


from __future__ import annotations


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = None       ,			_UpperCamelCase = None  ):


			'''simple docstring'''







			if start is None:
						__lowercase 					=   0

			if end is None:
						__lowercase 					=   len(UpperCAmelCase__  ) - 1

			if start >= end:
						return

			__lowercase 					=   (start + end) // 2

			slowsort(UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__  )
			slowsort(UpperCAmelCase__       ,			mid + 1       ,			UpperCAmelCase__  )

			if sequence[end] < sequence[mid]:
						__lowercase 					=   sequence[mid], sequence[end]

			slowsort(UpperCAmelCase__       ,			UpperCAmelCase__       ,			end - 1  )


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

	testmod()









import argparse

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


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

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

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


if __name__ == "__main__":
	a : Optional[int]		=				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 : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


