
lowercase						:      Optional[Any]									= {
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
lowercase						:      int									= {
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    float		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    str)      ->					float:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      from_type.lower().strip("s")
							__UpperCamelCase			:     List[str]         =      to_type.lower().strip("s")

							__UpperCamelCase			:     Any         =      UNIT_SYMBOL.get(_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase			:     int         =      UNIT_SYMBOL.get(_lowerCamelCase		,						_lowerCamelCase)

							if from_sanitized not in METRIC_CONVERSION:
														__UpperCamelCase			:     Tuple         =      (
														    F'Invalid \'from_type\' value: {from_type!r}.\n'
														    F'Conversion abbreviations are: {", ".join(_lowerCamelCase)}'
														)
														raise ValueError(_lowerCamelCase)
							if to_sanitized not in METRIC_CONVERSION:
														__UpperCamelCase			:     List[Any]         =      (
														    F'Invalid \'to_type\' value: {to_type!r}.\n'
														    F'Conversion abbreviations are: {", ".join(_lowerCamelCase)}'
														)
														raise ValueError(_lowerCamelCase)
							__UpperCamelCase			:     Tuple         =      METRIC_CONVERSION[from_sanitized]
							__UpperCamelCase			:     List[Any]         =      METRIC_CONVERSION[to_sanitized]
							__UpperCamelCase			:     int         =      1

							if from_exponent > to_exponent:
														__UpperCamelCase			:     Tuple         =      from_exponent - to_exponent
							else:
														__UpperCamelCase			:     str         =      -(to_exponent - from_exponent)

							return value * pow(10		,						_lowerCamelCase)


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

		testmod()

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

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


if __name__ == "__main__":
		lowercase						:      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(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT 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.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)