



'''simple docstring'''





_UpperCamelCase							    =						1000

def 	a_					(							_lowerCAmelCase	)				->				float:
  return x * x

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase = 4	)				->				float:
  assert callable(
      _lowerCAmelCase	), F'the function(object) passed should be callable your input : {function}'
  assert isinstance(_lowerCAmelCase	,(float, int)	), F'a should be float or integer your input : {a}'
  assert isinstance(function(_lowerCAmelCase	)	,(float, int)	), (
      "the function should return integer or float return type of your function, "
      F'{type(_lowerCAmelCase	)}'
  )
  assert isinstance(_lowerCAmelCase	,(float, int)	), F'b should be float or integer your input : {b}'
  assert (
      isinstance(_lowerCAmelCase	,_lowerCAmelCase	) 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

  __lowerCamelCase	:	Optional[int]   	=			(b - a) / N_STEPS
  __lowerCamelCase	:	int   	=			function(_lowerCAmelCase	) + function(_lowerCAmelCase	)

  for i in range(1	,_lowerCAmelCase	):
    __lowerCamelCase	:	Optional[int]   	=			a + h * i
    result += function(_lowerCAmelCase	) * (4 if i % 2 else 2)

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()











'''simple docstring'''





import argparse

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


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

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

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

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

  # Save tokenizer files
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


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. 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							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






