import sacrebleu as scb
from packaging import version

import datasets


snake_case__						      =			'\\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'

snake_case__						      =			'\\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'

snake_case__						      =			'\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 			UpperCamelCase					(			datasets.Metric			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """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 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_="exp"					,					A_=None					,					A_=False					,					A_=False					,					A_=None					,					A_=False					,					)			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					len(references[0]    )
           if any(len(A_    ) != references_per_prediction for refs in references    ):
                 raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
           _lowerCamelCase           =					[[refs[i] for refs in references] for i in range(A_    )]
           _lowerCamelCase           =					scb.corpus_bleu(
               A_					,					A_					,					smooth_method=A_					,					smooth_value=A_					,					force=A_					,					lowercase=A_					,					use_effective_order=A_					,					**({'''tokenize''': tokenize} if tokenize else {})					,					)
           _lowerCamelCase           =					{
               '''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
from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


snake_case__						      =			AttentionProcessor



class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )