




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							int:
    return round((float(lowercase_      ) * 9 / 5) + 32       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Any:
    return round(float(lowercase_      ) + 273.15       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Optional[Any]:
    return round((float(lowercase_      ) * 9 / 5) + 491.67       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							List[str]:
    return round((float(lowercase_      ) - 32) * 5 / 9       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							List[str]:
    return round(((float(lowercase_      ) - 32) * 5 / 9) + 273.15       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Optional[Any]:
    return round(float(lowercase_      ) + 459.67       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							str:
    return round(float(lowercase_      ) - 273.15       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Optional[int]:
    return round(((float(lowercase_      ) - 273.15) * 9 / 5) + 32       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							str:
    return round((float(lowercase_      ) * 9 / 5)       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Any:
    return round((float(lowercase_      ) - 491.67) * 5 / 9       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Any:
    return round(float(lowercase_      ) - 459.67       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							str:
    return round((float(lowercase_      ) * 5 / 9)       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							List[Any]:
    return round((float(lowercase_      ) * 1.25 + 273.15)       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Optional[Any]:
    return round((float(lowercase_      ) * 2.25 + 32)       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							Union[str, Any]:
    return round((float(lowercase_      ) * 1.25)       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						int = 2      )  ->							List[str]:
    return round((float(lowercase_      ) * 2.25 + 32 + 459.67)       ,     lowercase_      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()












"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


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(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 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.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

