

'''simple docstring'''




from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     lowercase_		(						a	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_       )							->			int:


    """simple docstring"""
    super().__init__()
    self.register_modules(unet=a_				,	scheduler=a_       )





  @torch.no_grad()
  def __call__(       self				,	a_ = 1				,	a_ = None				,	a_ = 5_0				,	a_ = "pil"				,	a_ = True				,	**a_				,	)							->			Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""
    UpperCAmelCase  							=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,	generator=a_				,	)
    UpperCAmelCase  							=		image.to(self.device       )

    # set step values
    self.scheduler.set_timesteps(a_       )

    for t in self.progress_bar(self.scheduler.timesteps       ):
      # 1. predict noise model_output
      UpperCAmelCase  							=		self.unet(a_				,	a_       ).sample

      # 2. predict previous mean of image x_t-1 and add variance depending on eta
      # eta corresponds to η in paper and should be between [0, 1]
      # do x_t -> x_t-1
      UpperCAmelCase  							=		self.scheduler.step(a_				,	a_				,	a_       ).prev_sample

    UpperCAmelCase  							=		(image / 2 + 0.5).clamp(0				,	1       )
    UpperCAmelCase  							=		image.cpu().permute(0				,	2				,	3				,	1       ).numpy()
    if output_type == "pil":
      UpperCAmelCase  							=		self.numpy_to_pil(a_       )

    if not return_dict:
      return (image,), "This is a local test"

    return ImagePipelineOutput(images=a_       ), "This is a local test"





'''simple docstring'''




from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
  import importlib_metadata
else:
  import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
_a			:	List[Any]								=  '\\n'

_a			:	Optional[Any]								=  '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

_a			:	Dict								=  '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

_a			:	Dict								=  ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
_a			:	Dict								=  ''

try:
  from jiwer import transforms as tr

  _a			:	Optional[Any]								=  True
except ImportError:
  _a			:	List[Any]								=  False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):



  class     lowercase_		(						tr.AbstractTransform	):

    '''simple docstring'''





    def __init__(       self				,	a_ = " "       )							->			str:


      """simple docstring"""
      UpperCAmelCase  							=		sentence_delimiter
    def   snake_case_     (       self				,	a_       )							->			Optional[int]:


      """simple docstring"""
      return list(a_       )





    def   snake_case_     (       self				,	a_       )							->			List[str]:


      """simple docstring"""
      UpperCAmelCase  							=		[]
      for sent_idx, sentence in enumerate(a_       ):
        chars.extend(self.process_string(a_       )       )
        if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(a_       ) - 1:
          chars.append(self.sentence_delimiter       )
      return chars




  _a			:	List[Any]								=  tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
elif _jiwer_available:
  _a			:	int								=  tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )
else:
  _a			:	Optional[int]								=  None


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
  return float((preds == labels).mean()  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				Optional[Any]  ):
  return {
      "f1": float(fa_score(y_true=SCREAMING_SNAKE_CASE    ,				y_pred=SCREAMING_SNAKE_CASE    ,				average='macro'  )  ),
      "accuracy": simple_accuracy(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ),
  }


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				str="exp"    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]=None    ,				SCREAMING_SNAKE_CASE :				Any=False    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=False    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				SCREAMING_SNAKE_CASE :				List[Any]=False    ,				):
  # xtreme-s can only have one label
  UpperCAmelCase  							=		[[label] for label in labels]
  UpperCAmelCase  							=		list(SCREAMING_SNAKE_CASE  )
  try:
    import sacrebleu as scb
  except ImportError:
    raise ValueError(
        'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
        'You can install it via `pip install sacrebleu`.'  )

  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"`.'  )

  UpperCAmelCase  							=		len(labels[0]  )
  if any(len(SCREAMING_SNAKE_CASE  ) != references_per_prediction for refs in labels  ):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'  )
  UpperCAmelCase  							=		[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE  )]
  UpperCAmelCase  							=		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 {})    ,				)
  return {"bleu": output.score}


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  try:
    from jiwer import compute_measures
  except ImportError:
    raise ValueError(
        f'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
        'You can install it via `pip install jiwer`.'  )

  if concatenate_texts:
    UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )['wer']

    UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				truth_transform=SCREAMING_SNAKE_CASE    ,				hypothesis_transform=SCREAMING_SNAKE_CASE  )['wer']
    return {"wer": wer, "cer": cer}
  else:

    def compute_score(SCREAMING_SNAKE_CASE :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				List[Any]    ,				SCREAMING_SNAKE_CASE :				List[str]="wer"  ):
      UpperCAmelCase  							=		0
      UpperCAmelCase  							=		0
      for prediction, reference in zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
        if score_type == "wer":
          UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
        elif score_type == "cer":
          UpperCAmelCase  							=		compute_measures(
              SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				truth_transform=SCREAMING_SNAKE_CASE    ,				hypothesis_transform=SCREAMING_SNAKE_CASE  )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
      return incorrect / total

    return {"wer": compute_score(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				'wer'  ), "cer": compute_score(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				'cer'  )}



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

  '''simple docstring'''





  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    if self.config_name not in _CONFIG_NAMES:
      raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''       )

    UpperCAmelCase  							=		'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

    return datasets.MetricInfo(
        description=_DESCRIPTION				,	citation=_CITATION				,	inputs_description=_KWARGS_DESCRIPTION				,	features=datasets.Features(
            {'predictions': datasets.Value(a_       ), 'references': datasets.Value(a_       )}       )				,	codebase_urls=[]				,	reference_urls=[]				,	format='numpy'				,	)





  def   snake_case_     (       self				,	a_				,	a_				,	a_=None				,	a_=None       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		bleu_kwargs if bleu_kwargs is not None else {}
    UpperCAmelCase  							=		wer_kwargs if wer_kwargs is not None else {}

    if self.config_name == "fleurs-lang_id":
      return {"accuracy": simple_accuracy(a_				,	a_       )}
    elif self.config_name == "minds14":
      return fa_and_simple_accuracy(a_				,	a_       )
    elif self.config_name == "covost2":
      UpperCAmelCase  							=		bleu_kwargs.pop('smooth_method'				,	'exp'       )
      UpperCAmelCase  							=		bleu_kwargs.pop('smooth_value'				,	a_       )
      UpperCAmelCase  							=		bleu_kwargs.pop('force'				,	a_       )
      UpperCAmelCase  							=		bleu_kwargs.pop('lowercase'				,	a_       )
      UpperCAmelCase  							=		bleu_kwargs.pop('tokenize'				,	a_       )
      UpperCAmelCase  							=		bleu_kwargs.pop('use_effective_order'				,	a_       )
      return bleu(
          preds=a_				,	labels=a_				,	smooth_method=a_				,	smooth_value=a_				,	force=a_				,	lowercase=a_				,	tokenize=a_				,	use_effective_order=a_				,	)
    elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
      UpperCAmelCase  							=		wer_kwargs.pop('concatenate_texts'				,	a_       )
      return wer_and_cer(a_				,	a_				,	a_				,	self.config_name       )
    else:
      raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''       )


