


'''simple docstring'''

import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						List[str]		):
 # Initialise PyTorch model
 lowerCamelCase_      =  LxmertConfig.from_json_file(UpperCAmelCase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 lowerCamelCase_      =  LxmertForPreTraining(UpperCAmelCase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_lxmert(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

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


if __name__ == "__main__":
     a_  :				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(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     a_  :				Dict						= parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						float		):
 return math.pow(UpperCAmelCase_					,				2		) - a





def __snake_case					(      UpperCAmelCase_						:						float		):
 return 2 * x





def __snake_case					(      UpperCAmelCase_						:						float		):
 lowerCamelCase_      =  2.0

 while start <= a:
  lowerCamelCase_      =  math.pow(UpperCAmelCase_					,				2		)

 return start





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						int = 9999					,				UpperCAmelCase_						:						float = 0.00_0000_0000_0001		):

 if a < 0:
  raise ValueError("math domain error"		)

 lowerCamelCase_      =  get_initial_point(UpperCAmelCase_		)

 for _ in range(UpperCAmelCase_		):
  lowerCamelCase_      =  value
  lowerCamelCase_      =  value - fx(UpperCAmelCase_					,				UpperCAmelCase_		) / fx_derivative(UpperCAmelCase_		)
  if abs(prev_value - value		) < tolerance:
   return value

 return value


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

     testmod()



