
import argparse

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


logging.set_verbosity_info()






def 						lowerCamelCase__							(      a	,	a   )   ->     Tuple:
			# Initialize PyTorch model
			_A:						Optional[int]	    = CanineConfig()
			_A:						Any	    = 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:						Tuple	    = CanineTokenizer()
			print(f"""Save tokenizer files to {pytorch_dump_path}"""   )
			tokenizer.save_pretrained(lowercase__   )


if __name__ == "__main__":
			UpperCAmelCase__	:  str      =   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.',
			)
			UpperCAmelCase__	:  Any      =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



