






def    a_       (					_A     )	->	list[int]:







				"""simple docstring"""
				snake_case__													=   [0 for i in range(len(__snake_case     )     )]

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

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

				return z_result
def    a_       (					_A					,      _A					,      _A     )	->	bool:







				"""simple docstring"""
				return i + z_result[i] < len(__snake_case     ) and s[z_result[i]] == s[i + z_result[i]]
def    a_       (					_A					,      _A     )	->	int:







				"""simple docstring"""
				snake_case__													=   0
				# concatenate 'pattern' and 'input_str' and call z_function
				# with concatenated string
				snake_case__													=   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()






"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


