

def 							__lowerCAmelCase     (							_UpperCamelCase		:	int = 10		,				_UpperCamelCase		:	int = 10_00		,				_UpperCamelCase		:	bool = True						)		->				int:
 '''simple docstring'''


 assert (
     isinstance(_UpperCamelCase		,				_UpperCamelCase						)
     and isinstance(_UpperCamelCase		,				_UpperCamelCase						)
     and isinstance(_UpperCamelCase		,				_UpperCamelCase						)
 ), "Invalid type of value(s) specified to function!"

 if min_val > max_val:
  raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'						)
 return min_val if option else max_val


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						)		->				int:
 '''simple docstring'''


 return int((number_a + number_a) / 2						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						)		->				None:
 '''simple docstring'''


 assert (
     isinstance(_UpperCamelCase		,				_UpperCamelCase						) and isinstance(_UpperCamelCase		,				_UpperCamelCase						) and isinstance(_UpperCamelCase		,				_UpperCamelCase						)
 ), 'argument values must be type of "int"'

 if lower > higher:
  raise ValueError('argument value for lower and higher must be(lower > higher)'						)

 if not lower < to_guess < higher:
  raise ValueError(
      'guess value must be within the range of lower and higher value'						)

 def answer(_UpperCamelCase		:	int						) -> str:
  if number > to_guess:
   return "high"
  elif number < to_guess:
   return "low"
  else:
   return "same"

 print('started...'						)

 SCREAMING_SNAKE_CASE		    =      lower
 SCREAMING_SNAKE_CASE		    =      higher

 SCREAMING_SNAKE_CASE		    =      []

 while True:
  SCREAMING_SNAKE_CASE		    =      get_avg(_UpperCamelCase		,				_UpperCamelCase						)
  last_numbers.append(_UpperCamelCase						)

  if answer(_UpperCamelCase						) == "low":
   SCREAMING_SNAKE_CASE		    =      number
  elif answer(_UpperCamelCase						) == "high":
   SCREAMING_SNAKE_CASE		    =      number
  else:
   break

 print(f"""guess the number : {last_numbers[-1]}"""						)
 print(f"""details : {last_numbers!s}"""						)


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(input('Enter lower value : '						).strip()						)
 SCREAMING_SNAKE_CASE		    =      int(input('Enter high value : '						).strip()						)
 SCREAMING_SNAKE_CASE		    =      int(input('Enter value to guess : '						).strip()						)
 guess_the_number(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)


if __name__ == "__main__":
      main()




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_UpperCamelCase		,				_UpperCamelCase		,				is_trivia_qa=_UpperCamelCase						)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
 model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    int     		=   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(
          "--big_bird_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."
      )
      parser.add_argument(
          "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
      )
      a_     :    Any     		=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

