






"""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_			: Dict				=			"\\n"

A_			: str				=			"\\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_			: List[str]				=			"\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_			: str				=			""

try:
      from jiwer import transforms as tr

      A_			: List[Any]				=			True
except ImportError:
      A_			: Tuple				=			False

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



      class 		a_      (	tr.AbstractTransform ):







            '''simple docstring'''

            def __init__(self,     lowerCamelCase_ = " "					):

             '''simple docstring'''



             lowerCamelCase__     :			Optional[Any]     				=						sentence_delimiter




            def 			a__							(self,     lowerCamelCase_					):

             '''simple docstring'''



             return list(UpperCAmelCase_					)





            def 			a__							(self,     lowerCamelCase_					):

             '''simple docstring'''



             lowerCamelCase__     :			Union[str, Any]     				=						[]
             for sent_idx, sentence in enumerate(UpperCAmelCase_					):
              chars.extend(self.process_string(UpperCAmelCase_					)					)
              if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase_					) - 1:
               chars.append(self.sentence_delimiter					)
             return chars


      A_			: Union[str, Any]				=			tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      A_			: List[Any]				=			tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      A_			: Tuple				=			None






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 return float((preds == labels).mean()				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 return {
     "f1": float(fa_score(y_true=a_			,   y_pred=a_			,   average='macro'				)				),
     "accuracy": simple_accuracy(a_			,   a_				),
 }






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase="exp"			,   _lowerCamelCase=None			,   _lowerCamelCase=False			,   _lowerCamelCase=False			,   _lowerCamelCase=None			,   _lowerCamelCase=False			,   ):
 lowerCamelCase__     :			Dict     				=						[[label] for label in labels]
 lowerCamelCase__     :			Dict     				=						list(a_				)
 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"`.'				)

 lowerCamelCase__     :			Dict     				=						len(labels[0]				)
 if any(len(a_				) != references_per_prediction for refs in labels				):
  raise ValueError('Sacrebleu requires the same number of references for each prediction'				)
 lowerCamelCase__     :			List[Any]     				=						[[refs[i] for refs in labels] for i in range(a_				)]
 lowerCamelCase__     :			Union[str, Any]     				=						scb.corpus_bleu(
     a_			,   a_			,   smooth_method=a_			,   smooth_value=a_			,   force=a_			,   lowercase=a_			,   use_effective_order=a_			,   **({'tokenize': tokenize} if tokenize else {})			,   )
 return {"bleu": output.score}






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):


 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:
  lowerCamelCase__     :			Union[str, Any]     				=						compute_measures(a_			,   a_				)['wer']

  lowerCamelCase__     :			List[str]     				=						compute_measures(a_			,   a_			,   truth_transform=a_			,   hypothesis_transform=a_				)['wer']
  return {"wer": wer, "cer": cer}
 else:

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

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





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







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			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(UpperCAmelCase_					), 'references': datasets.Value(UpperCAmelCase_					)}					),     codebase_urls=[],     reference_urls=[],     format='numpy',     )





      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_=None,     lowerCamelCase_=None					):

       '''simple docstring'''



       lowerCamelCase__     :			List[Any]     				=						bleu_kwargs if bleu_kwargs is not None else {}
       lowerCamelCase__     :			Tuple     				=						wer_kwargs if wer_kwargs is not None else {}

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



"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	RePaintPipeline
	lowercase_        =	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	lowercase_        =	PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	lowercase_        =	IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			lowerCamelCase     :			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')		, )
			lowerCamelCase     :			Union[str, Any]		=    RePaintScheduler()
			lowerCamelCase     :			Dict		=    {'unet': unet, 'scheduler': scheduler}
			return components




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=0					)		->							Optional[int]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			str		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Tuple		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			lowerCamelCase     :			str		=    np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			lowerCamelCase     :			Tuple		=    torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[int]		=    (image > 0).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[Any]		=    {
			    'image': image,
			    'mask_image': mask,
			    'generator': generator,
			    'num_inference_steps': 5,
			    'eta': 0.0,
			    'jump_length': 2,
			    'jump_n_sample': 2,
			    'output_type': 'numpy',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Any		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			Union[str, Any]		=    self.get_dummy_components()
			lowerCamelCase     :			List[Any]		=    RePaintPipeline(**UpperCAmelCase_					)
			lowerCamelCase     :			int		=    sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    self.get_dummy_inputs(UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    sd_pipe(**UpperCAmelCase_					).images
			lowerCamelCase     :			List[Any]		=    image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase     :			Optional[Any]		=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return super().test_save_load_local()




	@unittest.skip('non-deterministic pipeline'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_inference_batch_single_identical()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			return super().test_save_load_optional_components()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class        _lowercase   (				unittest.TestCase		):




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256.png'					)
			lowerCamelCase     :			str		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'					)
			lowerCamelCase     :			Optional[Any]		=    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256_result.npy'					)

			lowerCamelCase     :			Any		=    'google/ddpm-ema-celebahq-256'
			lowerCamelCase     :			List[Any]		=    UNetaDModel.from_pretrained(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    RePaintPipeline(unet=UpperCAmelCase_		, scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)
			lowerCamelCase     :			str		=    repaint(
			    UpperCAmelCase_		, UpperCAmelCase_		, num_inference_steps=250		, eta=0.0		, jump_length=10		, jump_n_sample=10		, generator=UpperCAmelCase_		, output_type='np'		, )
			lowerCamelCase     :			Optional[int]		=    output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2






