




'''simple docstring'''





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
  from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import SwinaSRImageProcessor




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=13		,     __lowerCAmelCase=32		,     __lowerCAmelCase=1		,     __lowerCAmelCase=3		,     __lowerCAmelCase=16		,     __lowerCAmelCase=[1, 2, 1]		,     __lowerCAmelCase=[2, 2, 4]		,     __lowerCAmelCase=2		,     __lowerCAmelCase=2.0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=False		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-5		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=2		,     ):







      '''simple docstring'''


      _a				:							str							       =      parent
      _a				:							str							       =      batch_size
      _a				:							int							       =      image_size
      _a				:							int							       =      patch_size
      _a				:							Any							       =      num_channels
      _a				:							Optional[Any]							       =      embed_dim
      _a				:							List[Any]							       =      depths
      _a				:							Union[str, Any]							       =      num_heads
      _a				:							List[Any]							       =      window_size
      _a				:							Optional[Any]							       =      mlp_ratio
      _a				:							Dict							       =      qkv_bias
      _a				:							Optional[int]							       =      hidden_dropout_prob
      _a				:							List[Any]							       =      attention_probs_dropout_prob
      _a				:							List[str]							       =      drop_path_rate
      _a				:							str							       =      hidden_act
      _a				:							Optional[int]							       =      use_absolute_embeddings
      _a				:							List[str]							       =      patch_norm
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							Any							       =      is_training
      _a				:							Any							       =      scope
      _a				:							int							       =      use_labels
      _a				:							Union[str, Any]							       =      upscale

      # here we set some attributes to make tests pass
      _a				:							List[str]							       =      len(__lowerCAmelCase )
      _a				:							str							       =      embed_dim
      _a				:							Optional[int]							       =      (image_size // patch_size) ** 2



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      _a				:							Optional[int]							       =      None
      if self.use_labels:
          _a				:							str							       =      ids_tensor([self.batch_size]		,     self.type_sequence_label_size )

      _a				:							str							       =      self.get_config()

      return config, pixel_values, labels



  def     a_							(			self ):







      '''simple docstring'''


      return SwinaSRConfig(
          image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     embed_dim=self.embed_dim		,     depths=self.depths		,     num_heads=self.num_heads		,     window_size=self.window_size		,     mlp_ratio=self.mlp_ratio		,     qkv_bias=self.qkv_bias		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     drop_path_rate=self.drop_path_rate		,     hidden_act=self.hidden_act		,     use_absolute_embeddings=self.use_absolute_embeddings		,     path_norm=self.patch_norm		,     layer_norm_eps=self.layer_norm_eps		,     initializer_range=self.initializer_range		,     upscale=self.upscale		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      SwinaSRModel(config=__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							List[str]							       =      model(__lowerCAmelCase )

      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.embed_dim, self.image_size, self.image_size) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Dict							       =      SwinaSRForImageSuperResolution(__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							Tuple							       =      model(__lowerCAmelCase )

      _a				:							Dict							       =      self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape		,     (self.batch_size, self.num_channels, expected_image_size, expected_image_size) )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.prepare_config_and_inputs()
      _a       , _a       , _a				:							int							       =      config_and_inputs
      _a				:							Optional[int]							       =      {"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class      UpperCamelCase       (	snake_case__      ,		snake_case__      ,		unittest.TestCase       ):
  a     :Dict          =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  a     :List[str]          =					{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

  a     :List[Any]          =					False
  a     :Tuple          =					False
  a     :Any          =					False
  a     :Union[str, Any]          =					False
  a     :Union[str, Any]          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      SwinaSRModelTester(self )
      _a				:							Optional[int]							       =      ConfigTester(self		,     config_class=__lowerCAmelCase		,     embed_dim=37 )



  def     a_							(			self ):







      '''simple docstring'''


      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*__lowerCAmelCase )



  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not use inputs_embeds""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							int							       =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module) )
          _a				:							Optional[int]							       =      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowerCAmelCase		,     nn.Linear ) )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a				:							str							       =      model_class(__lowerCAmelCase )
          _a				:							Tuple							       =      inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _a				:							int							       =      [*signature.parameters.keys()]

          _a				:							List[Any]							       =      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,     __lowerCAmelCase )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a				:							List[str]							       =      SwinaSRModel.from_pretrained(__lowerCAmelCase )
          self.assertIsNotNone(__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							str							       =      self.model_tester.prepare_config_and_inputs_for_common()

      _a				:							Union[str, Any]							       =      _config_zero_init(__lowerCAmelCase )
      for model_class in self.all_model_classes:
          _a				:							Any							       =      model_class(config=__lowerCAmelCase )
          for name, param in model.named_parameters():
              if "logit_scale" in name:
                  continue
              if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Dict							       =      self.model_tester.prepare_config_and_inputs_for_common()
      _a				:							List[str]							       =      True

      for model_class in self.all_model_classes:
          _a				:							Dict							       =      True
          _a				:							Optional[int]							       =      False
          _a				:							Any							       =      True
          _a				:							Dict							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          _a				:							Tuple							       =      len(self.model_tester.depths )
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a				:							List[str]							       =      True
          _a				:							str							       =      config.window_size**2
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Any							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )
          _a				:							Dict							       =      len(__lowerCAmelCase )

          # Check attention is always last and order is fine
          _a				:							Tuple							       =      True
          _a				:							Any							       =      True
          _a				:							str							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )

          self.assertEqual(out_len + 1		,     len(__lowerCAmelCase ) )

          _a				:							List[str]							       =      outputs.attentions

          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )






@require_vision
@require_torch
@slow
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      SwinaSRImageProcessor()
      _a				:							Optional[Any]							       =      SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64""" ).to(__lowerCAmelCase )

      _a				:							Tuple							       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      _a				:							Optional[int]							       =      processor(images=__lowerCAmelCase		,     return_tensors="""pt""" ).to(__lowerCAmelCase )

      # forward pass
      with torch.no_grad():
          _a				:							str							       =      model(**__lowerCAmelCase )

      # verify the logits
      _a				:							List[Any]							       =      torch.Size([1, 3, 976, 1296] )
      self.assertEqual(outputs.reconstruction.shape		,     __lowerCAmelCase )
      _a				:							List[str]							       =      torch.tensor(
          [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]] ).to(__lowerCAmelCase )
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,     __lowerCAmelCase		,     atol=1e-4 ) )










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)
lowerCAmelCase_     =			'\\n'

lowerCAmelCase_     =			'\\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'

lowerCAmelCase_     =			'\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'

lowerCAmelCase_     =			['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
lowerCAmelCase_     =			''

try:
					from jiwer import transforms as tr

					lowerCAmelCase_     =			True
except ImportError:
					lowerCAmelCase_     =			False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
					class   UpperCamelCase			(					tr.AbstractTransform			):







							"""simple docstring"""



							def __init__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							str = " "		)     -> Dict:


											'''simple docstring'''
											A									=   sentence_delimiter
							def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> int:


											'''simple docstring'''
											return list(_SCREAMING_SNAKE_CASE		)







							def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Tuple:


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


					lowerCAmelCase_     =			tr.Compose(
					    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
					)
elif _jiwer_available:
					lowerCAmelCase_     =			tr.Compose(
					    [
					        tr.RemoveMultipleSpaces(),
					        tr.Strip(),
					        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
					        tr.ReduceToListOfListOfChars(),
					    ]
					)
else:
					lowerCAmelCase_     =			None







def   snake_case		(				UpperCAmelCase						:							List[str],    UpperCAmelCase						:							Tuple ):
				return float((preds == labels).mean() )







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							Tuple ):
				return {
				    "f1": float(fa_score(y_true=UpperCAmelCase,    y_pred=UpperCAmelCase,    average='macro' ) ),
				    "accuracy": simple_accuracy(UpperCAmelCase,    UpperCAmelCase ),
				}







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[str],    UpperCAmelCase						:							List[Any]="exp",    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							int=False,    UpperCAmelCase						:							Dict=False,    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							str=False,    ):
				# xtreme-s can only have one label
				A									=   [[label] for label in labels]
				A									=   list(UpperCAmelCase )
				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									=   len(labels[0] )
				if any(len(UpperCAmelCase ) != references_per_prediction for refs in labels ):
								raise ValueError('Sacrebleu requires the same number of references for each prediction' )
				A									=   [[refs[i] for refs in labels] for i in range(UpperCAmelCase )]
				A									=   scb.corpus_bleu(
				    UpperCAmelCase,    UpperCAmelCase,    smooth_method=UpperCAmelCase,    smooth_value=UpperCAmelCase,    force=UpperCAmelCase,    lowercase=UpperCAmelCase,    use_effective_order=UpperCAmelCase,    **({'tokenize': tokenize} if tokenize else {}),    )
				return {"bleu": output.score}







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							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:
								A									=   compute_measures(UpperCAmelCase,    UpperCAmelCase )['wer']

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

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

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



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







		"""simple docstring"""



		def    A(	self		:							Any		)     -> str:


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

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







		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Dict=None    ,_SCREAMING_SNAKE_CASE		:							List[Any]=None		)     -> Tuple:


						'''simple docstring'''
						A									=   bleu_kwargs if bleu_kwargs is not None else {}
						A									=   wer_kwargs if wer_kwargs is not None else {}

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





