






'''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)
SCREAMING_SNAKE_CASE_				      =							"\\n"

SCREAMING_SNAKE_CASE_				      =							"\\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"

SCREAMING_SNAKE_CASE_				      =							"\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"

SCREAMING_SNAKE_CASE_				      =							["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
SCREAMING_SNAKE_CASE_				      =							""

try:
  from jiwer import transforms as tr

  SCREAMING_SNAKE_CASE_				      =							True
except ImportError:
  SCREAMING_SNAKE_CASE_				      =							False

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




  class 							lowerCAmelCase					(							tr.AbstractTransform   ):







     """simple docstring"""







     def __init__( self   ,   _A = " "			)    ->   str:
      __a    :	List[str]									=						sentence_delimiter



     def    __magic_name__    ( self   ,   _A			)    ->   Union[str, Any]:
      return list(_A			)



     def    __magic_name__    ( self   ,   _A			)    ->   int:
      __a    :	Optional[int]									=						[]
      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
  SCREAMING_SNAKE_CASE_				      =							tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
elif _jiwer_available:
  SCREAMING_SNAKE_CASE_				      =							tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )
else:
  SCREAMING_SNAKE_CASE_				      =							None






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 return float((preds == labels).mean()		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 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__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__="exp"       ,		SCREAMING_SNAKE_CASE__=None       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=None       ,		SCREAMING_SNAKE_CASE__=False       ,		):
 # xtreme-s can only have one label
 __a    :	Tuple									=						[[label] for label in labels]
 __a    :	Optional[int]									=						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"`.'		)

 __a    :	str									=						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'		)
 __a    :	Dict									=						[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE__		)]
 __a    :	int									=						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__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 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:
  __a    :	Union[str, Any]									=						compute_measures(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)['wer']

  __a    :	Dict									=						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__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__="wer"		):
   __a    :	List[Any]									=						0
   __a    :	Union[str, Any]									=						0
   for prediction, reference in zip(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
    if score_type == "wer":
     __a    :	Optional[int]									=						compute_measures(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
    elif score_type == "cer":
     __a    :	Optional[int]									=						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 							lowerCAmelCase					(							datasets.Metric   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   Any:
    if self.config_name not in _CONFIG_NAMES:
     raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''			)

    __a    :	Any									=						'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    __magic_name__    ( self   ,   _A   ,   _A   ,   _A=None   ,   _A=None			)    ->   Dict:
    __a    :	List[str]									=						bleu_kwargs if bleu_kwargs is not None else {}
    __a    :	List[str]									=						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":
     __a    :	List[str]									=						bleu_kwargs.pop('smooth_method'   ,   'exp'			)
     __a    :	List[str]									=						bleu_kwargs.pop('smooth_value'   ,   _A			)
     __a    :	List[Any]									=						bleu_kwargs.pop('force'   ,   _A			)
     __a    :	str									=						bleu_kwargs.pop('lowercase'   ,   _A			)
     __a    :	Optional[Any]									=						bleu_kwargs.pop('tokenize'   ,   _A			)
     __a    :	str									=						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"]:
     __a    :	int									=						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}'''			)












'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						UNetaDModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,   )
    return model



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




