






'''simple docstring'''



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




      def __init__(	self			, lowerCamelCase   )				->Any:



         '''simple docstring'''
         __a					  =     data
         __a					  =     [0x67452301, 0xefcdab89, 0x98badcfe, 0x10325476, 0xc3d2e1f0]




      @staticmethod
      def 			__UpperCamelCase				(	lowerCamelCase			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         return ((n << b) | (n >> (32 - b))) & 0xffffffff




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     B'\x80' + B'\x00' * (63 - (len(self.data   ) + 8) % 64)
         __a					  =     self.data + padding + struct.pack('>Q'			, 8 * len(self.data   )   )
         return padded_data




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return [
             self.padded_data[i : i + 64] for i in range(0			, len(self.padded_data   )			, 64   )
         ]




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Tuple:



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






      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     self.padding()
         __a					  =     self.split_blocks()
         for block in self.blocks:
            __a					  =     self.expand_block(lowerCamelCase   )
            __a  ,      __a  ,      __a  ,      __a  ,      __a					  =     self.h
            for i in range(0			, 80   ):
               if 0 <= i < 20:
                  __a					  =     (b & c) | ((~b) & d)
                  __a					  =     0x5a827999
               elif 20 <= i < 40:
                  __a					  =     b ^ c ^ d
                  __a					  =     0x6ed9eba1
               elif 40 <= i < 60:
                  __a					  =     (b & c) | (b & d) | (c & d)
                  __a					  =     0x8f1bbcdc
               elif 60 <= i < 80:
                  __a					  =     b ^ c ^ d
                  __a					  =     0xca62c1d6
               __a  ,      __a  ,      __a  ,      __a  ,      __a					  =     (
                   self.rotate(lowerCamelCase			, 5   ) + f + e + k + expanded_block[i] & 0xffffffff,
                   a,
                   self.rotate(lowerCamelCase			, 30   ),
                   c,
                   d,
               )
            __a					  =     (
                self.h[0] + a & 0xffffffff,
                self.h[1] + b & 0xffffffff,
                self.h[2] + c & 0xffffffff,
                self.h[3] + d & 0xffffffff,
                self.h[4] + e & 0xffffffff,
            )
         return ("{:08x}" * 5).format(*self.h   )







def  __UpperCAmelCase						(					)	->      int:

   """simple docstring"""
   __a					  =     b'Test String'
   assert SHAaHash(SCREAMING_SNAKE_CASE__     ).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__     ).hexdigest()  # noqa: S324



def  __UpperCAmelCase						(					)	->      Any:

   """simple docstring"""
   __a					  =     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'     )
   __a					  =     parser.parse_args()
   __a					  =     args.input_string
   # In any case hash input should be a bytestring
   if args.input_file:
      with open(args.input_file,       'rb'     ) as f:
         __a					  =     f.read()
   else:
      __a					  =     bytes(SCREAMING_SNAKE_CASE__,       'utf-8'     )
   print(SHAaHash(SCREAMING_SNAKE_CASE__     ).final_hash()     )


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

 doctest.testmod()







'''simple docstring'''



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__UpperCamelCase   :							Optional[int]         =   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__UpperCamelCase   :							Tuple         =   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__UpperCamelCase   :							Optional[int]         =   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str     )	->      List[Any]:

   """simple docstring"""
   return float((preds == labels).mean()     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     simple_accuracy(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     float(fa_score(y_true=SCREAMING_SNAKE_CASE__,       y_pred=SCREAMING_SNAKE_CASE__     )     )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      int:

   """simple docstring"""
   __a					  =     float(pearsonr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   __a					  =     float(spearmanr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }



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




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         if self.config_name not in [
             "sst2",
             "mnli",
             "mnli_mismatched",
             "mnli_matched",
             "cola",
             "stsb",
             "mrpc",
             "qqp",
             "qnli",
             "rte",
             "wnli",
             "hans",
         ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'   )
         return datasets.MetricInfo(
             description=_DESCRIPTION			, citation=_CITATION			, inputs_description=_KWARGS_DESCRIPTION			, features=datasets.Features(
                 {
                     'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'   ),
                     'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'   ),
                 }   )			, codebase_urls=[]			, reference_urls=[]			, format='numpy'			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(lowerCamelCase			, lowerCamelCase   )}
         elif self.config_name == "stsb":
            return pearson_and_spearman(lowerCamelCase			, lowerCamelCase   )
         elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(lowerCamelCase			, lowerCamelCase   )
         elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(lowerCamelCase			, lowerCamelCase   )}
         else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'   )