


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__snake_case   							=						'''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

__snake_case   							=						'''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

__snake_case   							=						'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''



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







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                    {
                        'predictions': datasets.Sequence(datasets.Value('string',id='token'					),id='sequence'					),
                        'references': datasets.Sequence(
                            datasets.Sequence(datasets.Value('string',id='token'					),id='sequence'					),id='references'					),
                    }					),)







       def 		snake_case_	(			self: Union[str, Any],A_: List[List[List[str]]],A_: List[List[str]],A_: int = 1,A_: int = 4,):






            '''simple docstring'''




            return {
                "google_bleu": gleu_score.corpus_gleu(
                    list_of_references=A_,hypotheses=A_,min_len=A_,max_len=A_					)
            }






from collections.abc import Generator
from math import sin






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     if len(_lowercase ) != 32:
          raise ValueError('Input must be of length 32' )

     __UpperCamelCase =			B''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     if i < 0:
          raise ValueError('Input must be non-negative' )

     __UpperCamelCase =			format(_lowercase					,  '08x' )[-8:]
     __UpperCamelCase =			B''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
     return little_endian_hex






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     __UpperCamelCase =			B''
     for char in message:
          bit_string += format(_lowercase					,  '08b' ).encode('utf-8' )
     __UpperCamelCase =			format(len(_lowercase )					,  '064b' ).encode('utf-8' )

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_lowercase ) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

     return bit_string






def 		_A		(		_lowercase )					-> Generator[list[int], None, None]:


     """simple docstring"""


     if len(_lowercase ) % 5_12 != 0:
          raise ValueError('Input must have length that\'s a multiple of 512' )

     for pos in range(0					,  len(_lowercase )					,  5_12 ):
          __UpperCamelCase =			bit_string[pos : pos + 5_12]
          __UpperCamelCase =			[]
          for i in range(0					,  5_12					,  32 ):
               block_words.append(int(to_little_endian(block[i : i + 32] )					,  2 ) )
          yield block_words






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     if i < 0:
          raise ValueError('Input must be non-negative' )

     __UpperCamelCase =			format(_lowercase					,  '032b' )
     __UpperCamelCase =			''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_lowercase					,  2 )






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (a + b) % 2**32






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     if i < 0:
          raise ValueError('Input must be non-negative' )
     if shift < 0:
          raise ValueError('Shift must be non-negative' )
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     __UpperCamelCase =			preprocess(_lowercase )

     __UpperCamelCase =			[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

     # Starting states
     __UpperCamelCase =			0X67_45_23_01
     __UpperCamelCase =			0Xef_cd_ab_89
     __UpperCamelCase =			0X98_ba_dc_fe
     __UpperCamelCase =			0X10_32_54_76

     __UpperCamelCase =			[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(_lowercase ):
          __UpperCamelCase =			aa
          __UpperCamelCase =			ba
          __UpperCamelCase =			ca
          __UpperCamelCase =			da

          # Hash current chunk
          for i in range(64 ):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    __UpperCamelCase =			d ^ (b & (c ^ d))
                    __UpperCamelCase =			i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    __UpperCamelCase =			c ^ (d & (b ^ c))
                    __UpperCamelCase =			(5 * i + 1) % 16
               elif i <= 47:
                    __UpperCamelCase =			b ^ c ^ d
                    __UpperCamelCase =			(3 * i + 5) % 16
               else:
                    __UpperCamelCase =			c ^ (b | not_aa(_lowercase ))
                    __UpperCamelCase =			(7 * i) % 16
               __UpperCamelCase =			(f + a + added_consts[i] + block_words[g]) % 2**32
               __UpperCamelCase =			d
               __UpperCamelCase =			c
               __UpperCamelCase =			b
               __UpperCamelCase =			sum_aa(_lowercase					,  left_rotate_aa(_lowercase					,  shift_amounts[i] ) )

          # Add hashed chunk to running total
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )

     __UpperCamelCase =			reformat_hex(_lowercase ) + reformat_hex(_lowercase ) + reformat_hex(_lowercase ) + reformat_hex(_lowercase )
     return digest


if __name__ == "__main__":
    import doctest

    doctest.testmod()


