





"""simple docstring"""






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			Optional[int]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	Tuple		 =			TaConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	int		 =			TaForConditionalGeneration(lowerCAmelCase__			)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"			)
			model.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""






from __future__ import annotations






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			tuple[int, int]:



			'''simple docstring'''
			if b == 0:
						return (1, 0)
			((a__) ,	(a__))  :	List[Any]		 =			extended_euclid(lowerCAmelCase__							,			a % b			)
			a__  :	str		 =			a // b
			return (y, x - k * y)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			((a__) ,	(a__))  :	Tuple		 =			extended_euclid(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	List[str]		 =			na * na
			a__  :	Union[str, Any]		 =			ra * x * na + ra * y * na
			return (n % m + m) % m






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			((a__) ,	(a__))  :	Optional[Any]		 =			extended_euclid(lowerCAmelCase__							,			lowerCAmelCase__			)
			if b < 0:
						a__  :	Optional[int]		 =			(b % n + n) % n
			return b






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__ ,	a__  :	List[Any]		 =			invert_modulo(lowerCAmelCase__							,			lowerCAmelCase__			), invert_modulo(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Dict		 =			na * na
			a__  :	Any		 =			ra * x * na + ra * y * na
			return (n % m + m) % m


if __name__ == "__main__":
		from doctest import testmod

		testmod(name='''chinese_remainder_theorem''', verbose=True)
		testmod(name='''chinese_remainder_theorem2''', verbose=True)
		testmod(name='''invert_modulo''', verbose=True)
		testmod(name='''extended_euclid''', verbose=True)