

'''simple docstring'''


from __future__ import annotations

from collections.abc import Generator







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Generator[int, None, None]:







	if not isinstance(UpperCamelCase ,UpperCamelCase	) or n < 1:
		raise Exception('Sequence only defined for positive integers'	)

	yield n
	while n != 1:
		if n % 2 == 0:
			n //= 2
		else:
			UpperCAmelCase_		:   int													=       3 * n + 1
		yield n







def     SCREAMING_SNAKE_CASE(	)     ->     List[str]:
	UpperCAmelCase_		:   Tuple													=       4_3
	UpperCAmelCase_		:   Optional[int]													=       tuple(collatz_sequence(UpperCamelCase	)	)
	print(UpperCamelCase	)
	print(f"""Collatz sequence from {n} took {len(UpperCamelCase	)} steps."""	)


if __name__ == "__main__":
						main()









"""simple docstring"""
import argparse

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


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     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__":
 A									=						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."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
