
"""simple docstring"""







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







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        lowerCAmelCase_       :    Optional[int]								=			data
        lowerCAmelCase_       :    Tuple								=			[0X67_45_23_01, 0XEF_CD_AB_89, 0X98_BA_DC_FE, 0X10_32_54_76, 0XC3_D2_E1_F0]



      @staticmethod
      def 					SCREAMING_SNAKE_CASE__			(				SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        return ((n << b) | (n >> (3_2 - b))) & 0XFF_FF_FF_FF



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			b'\x80' + b'\x00' * (6_3 - (len(self.data	) + 8) % 6_4)
        lowerCAmelCase_       :    List[str]								=			self.data + padding + struct.pack('>Q'   ,      8 * len(self.data	)	)
        return padded_data



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        return [
            self.padded_data[i : i + 6_4] for i in range(0   ,      len(self.padded_data	)   ,      6_4	)
        ]



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        lowerCAmelCase_       :    Optional[Any]								=			list(struct.unpack('>16L'   ,      SCREAMING_SNAKE_CASE_	)	) + [0] * 6_4
        for i in range(1_6   ,      8_0	):
          lowerCAmelCase_       :    int								=			self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 1_4] ^ w[i - 1_6])   ,      1	)
        return w





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Any								=			self.padding()
        lowerCAmelCase_       :    Tuple								=			self.split_blocks()
        for block in self.blocks:
          lowerCAmelCase_       :    List[str]								=			self.expand_block(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    List[str]								=			self.h
          for i in range(0   ,      8_0	):
            if 0 <= i < 2_0:
              lowerCAmelCase_       :    Union[str, Any]								=			(b & c) | ((~b) & d)
              lowerCAmelCase_       :    Optional[Any]								=			0X5A_82_79_99
            elif 2_0 <= i < 4_0:
              lowerCAmelCase_       :    int								=			b ^ c ^ d
              lowerCAmelCase_       :    Any								=			0X6E_D9_EB_A1
            elif 4_0 <= i < 6_0:
              lowerCAmelCase_       :    str								=			(b & c) | (b & d) | (c & d)
              lowerCAmelCase_       :    List[str]								=			0X8F_1B_BC_DC
            elif 6_0 <= i < 8_0:
              lowerCAmelCase_       :    Optional[Any]								=			b ^ c ^ d
              lowerCAmelCase_       :    Optional[Any]								=			0XCA_62_C1_D6
            lowerCAmelCase_       :    int								=			(
                self.rotate(SCREAMING_SNAKE_CASE_   ,      5	) + f + e + k + expanded_block[i] & 0XFF_FF_FF_FF,
                a,
                self.rotate(SCREAMING_SNAKE_CASE_   ,      3_0	),
                c,
                d,
            )
          lowerCAmelCase_       :    Any								=			(
              self.h[0] + a & 0XFF_FF_FF_FF,
              self.h[1] + b & 0XFF_FF_FF_FF,
              self.h[2] + c & 0XFF_FF_FF_FF,
              self.h[3] + d & 0XFF_FF_FF_FF,
              self.h[4] + e & 0XFF_FF_FF_FF,
          )
        return ("{:08x}" * 5).format(*self.h	)




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

  """simple docstring"""




  lowerCAmelCase_       :    str								=			b'Test String'
  assert SHAaHash(lowerCAmelCase__						).final_hash() == hashlib.shaa(lowerCAmelCase__						).hexdigest()  # noqa: S324

def     UpperCamelCase_   (   )						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			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'						)
  lowerCAmelCase_       :    List[str]								=			parser.parse_args()
  lowerCAmelCase_       :    Union[str, Any]								=			args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file   ,       'rb'						) as f:
      lowerCAmelCase_       :    Union[str, Any]								=			f.read()
  else:
    lowerCAmelCase_       :    List[str]								=			bytes(lowerCAmelCase__   ,       'utf-8'						)
  print(SHAaHash(lowerCAmelCase__						).final_hash()						)


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

     doctest.testmod()










"""simple docstring"""







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowercase__		:    Tuple				   =     datasets.logging.get_logger(__name__)


lowercase__		:    List[Any]				   =     """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowercase__		:    Tuple				   =     """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowercase__		:    List[Any]				   =     """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowercase__		:    List[Any]				   =     {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}







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




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        return datasets.MetricInfo(
            description=_DESCRIPTION   ,      citation=_CITATION   ,      homepage='https://github.com/google-research/bleurt'   ,      inputs_description=_KWARGS_DESCRIPTION   ,      features=datasets.Features(
                {
                    'predictions': datasets.Value('string'   ,      id='sequence'	),
                    'references': datasets.Value('string'   ,      id='sequence'	),
                }	)   ,      codebase_urls=['https://github.com/google-research/bleurt']   ,      reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				str	):
        # check that config name specifies a valid BLEURT model
        if self.config_name == "default":
          logger.warning(
              'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
              'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'	)
          lowerCAmelCase_       :    List[Any]								=			'bleurt-base-128'

        if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase_       :    List[Any]								=			self.config_name.lower()

        elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase_       :    Tuple								=			self.config_name.upper()

        else:
          raise KeyError(
              F"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"	)

        # download the model checkpoint specified by self.config_name and set up the scorer
        lowerCAmelCase_       :    List[Any]								=			dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]	)
        lowerCAmelCase_       :    List[str]								=			score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    Tuple								=			self.scorer.score(references=SCREAMING_SNAKE_CASE_   ,      candidates=SCREAMING_SNAKE_CASE_	)
        return {"scores": scores}





