


import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= AlbertConfig.from_json_file(_a	)
		print(f'Building PyTorch model from configuration: {config}'	)
		lowerCAmelCase__       :      List[str]   					= AlbertForPreTraining(_a	)

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

		# Save pytorch-model
		print(f'Save PyTorch model to {pytorch_dump_path}'	)
		torch.save(model.state_dict()     ,       _a	)


if __name__ == "__main__":
		lowerCamelCase						=		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.'''
		)
		lowerCamelCase						=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



from __future__ import annotations






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a	):  # noqa: E741




		"""simple docstring"""






		while r - l > 1:
				lowerCAmelCase__       :      Any   					= (l + r) // 2
				if v[m] >= key:
						lowerCAmelCase__       :      int   					= m
				else:
						lowerCAmelCase__       :      Tuple   					= m  # noqa: E741
		return r






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		if len(_a	) == 0:
				return 0

		lowerCAmelCase__       :      Optional[int]   					= [0] * len(_a	)
		lowerCAmelCase__       :      List[Any]   					= 1

		lowerCAmelCase__       :      int   					= v[0]

		for i in range(1     ,       len(_a	)	):
				if v[i] < tail[0]:
						lowerCAmelCase__       :      str   					= v[i]
				elif v[i] > tail[length - 1]:
						lowerCAmelCase__       :      Any   					= v[i]
						length += 1
				else:
						lowerCAmelCase__       :      int   					= v[i]

		return length


if __name__ == "__main__":
		import doctest

		doctest.testmod()




