


import sacrebleu as scb
from packaging import version

import datasets


lowerCamelCase						 =	'\\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

lowerCamelCase						 =	'\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n'

lowerCamelCase						 =	'\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = ["hello there general kenobi", "foo bar foobar"]\n        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = ["hello there general kenobi",\n        ...                 "on our way to ankh morpork"]\n        >>> references = [["hello there general kenobi", "hello there !"],\n        ...                 ["goodbye ankh morpork", "ankh morpork"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        39.8\n'




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






      def 	lowerCamelCase	( self						:					int			) ->						Optional[int]:
             """simple docstring"""




             if version.parse(scb.__version__			) < version.parse('1.4.12'			):
                    raise ImportWarning(
                        'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                        'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)
             return datasets.MetricInfo(
                 description=_DESCRIPTION      ,       citation=_CITATION      ,       homepage='https://github.com/mjpost/sacreBLEU'      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
                     {
                         'predictions': datasets.Value('string'      ,       id='sequence'			),
                         'references': datasets.Sequence(datasets.Value('string'      ,       id='sequence'			)      ,       id='references'			),
                     }			)      ,       codebase_urls=['https://github.com/mjpost/sacreBLEU']      ,       reference_urls=[
                     'https://github.com/mjpost/sacreBLEU',
                     '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]      ,       lowercase_						:					List[str]      ,       lowercase_						:					List[str]      ,       lowercase_						:					Optional[int]="exp"      ,       lowercase_						:					Optional[Any]=None      ,       lowercase_						:					List[str]=False      ,       lowercase_						:					str=False      ,       lowercase_						:					str=None      ,       lowercase_						:					Optional[int]=False      ,       ) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =len(references[0]			)
             if any(len(lowercase_			) != references_per_prediction for refs in references			):
                    raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
             _lowerCamelCase	:		Union[str, Any]          =[[refs[i] for refs in references] for i in range(lowercase_			)]
             _lowerCamelCase	:		int          =scb.corpus_bleu(
                 lowercase_      ,       lowercase_      ,       smooth_method=lowercase_      ,       smooth_value=lowercase_      ,       force=lowercase_      ,       lowercase=lowercase_      ,       use_effective_order=lowercase_      ,       **({'tokenize': tokenize} if tokenize else {})      ,       )
             _lowerCamelCase	:		str          ={
                 'score': output.score,
                 'counts': output.counts,
                 'totals': output.totals,
                 'precisions': output.precisions,
                 'bp': output.bp,
                 'sys_len': output.sys_len,
                 'ref_len': output.ref_len,
             }
             return output_dict






import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =np.arange(start=3		,      stop=SCREAMING_SNAKE_CASE__		,      step=4					).tolist() if k > 0 else [10]
       _lowerCamelCase	:		List[Any]          =_quantize(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       # count number of elements per group
       _lowerCamelCase	:		Union[str, Any]          =np.unique(SCREAMING_SNAKE_CASE__		,      return_counts=SCREAMING_SNAKE_CASE__					)[1]
       _lowerCamelCase	:		int          =[0] + bins + [np.inf]
       logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE__					)					)
       logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE__					)					)
       return groups




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =self.group_ids[idx]
                    buffer_per_group[group_id].append(lowercase_			)
                    samples_per_group[group_id].append(lowercase_			)
                    if len(buffer_per_group[group_id]			) == self.batch_size:
                           yield buffer_per_group[group_id]  # TODO
                           num_batches += 1
                           del buffer_per_group[group_id]
                    assert len(buffer_per_group[group_id]			) < self.batch_size

             # now we have run out of elements that satisfy
             # the group criteria, let's return the remaining
             # elements so that the size of the sampler is
             # deterministic
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =batch_idx[self.batch_size :]
                                  num_remaining -= 1
                    if len(lowercase_			) > 0:
                           yield batch_idx
                           num_remaining -= 1
             assert num_remaining == 0






      def __len__( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




             return (len(self.sampler			) + self.batch_size - 1) // self.batch_size


