





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


snake_case__					:							Union[str, Any]													=     '\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

snake_case__					:							List[Any]													=     '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

snake_case__					:							Optional[int]													=     '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'





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




   def _lowerCAmelCase      (self	:List[Any] )->					Any:
         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''' ),
                 } )    ,   codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']    ,   reference_urls=[
                 '''https://en.wikipedia.org/wiki/BLEU''',
                 '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
             ]    ,   )






   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Optional[Any]=4    ,   _UpperCamelCase	:Optional[int]=False )->					Dict:
         __A          =				compute_bleu(
             reference_corpus=_UpperCamelCase    ,   translation_corpus=_UpperCamelCase    ,   max_order=_UpperCamelCase    ,   smooth=_UpperCamelCase )
         ((__A)   ,						(__A)   ,						(__A)   ,						(__A)   ,						(__A)   ,						(__A))          =				score
         return {
             "bleu": bleu,
             "precisions": precisions,
             "brevity_penalty": bp,
             "length_ratio": ratio,
             "translation_length": translation_length,
             "reference_length": reference_length,
         }












from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





