






"""simple docstring"""



import sacrebleu as scb
from packaging import version

import datasets


_UpperCamelCase						:    List[str]		=						'\\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'

_UpperCamelCase						:    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'

_UpperCamelCase						:    Optional[Any]		=						'\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 			a	(  datasets.Metric      ):







    def      UpperCamelCase_ (     self			):
      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				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase="exp"				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						):
      lowercase				       =			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				       =			[[refs[i] for refs in references] for i in range(_lowerCamelCase			)]
      lowercase				       =			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				       =			{
          '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"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


    def        lowerCamelCase__						(     self  ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2    ,     )

