



import argparse

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


logging.set_verbosity_info()



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					# Initialise PyTorch model
					UpperCamelCase_			= TaConfig.from_json_file(_lowerCAmelCase)
					print(f"""Building PyTorch model from configuration: {config}""")
					UpperCamelCase_			= 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 :				Any							=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 :				Dict							=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""
import math







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[int]:
							if not isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE					)
							if number < 1:
														snake_case_     							=		f"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		[number]
							while number != 1:
														if number % 2 == 0:
																					snake_case_     							=		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														else:
																					snake_case_     							=		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														sequence.append(_SCREAMING_SNAKE_CASE					)
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



