
import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# Initialize PyTorch model
				A_       	=  CanineConfig()
				A_       	=  CanineModel(lowercase_     )
				model.eval()

				print(F"Building PyTorch model from configuration: {config}"     )

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

				# Save pytorch-model (weights and configuration)
				print(F"Save PyTorch model to {pytorch_dump_path}"     )
				model.save_pretrained(lowercase_     )

				# Save tokenizer files
				A_       	=  CanineTokenizer()
				print(F"Save tokenizer files to {pytorch_dump_path}"     )
				tokenizer.save_pretrained(lowercase_     )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE   :		Tuple					 =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
				)
				parser.add_argument(
				    "--pytorch_dump_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to a folder where the PyTorch model will be placed.",
				)
				SCREAMING_SNAKE_CASE   :		Dict					 =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)

from collections import namedtuple

SCREAMING_SNAKE_CASE   :		Any					 =       namedtuple("from_to", "from_ to")

SCREAMING_SNAKE_CASE   :		str					 =       {
    "atm": from_to(1, 1),
    "pascal": from_to(0.0000098, 10_1325),
    "bar": from_to(0.986923, 1.01325),
    "kilopascal": from_to(0.00986923, 101.325),
    "megapascal": from_to(9.86923, 0.101325),
    "psi": from_to(0.068046, 14.6959),
    "inHg": from_to(0.0334211, 29.9213),
    "torr": from_to(0.00131579, 760),
}



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				if from_type not in PRESSURE_CONVERSION:
								raise ValueError(
								    F"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
								    + ''', '''.join(__UpperCamelCase     )     )
				if to_type not in PRESSURE_CONVERSION:
								raise ValueError(
								    F"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
								    + ''', '''.join(__UpperCamelCase     )     )
				return (
				    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()