


A       :		Any    =   'Tobias Carryer'

from time import time



class 							A    :
					'''simple docstring'''






					def __init__(self    :						str      ,					_UpperCAmelCase    :						List[str]      ,					_UpperCAmelCase    :						List[str]      ,					_UpperCAmelCase    :						Dict      ,					_UpperCAmelCase    :						Optional[int]=int(time()		)		)						->   Dict:  # noqa: B008




							"""simple docstring"""


							lowercase__     			=		multiplier
							lowercase__     			=		increment
							lowercase__     			=		modulo
							lowercase__     			=		seed


					def   lowerCamelCase__     (self    :						List[Any]		)						->   Dict:




							"""simple docstring"""


							lowercase__     			=		(self.multiplier * self.seed + self.increment) % self.modulo
							return self.seed


if __name__ == "__main__":
	# Show the LCG in action.
	A       :		List[Any]    =   LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
	while True:
		print(lcg.next_number())


import argparse

import torch

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


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   Optional[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__":
      UpperCamelCase  							=						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.'''
      )
      UpperCamelCase  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



