def 					UpperCamelCase_						( snake_case_	:	List[Any]						,		snake_case_	:	List[str] = 10_00				) ->					str:






 '''simple docstring'''

 if (not isinstance(A__						,		A__				)) or (digit_position <= 0):
  raise ValueError("""Digit position must be a positive integer"""				)
 elif (not isinstance(A__						,		A__				)) or (precision < 0):
  raise ValueError("""Precision must be a nonnegative integer"""				)

 # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
 # accurate
 __lowerCAmelCase    							=						(
     4 * _subsum(A__						,		1						,		A__				)
     - 2 * _subsum(A__						,		4						,		A__				)
     - _subsum(A__						,		5						,		A__				)
     - _subsum(A__						,		6						,		A__				)
 )

 # return the first hex digit of the fractional part of the result
 return hex(int((sum_result % 1) * 16				)				)[2:]







def 					UpperCamelCase_						( snake_case_	:	List[Any]						,		snake_case_	:	str						,		snake_case_	:	Any				) ->					float:






 '''simple docstring'''

 __lowerCAmelCase    							=						0.0
 for sum_index in range(digit_pos_to_extract + precision				):
  __lowerCAmelCase    							=						8 * sum_index + denominator_addend
  if sum_index < digit_pos_to_extract:
   # if the exponential term is an integer and we mod it by the denominator
   # before dividing, only the integer part of the sum will change;
   # the fractional part will not
   __lowerCAmelCase    							=						pow(
       16						,		digit_pos_to_extract - 1 - sum_index						,		A__				)
  else:
   __lowerCAmelCase    							=						pow(16						,		digit_pos_to_extract - 1 - sum_index				)
  total += exponential_term / denominator
 return total


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 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

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

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

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

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    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.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






