





'''simple docstring'''



import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				data
       _snake_case     		=				[0x6745_2301, 0xEFCD_AB89, 0x98BA_DCFE, 0x1032_5476, 0xC3D2_E1F0]

     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase	):
       return ((n << b) | (n >> (32 - b))) & 0xFFFF_FFFF

     def 	UpperCamelCase(					self	):
       _snake_case     		=				b"\x80" + b"\x00" * (63 - (len(self.data	) + 8) % 64)
       _snake_case     		=				self.data + padding + struct.pack(">Q"						,   8 * len(self.data	)	)
       return padded_data

     def 	UpperCamelCase(					self	):
       return [
           self.padded_data[i : i + 64] for i in range(0						,   len(self.padded_data	)						,   64	)
       ]

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(struct.unpack(">16L"						,   lowerCamelCase	)	) + [0] * 64
       for i in range(16						,   80	):
         _snake_case     		=				self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])						,   1	)
       return w






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.padding()
       _snake_case     		=				self.split_blocks()
       for block in self.blocks:
         _snake_case     		=				self.expand_block(lowerCamelCase	)
         _snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case     		=				self.h
         for i in range(0						,   80	):
           if 0 <= i < 20:
             _snake_case     		=				(b & c) | ((~b) & d)
             _snake_case     		=				0x5A82_7999
           elif 20 <= i < 40:
             _snake_case     		=				b ^ c ^ d
             _snake_case     		=				0x6ED9_EBA1
           elif 40 <= i < 60:
             _snake_case     		=				(b & c) | (b & d) | (c & d)
             _snake_case     		=				0x8F1B_BCDC
           elif 60 <= i < 80:
             _snake_case     		=				b ^ c ^ d
             _snake_case     		=				0xCA62_C1D6
           _snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case     		=				(
               self.rotate(lowerCamelCase						,   5	) + f + e + k + expanded_block[i] & 0xFFFF_FFFF,
               a,
               self.rotate(lowerCamelCase						,   30	),
               c,
               d,
           )
         _snake_case     		=				(
             self.h[0] + a & 0xFFFF_FFFF,
             self.h[1] + b & 0xFFFF_FFFF,
             self.h[2] + c & 0xFFFF_FFFF,
             self.h[3] + d & 0xFFFF_FFFF,
             self.h[4] + e & 0xFFFF_FFFF,
         )
       return ("{:08x}" * 5).format(*self.h	)







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				b"Test String"
  assert SHAaHash(SCREAMING_SNAKE_CASE__       ).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__       ).hexdigest()  # noqa: S324




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				argparse.ArgumentParser(description="Process some strings or files"       )
  parser.add_argument(
      "--string"					,	dest="input_string"					,	default="Hello World!! Welcome to Cryptography"					,	help="Hash the string"					,	)
  parser.add_argument("--file"					,	dest="input_file"					,	help="Hash contents of a file"       )
  _snake_case     		=				parser.parse_args()
  _snake_case     		=				args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file					,	"rb"       ) as f:
      _snake_case     		=				f.read()
  else:
    _snake_case     		=				bytes(SCREAMING_SNAKE_CASE__					,	"utf-8"       )
  print(SHAaHash(SCREAMING_SNAKE_CASE__       ).final_hash()       )


if __name__ == "__main__":
 main()
 import doctest

 doctest.testmod()









'''simple docstring'''



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import fa_score, matthews_corrcoef


__magic_name__   :						List[str]    =			(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  return (preds == labels).mean()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__       )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       ), f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mrpc":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "sts-b":
    return pearson_and_spearman(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "qqp":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "rte":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "hans":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
    raise ValueError(f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''       )
  if task_name == "xnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )


