



"""simple docstring"""






def     _lowercase       (						__snake_case			)		->     list[int]:
			__lowerCAmelCase   :						Optional[Any]				= [0 for i in range(len(__snake_case			)			)]

			# initialize interval's left pointer and right pointer
			__lowerCAmelCase		,			__lowerCAmelCase   :						str				= 0, 0

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

			return z_result


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     bool:
			return i + z_result[i] < len(__snake_case			) and s[z_result[i]] == s[i + z_result[i]]


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						Optional[int]				= 0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			__lowerCAmelCase   :						Optional[Any]				= 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 ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

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

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


if __name__ == "__main__":
	__snake_case							:     Union[str, 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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)