




'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
      from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

      from .test_feature_extraction_speechta import floats_list


lowercase			:      Optional[int]			=					get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")







@require_torch
class 				_a   (unittest.TestCase  ):





     '''simple docstring'''



     def  snake_case_			(							self       )			->							str:
          snake_case :			Optional[int]					   = tempfile.mkdtemp()

          snake_case :			Tuple					   = SpeechTaTokenizer(__a       )
          tokenizer.save_pretrained(self.tmpdirname       )

          snake_case :			List[Any]					   = {
              """feature_size""": 1,
              """padding_value""": 0.0,
              """sampling_rate""": 16_000,
              """do_normalize""": False,
              """num_mel_bins""": 80,
              """hop_length""": 16,
              """win_length""": 64,
              """win_function""": """hann_window""",
              """fmin""": 80,
              """fmax""": 7_600,
              """mel_floor""": 1E-10,
              """reduction_factor""": 2,
              """return_attention_mask""": True,
          }

          snake_case :			Optional[int]					   = os.path.join(self.tmpdirname       ,__a       )
          with open(self.feature_extraction_file       ,"""w"""       ,encoding="""utf-8"""       ) as fp:
               fp.write(json.dumps(__a       ) + """\n"""       )







     def  snake_case_			(							self       ,**__a       )			->							str:
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,**__a       )







     def  snake_case_			(							self       ,**__a       )			->							Optional[int]:
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,**__a       )







     def  snake_case_			(							self       )			->							Optional[Any]:
          shutil.rmtree(self.tmpdirname       )







     def  snake_case_			(							self       )			->							List[Any]:
          snake_case :			Tuple					   = self.get_tokenizer()
          snake_case :			str					   = self.get_feature_extractor()

          snake_case :			Dict					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          processor.save_pretrained(self.tmpdirname       )
          snake_case :			Union[str, Any]					   = SpeechTaProcessor.from_pretrained(self.tmpdirname       )

          self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer.get_vocab()       )
          self.assertIsInstance(processor.tokenizer       ,__a       )

          self.assertEqual(processor.feature_extractor.to_json_string()       ,feature_extractor.to_json_string()       )
          self.assertIsInstance(processor.feature_extractor       ,__a       )







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			int					   = SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()       )
          processor.save_pretrained(self.tmpdirname       )

          snake_case :			List[Any]					   = self.get_tokenizer(bos_token="""(BOS)"""       ,eos_token="""(EOS)"""       )
          snake_case :			Tuple					   = self.get_feature_extractor(do_normalize=__a       ,padding_value=1.0       )

          snake_case :			Tuple					   = SpeechTaProcessor.from_pretrained(
              self.tmpdirname       ,bos_token="""(BOS)"""       ,eos_token="""(EOS)"""       ,do_normalize=__a       ,padding_value=1.0       )

          self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()       )
          self.assertIsInstance(processor.tokenizer       ,__a       )

          self.assertEqual(processor.feature_extractor.to_json_string()       ,feature_extractor_add_kwargs.to_json_string()       )
          self.assertIsInstance(processor.feature_extractor       ,__a       )







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			Optional[int]					   = self.get_feature_extractor()
          snake_case :			int					   = self.get_tokenizer()

          snake_case :			int					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			Dict					   = floats_list((3, 1_000)       )

          snake_case :			Optional[Any]					   = feature_extractor(audio=__a       ,return_tensors="""np"""       )
          snake_case :			int					   = processor(audio=__a       ,return_tensors="""np"""       )

          for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1E-2       )







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			Any					   = self.get_feature_extractor()
          snake_case :			Dict					   = self.get_tokenizer()

          snake_case :			List[str]					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			str					   = floats_list((3, 1_000)       )

          snake_case :			Tuple					   = feature_extractor(audio_target=__a       ,return_tensors="""np"""       )
          snake_case :			int					   = processor(audio_target=__a       ,return_tensors="""np"""       )

          for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1E-2       )







     def  snake_case_			(							self       )			->							Dict:
          snake_case :			Tuple					   = self.get_feature_extractor()
          snake_case :			str					   = self.get_tokenizer()

          snake_case :			Dict					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			Union[str, Any]					   = """This is a test string"""

          snake_case :			Optional[Any]					   = processor(text=__a       )
          snake_case :			Union[str, Any]					   = tokenizer(__a       )

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]       )







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Dict					   = self.get_feature_extractor()
          snake_case :			Any					   = self.get_tokenizer()

          snake_case :			str					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			Tuple					   = """This is a test string"""

          snake_case :			Union[str, Any]					   = processor(text_target=__a       )
          snake_case :			int					   = tokenizer(__a       )

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]       )







     def  snake_case_			(							self       )			->							Optional[int]:
          snake_case :			Dict					   = self.get_feature_extractor()
          snake_case :			Optional[int]					   = self.get_tokenizer()

          snake_case :			Optional[int]					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			List[Any]					   = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case :			str					   = processor.batch_decode(__a       )
          snake_case :			List[Any]					   = tokenizer.batch_decode(__a       )

          self.assertListEqual(__a       ,__a       )







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Dict					   = self.get_feature_extractor()
          snake_case :			Any					   = self.get_tokenizer()

          snake_case :			Optional[Any]					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          self.assertListEqual(
              processor.model_input_names       ,feature_extractor.model_input_names       ,msg="""`processor` and `feature_extractor` model input names do not match"""       ,)









'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


lowercase			:      int			=					"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

lowercase			:      Optional[Any]			=					"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowercase			:      List[Any]			=					"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""







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





     '''simple docstring'''



     def  snake_case_			(							self       )			->							int:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(self._get_feature_types()       )       ,reference_urls=[
                  """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
              ]       ,)







     def  snake_case_			(							self       )			->							Any:
          if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("""float"""       )       ),
                   "references": datasets.Sequence(datasets.Value("""float"""       )       ),
               }
          else:
               return {
                   "predictions": datasets.Value("""float"""       ),
                   "references": datasets.Value("""float"""       ),
               }







     def  snake_case_			(							self       ,__a       ,__a       ,__a=None       ,__a="uniform_average"       )			->							str:
          snake_case :			int					   = mean_absolute_error(__a       ,__a       ,sample_weight=__a       ,multioutput=__a       )

          return {"mae": mae_score}



