


"""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.
SCREAMING_SNAKE_CASE :    Union[str, Any]									=				10






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      int     ,   _snake_case						:      list[int]     ,   _snake_case						:      int     )     ->int:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      list[int]     ,   _snake_case						:      int     )     ->int:
	"""simple docstring"""





	__snake_case	:						str						=				0
	__snake_case	:						str						=				len(_snake_case     )
	while left <= right:
		if right - left < precision:
			return lin_search(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )

		__snake_case	:						Optional[Any]						=				(left + right) // 3 + 1
		__snake_case	:						int						=				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]:
			__snake_case	:						List[str]						=				one_third - 1
		elif array[two_third] < target:
			__snake_case	:						Any						=				two_third + 1

		else:
			__snake_case	:						Optional[Any]						=				one_third + 1
			__snake_case	:						Any						=				two_third - 1
	else:
		return -1






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      int     ,   _snake_case						:      list[int]     ,   _snake_case						:      int     )     ->int:
	"""simple docstring"""





	if left < right:
		if right - left < precision:
			return lin_search(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )
		__snake_case	:						Optional[int]						=				(left + right) // 3 + 1
		__snake_case	:						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]:
			return rec_ternary_search(_snake_case     ,   one_third - 1     ,   _snake_case     ,   _snake_case     )
		elif array[two_third] < target:
			return rec_ternary_search(two_third + 1     ,   _snake_case     ,   _snake_case     ,   _snake_case     )
		else:
			return rec_ternary_search(one_third + 1     ,   two_third - 1     ,   _snake_case     ,   _snake_case     )
	else:
		return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			SCREAMING_SNAKE_CASE :    Optional[Any]									=				input("""Enter numbers separated by comma:\n""").strip()
			SCREAMING_SNAKE_CASE :    List[Any]									=				[int(item.strip()) for item in user_input.split(""",""")]
			assert collection == sorted(collection), F"List must be ordered.\n{collection}."
			SCREAMING_SNAKE_CASE :    List[str]									=				int(input("""Enter the number to be found in the list:\n""").strip())
			SCREAMING_SNAKE_CASE :    Dict									=				ite_ternary_search(collection, target)
			SCREAMING_SNAKE_CASE :    Any									=				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""")









"""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 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

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

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				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."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





