

"""simple docstring"""

import argparse

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


logging.set_verbosity_info()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						str:
 # Initialise PyTorch model
 lowerCAmelCase__   :				Optional[Any] 			=				TaConfig.from_json_file(lowercase__   )
 print(F"""Building PyTorch model from configuration: {config}"""   )
 lowerCAmelCase__   :				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__":
    _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(
        """--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."""
    )
    _UpperCamelCase		     =    parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






