


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [0 for i in range(len(_snake_case  )  )]

			# initialize interval's left pointer and right pointer
			__lowercase					, __lowercase 					=   0, 0

			for i in range(1       ,			len(_snake_case  )  ):
						# case when current index is inside the interval
						if i <= right_pointer:
									__lowercase 					=   min(right_pointer - i + 1       ,			z_result[i - left_pointer]  )
									__lowercase 					=   min_edge

						while go_next(_snake_case       ,			_snake_case       ,			_snake_case  ):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									__lowercase					, __lowercase 					=   i, i + z_result[i] - 1

			return z_result


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return i + z_result[i] < len(_snake_case  ) and s[z_result[i]] == s[i + z_result[i]]


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			__lowercase 					=   z_function(pattern + input_str  )

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(_snake_case  ):
									answer += 1

			return answer


if __name__ == "__main__":
	import doctest

	doctest.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)


