





'''simple docstring'''

import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax






@require_flax
class  UpperCAmelCase			:
 def   UpperCAmelCase_							(       self   :str							)->							str:
     A__,			A__       =    self.prepare_init_args_and_inputs_for_common()

     A__       =    self.model_class(**lowercase_							)
     A__       =    model.init(inputs_dict["prng_key"]		, inputs_dict["sample"]							)
     jax.lax.stop_gradient(lowercase_							)

     A__       =    model.apply(lowercase_		, inputs_dict["sample"]							)

     if isinstance(lowercase_		, lowercase_							):
         A__       =    output.sample

     self.assertIsNotNone(lowercase_							)
     A__       =    inputs_dict["sample"].shape
     self.assertEqual(output.shape		, lowercase_		, "Input and output shapes do not match"							)
 def   UpperCAmelCase_							(       self   :Any							)->							Optional[int]:
     A__,			A__       =    self.prepare_init_args_and_inputs_for_common()

     A__       =    16
     A__       =    (16, 32)

     A__       =    self.model_class(**lowercase_							)
     A__       =    model.init(inputs_dict["prng_key"]		, inputs_dict["sample"]							)
     jax.lax.stop_gradient(lowercase_							)

     A__       =    model.apply(lowercase_		, inputs_dict["sample"]							)

     if isinstance(lowercase_		, lowercase_							):
         A__       =    output.sample

     self.assertIsNotNone(lowercase_							)
     A__       =    inputs_dict["sample"].shape
     self.assertEqual(output.shape		, lowercase_		, "Input and output shapes do not match"							)



 def   UpperCAmelCase_							(       self   :int							)->							Tuple:
     A__       =    "kwargs" in inspect.signature(self.model_class.__init__							).parameters
     A__       =    len(self.model_class._deprecated_kwargs							) > 0

     if has_kwarg_in_model_class and not has_deprecated_kwarg:
         raise ValueError(
             F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
             " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
             " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
             " [<deprecated_argument>]`"							)

     if not has_kwarg_in_model_class and has_deprecated_kwarg:
         raise ValueError(
             F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
             " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
             F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
             " from `_deprecated_kwargs = [<deprecated_argument>]`"							)










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


__lowerCAmelCase							:			Tuple				="\\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							:			Dict				="\\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							:			Dict				="\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       ):
 def   UpperCAmelCase_							(       self   :int							)->							Union[str, 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   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
     A__       =    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"							)
     A__       =    [[refs[i] for refs in references] for i in range(lowercase_							)]
     A__       =    scb.corpus_bleu(
         lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"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



