1							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        else:
            return rec_ternary_search(one_third + 1							,   two_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
    else:
        return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _lowerCAmelCase										=     input('''Enter numbers separated by comma:\n''').strip()
 _lowerCAmelCase										=     [int(item.strip()) for item in user_input.split(''',''')]
 assert collection == sorted(collection), F"List must be ordered.\n{collection}."
 _lowerCAmelCase										=     int(input('''Enter the number to be found in the list:\n''').strip())
 _lowerCAmelCase										=     ite_ternary_search(collection, target)
 _lowerCAmelCase										=     rec_ternary_search(0, len(collection) - 1, collection, target)
 if resulta != -1:
  print(F"""Iterative search: {target} found at positions: {resulta}""")
  print(F"""Recursive search: {target} found at positions: {resulta}""")
 else:
  print('''Not found''')


