
"""simple docstring"""
import sacrebleu as scb
from packaging import version

import datasets


lowercase		:						Optional[int] =					'\\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'

lowercase		:						List[Any] =					'\\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'

lowercase		:						List[str] =					'\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    _snake_case  (						datasets.Metric       ):






       def 							__lowercase  (					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 							__lowercase  (					self						:			str						, snake_case__						:			Tuple						, snake_case__						:			Optional[int]						, snake_case__						:			List[Any]="exp"						, snake_case__						:			Any=None						, snake_case__						:			Optional[Any]=False						, snake_case__						:			Tuple=False						, snake_case__						:			str=None						, snake_case__						:			int=False						, ):






            """simple docstring"""



            __a:						List[str]		    =					len(references[0]    )
            if any(len(snake_case__    ) != references_per_prediction for refs in references    ):
                 raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
            __a:						Tuple		    =					[[refs[i] for refs in references] for i in range(snake_case__    )]
            __a:						Dict		    =					scb.corpus_bleu(
                snake_case__						, snake_case__						, smooth_method=snake_case__						, smooth_value=snake_case__						, force=snake_case__						, lowercase=snake_case__						, use_effective_order=snake_case__						, **({"""tokenize""": tokenize} if tokenize else {})						, )
            __a:						List[Any]		    =					{
                '''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


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
