


'''simple docstring'''




import argparse
import struct
import unittest

class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      bytes    )				->  None:
        lowerCAmelCase     =			data

        # Initialize hash values
        lowerCAmelCase     =			[
            0X6_A_0_9_E_6_6_7,
            0XB_B_6_7_A_E_8_5,
            0X3_C_6_E_F_3_7_2,
            0XA_5_4_F_F_5_3_A,
            0X5_1_0_E_5_2_7_F,
            0X9_B_0_5_6_8_8_C,
            0X1_F_8_3_D_9_A_B,
            0X5_B_E_0_C_D_1_9,
        ]

        # Initialize round constants
        lowerCAmelCase     =			[
            0X4_2_8_A_2_F_9_8,
            0X7_1_3_7_4_4_9_1,
            0XB_5_C_0_F_B_C_F,
            0XE_9_B_5_D_B_A_5,
            0X3_9_5_6_C_2_5_B,
            0X5_9_F_1_1_1_F_1,
            0X9_2_3_F_8_2_A_4,
            0XA_B_1_C_5_E_D_5,
            0XD_8_0_7_A_A_9_8,
            0X1_2_8_3_5_B_0_1,
            0X2_4_3_1_8_5_B_E,
            0X5_5_0_C_7_D_C_3,
            0X7_2_B_E_5_D_7_4,
            0X8_0_D_E_B_1_F_E,
            0X9_B_D_C_0_6_A_7,
            0XC_1_9_B_F_1_7_4,
            0XE_4_9_B_6_9_C_1,
            0XE_F_B_E_4_7_8_6,
            0X0_F_C_1_9_D_C_6,
            0X2_4_0_C_A_1_C_C,
            0X2_D_E_9_2_C_6_F,
            0X4_A_7_4_8_4_A_A,
            0X5_C_B_0_A_9_D_C,
            0X7_6_F_9_8_8_D_A,
            0X9_8_3_E_5_1_5_2,
            0XA_8_3_1_C_6_6_D,
            0XB_0_0_3_2_7_C_8,
            0XB_F_5_9_7_F_C_7,
            0XC_6_E_0_0_B_F_3,
            0XD_5_A_7_9_1_4_7,
            0X0_6_C_A_6_3_5_1,
            0X1_4_2_9_2_9_6_7,
            0X2_7_B_7_0_A_8_5,
            0X2_E_1_B_2_1_3_8,
            0X4_D_2_C_6_D_F_C,
            0X5_3_3_8_0_D_1_3,
            0X6_5_0_A_7_3_5_4,
            0X7_6_6_A_0_A_B_B,
            0X8_1_C_2_C_9_2_E,
            0X9_2_7_2_2_C_8_5,
            0XA_2_B_F_E_8_A_1,
            0XA_8_1_A_6_6_4_B,
            0XC_2_4_B_8_B_7_0,
            0XC_7_6_C_5_1_A_3,
            0XD_1_9_2_E_8_1_9,
            0XD_6_9_9_0_6_2_4,
            0XF_4_0_E_3_5_8_5,
            0X1_0_6_A_A_0_7_0,
            0X1_9_A_4_C_1_1_6,
            0X1_E_3_7_6_C_0_8,
            0X2_7_4_8_7_7_4_C,
            0X3_4_B_0_B_C_B_5,
            0X3_9_1_C_0_C_B_3,
            0X4_E_D_8_A_A_4_A,
            0X5_B_9_C_C_A_4_F,
            0X6_8_2_E_6_F_F_3,
            0X7_4_8_F_8_2_E_E,
            0X7_8_A_5_6_3_6_F,
            0X8_4_C_8_7_8_1_4,
            0X8_C_C_7_0_2_0_8,
            0X9_0_B_E_F_F_F_A,
            0XA_4_5_0_6_C_E_B,
            0XB_E_F_9_A_3_F_7,
            0XC_6_7_1_7_8_F_2,
        ]

        lowerCAmelCase     =			self.preprocessing(self.data    )
        self.final_hash()





     @staticmethod
     def 							__UpperCAmelCase      ( UpperCAmelCase__       :      bytes    )				->  bytes:
        lowerCAmelCase     =			b'\x80' + (b'\x00' * (6_3 - (len(UpperCAmelCase__    ) + 8) % 6_4))
        lowerCAmelCase     =			struct.pack('>Q'    ,      (len(UpperCAmelCase__    ) * 8)    )
        return data + padding + big_endian_integer





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  None:
        # Convert into blocks of 64 bytes
        lowerCAmelCase     =			[
            self.preprocessed_data[x : x + 6_4]
            for x in range(0    ,      len(self.preprocessed_data    )    ,      6_4    )
        ]

        for block in self.blocks:
           # Convert the given block into a list of 4 byte integers
           lowerCAmelCase     =			list(struct.unpack('>16L'    ,      UpperCAmelCase__    )    )
           # add 48 0-ed integers
           words += [0] * 4_8

           lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self.hashes

           for index in range(0    ,      6_4    ):
              if index > 1_5:
                 # modify the zero-ed indexes at the end of the array
                 lowerCAmelCase     =			(
                     self.ror(words[index - 1_5]    ,      7    )
                     ^ self.ror(words[index - 1_5]    ,      1_8    )
                     ^ (words[index - 1_5] >> 3)
                 )
                 lowerCAmelCase     =			(
                     self.ror(words[index - 2]    ,      1_7    )
                     ^ self.ror(words[index - 2]    ,      1_9    )
                     ^ (words[index - 2] >> 1_0)
                 )

                 lowerCAmelCase     =			(
                     words[index - 1_6] + sa + words[index - 7] + sa
                 ) % 0X1_0_0_0_0_0_0_0_0

              # Compression
              lowerCAmelCase     =			self.ror(UpperCAmelCase__    ,      6    ) ^ self.ror(UpperCAmelCase__    ,      1_1    ) ^ self.ror(UpperCAmelCase__    ,      2_5    )
              lowerCAmelCase     =			(e & f) ^ ((~e & 0XF_F_F_F_F_F_F_F) & g)
              lowerCAmelCase     =			(
                  h + sa + ch + self.round_constants[index] + words[index]
              ) % 0X1_0_0_0_0_0_0_0_0
              lowerCAmelCase     =			self.ror(UpperCAmelCase__    ,      2    ) ^ self.ror(UpperCAmelCase__    ,      1_3    ) ^ self.ror(UpperCAmelCase__    ,      2_2    )
              lowerCAmelCase     =			(a & b) ^ (a & c) ^ (b & c)
              lowerCAmelCase     =			(sa + maj) % 0X1_0_0_0_0_0_0_0_0

              lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			(
                  g,
                  f,
                  e,
                  ((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
                  c,
                  b,
                  a,
                  ((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
              )

           lowerCAmelCase     =			[a, b, c, d, e, f, g, h]

           # Modify final values
           lowerCAmelCase     =			[
               ((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
               for index, element in enumerate(self.hashes    )
           ]

        lowerCAmelCase     =			''.join([hex(UpperCAmelCase__    )[2:].zfill(8    ) for value in self.hashes]    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  int:
        return 0XF_F_F_F_F_F_F_F & (value << (3_2 - rotations)) | (value >> rotations)




class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  None:
        import hashlib

        lowerCAmelCase     =			bytes('Test String'    ,      'utf-8'    )
        self.assertEqual(SHAaaa(UpperCAmelCase__    ).hash    ,      hashlib.shaaaa(UpperCAmelCase__    ).hexdigest()    )





def 	a_					( ):
   import doctest

   doctest.testmod()

   lowerCAmelCase     =			argparse.ArgumentParser()
   parser.add_argument(
       '-s'	, '--string'	, dest='input_string'	, default='Hello World!! Welcome to Cryptography'	, help='Hash the string'	, )
   parser.add_argument(
       '-f'	, '--file'	, dest='input_file'	, help='Hash contents of a file'				)

   lowerCAmelCase     =			parser.parse_args()

   lowerCAmelCase     =			args.input_string

   # hash input should be a bytestring
   if args.input_file:
      with open(args.input_file	, 'rb'				) as f:
         lowerCAmelCase     =			f.read()
   else:
      lowerCAmelCase     =			bytes(lowerCamelCase	, 'utf-8'				)

   print(SHAaaa(lowerCamelCase				).hash				)


if __name__ == "__main__":
       main()









'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__snake_case    						="""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

__snake_case    						="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

__snake_case    						="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     Union[str, Any]				):
   return float((preds == labels).mean()				)





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str="binary"				):
   lowerCAmelCase     =			simple_accuracy(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			float(fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average=lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			{}
   for id_pred, label in zip(lowerCamelCase	, lowerCamelCase				):
      lowerCAmelCase     =			f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
      lowerCAmelCase     =			id_pred['prediction']
      if question_id in question_map:
         question_map[question_id].append((pred, label)				)
      else:
         lowerCAmelCase     =			[(pred, label)]
   lowerCAmelCase     ,  lowerCAmelCase     =			[], []
   for question, preds_labels in question_map.items():
      lowerCAmelCase     ,  lowerCAmelCase     =			zip(*lowerCamelCase				)
      lowerCAmelCase     =			fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average='macro'				)
      fas.append(lowerCamelCase				)
      lowerCAmelCase     =			int(sum(pred == label for pred, label in preds_labels				) == len(lowerCamelCase				)				)
      ems.append(lowerCamelCase				)
   lowerCAmelCase     =			float(sum(lowerCamelCase				) / len(lowerCamelCase				)				)
   lowerCAmelCase     =			sum(lowerCamelCase				) / len(lowerCamelCase				)
   lowerCAmelCase     =			float(fa_score(y_true=lowerCamelCase	, y_pred=[id_pred['prediction'] for id_pred in ids_preds]				)				)
   return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION		)
class 		UpperCAmelCase_      (  datasets.Metric		):





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        if self.config_name not in [
            "boolq",
            "cb",
            "copa",
            "multirc",
            "record",
            "rte",
            "wic",
            "wsc",
            "wsc.fixed",
            "axb",
            "axg",
        ]:
           raise KeyError(
               'You should supply a configuration name selected in '
               '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'    )
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,      citation=_CITATION    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(self._get_feature_types()    )    ,      codebase_urls=[]    ,      reference_urls=[]    ,      format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None    ,      )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  str:
        if self.config_name == "record":
           return {
               "predictions": {
                   "idx": {
                       "passage": datasets.Value('int64'    ),
                       "query": datasets.Value('int64'    ),
                   },
                   "prediction_text": datasets.Value('string'    ),
               },
               "references": {
                   "idx": {
                       "passage": datasets.Value('int64'    ),
                       "query": datasets.Value('int64'    ),
                   },
                   "answers": datasets.Sequence(datasets.Value('string'    )    ),
               },
           }
        elif self.config_name == "multirc":
           return {
               "predictions": {
                   "idx": {
                       "answer": datasets.Value('int64'    ),
                       "paragraph": datasets.Value('int64'    ),
                       "question": datasets.Value('int64'    ),
                   },
                   "prediction": datasets.Value('int64'    ),
               },
               "references": datasets.Value('int64'    ),
           }
        else:
           return {
               "predictions": datasets.Value('int64'    ),
               "references": datasets.Value('int64'    ),
           }





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Any:
        if self.config_name == "axb":
           return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase__    ,      UpperCAmelCase__    )}
        elif self.config_name == "cb":
           return acc_and_fa(UpperCAmelCase__    ,      UpperCAmelCase__    ,      fa_avg='macro'    )
        elif self.config_name == "record":
           lowerCAmelCase     =			[
               {
                   'qas': [
                       {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                       for ref in references
                   ]
               }
           ]
           lowerCAmelCase     =			{pred['idx']['query']: pred['prediction_text'] for pred in predictions}
           return evaluate_record(UpperCAmelCase__    ,      UpperCAmelCase__    )[0]
        elif self.config_name == "multirc":
           return evaluate_multirc(UpperCAmelCase__    ,      UpperCAmelCase__    )
        elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
           return {"accuracy": simple_accuracy(UpperCAmelCase__    ,      UpperCAmelCase__    )}
        else:
           raise KeyError(
               'You should supply a configuration name selected in '
               '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'    )





