





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:  Any   =   {"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   ["""DetrFeatureExtractor"""]
			__UpperCamelCase:  Union[str, Any]   =   ["""DetrImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  int   =   [
			    """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DetrForObjectDetection""",
			    """DetrForSegmentation""",
			    """DetrModel""",
			    """DetrPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_detr import DetrFeatureExtractor
						from .image_processing_detr import DetrImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_detr import (
						    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetrForObjectDetection,
						    DetrForSegmentation,
						    DetrModel,
						    DetrPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  List[str]   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import sacrebleu as scb
from packaging import version

import datasets


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

__UpperCamelCase:  int   =   """\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

__UpperCamelCase:  int   =   """
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

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`): 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).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    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`).
    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:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    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`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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


	'''simple docstring'''





	def       snake_case__(	self:	Optional[Any] ):
			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       snake_case__(	self:	Optional[Any],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	List[str],				lowerCamelCase_:	Dict="exp",				lowerCamelCase_:	Optional[int]=None,				lowerCamelCase_:	Dict=False,				lowerCamelCase_:	int=False,				lowerCamelCase_:	Tuple=None,				lowerCamelCase_:	str=False,				):
			lowercase__				:			int            =		len(references[0] )
			if any(len(lowerCamelCase_ ) != references_per_prediction for refs in references ):
					raise ValueError('Sacrebleu requires the same number of references for each prediction' )
			lowercase__				:			Optional[Any]            =		[[refs[i] for refs in references] for i in range(lowerCamelCase_ )]
			lowercase__				:			List[str]            =		scb.corpus_bleu(
			    lowerCamelCase_,				lowerCamelCase_,				smooth_method=lowerCamelCase_,				smooth_value=lowerCamelCase_,				force=lowerCamelCase_,				lowercase=lowerCamelCase_,				use_effective_order=lowerCamelCase_,				**({'tokenize': tokenize} if tokenize else {}),				)
			lowercase__				:			Union[str, 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


