


import sacrebleu as scb
from packaging import version

import datasets


lowercase_								=    '\\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_								=    '\\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_								=    '\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  :				List[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 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]="exp"		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				SCREAMING_SNAKE_CASE_  :				List[str]=False		,				SCREAMING_SNAKE_CASE_  :				int=False		,				SCREAMING_SNAKE_CASE_  :				List[str]=None		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=False		,				):
													_a     =			len(references[0]		)
													if any(len(SCREAMING_SNAKE_CASE_		) != references_per_prediction for refs in references		):
																			raise ValueError('Sacrebleu requires the same number of references for each prediction'		)
													_a     =			[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_		)]
													_a     =			scb.corpus_bleu(
													    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				smooth_method=SCREAMING_SNAKE_CASE_		,				smooth_value=SCREAMING_SNAKE_CASE_		,				force=SCREAMING_SNAKE_CASE_		,				lowercase=SCREAMING_SNAKE_CASE_		,				use_effective_order=SCREAMING_SNAKE_CASE_		,				**({'tokenize': tokenize} if tokenize else {})		,				)
													_a     =			{
													    '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 typing import TYPE_CHECKING

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


lowercase_								=    {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    ['CodeGenTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'CodeGenForCausalLM',
		    'CodeGenModel',
		    'CodeGenPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
		from .tokenization_codegen import CodeGenTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_codegen_fast import CodeGenTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_codegen import (
				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CodeGenForCausalLM,
				    CodeGenModel,
				    CodeGenPreTrainedModel,
				)

else:
		import sys

		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


