


from sklearn.metrics import accuracy_score

import datasets


A				:   List[Any]		= "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


A				:   Union[str, Any]		= "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


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







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


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION ,				citation=_CITATION ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
                     {
                         "predictions": datasets.Sequence(datasets.Value("int32"	)	),
                         "references": datasets.Sequence(datasets.Value("int32"	)	),
                     }
                     if self.config_name == "multilabel"
                     else {
                         "predictions": datasets.Value("int32"	),
                         "references": datasets.Value("int32"	),
                     }	) ,				reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"] ,				)





       def 						lowerCAmelCase					(       self							:      Optional[Any] ,				__lowerCamelCase							:      Optional[int] ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Tuple=True ,				__lowerCamelCase							:      Optional[int]=None	):


             '''simple docstring'''



             return {
                 "accuracy": float(
                     accuracy_score(__lowerCamelCase ,				__lowerCamelCase ,				normalize=__lowerCamelCase ,				sample_weight=__lowerCamelCase	)	)
             }






import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


A				:   Union[str, Any]		= random.Random()

def 				lowercase_		(		_A      :   Any   , _A      :   int=1.0   , _A      :   Optional[int]=None   , _A      :   Union[str, Any]=None						):

      """simple docstring"""




      if rng is None:
            lowerCamelCase__		:    Any   =  global_rng

      lowerCamelCase__		:    Optional[Any]   =  []
      for batch_idx in range(shape[0]						):
            values.append([]						)
            for _ in range(shape[1]						):
                  values[-1].append(rng.random() * scale						)

      return values







@require_torch
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Union[str, Any]=7 ,				__lowerCamelCase							:      int=400 ,				__lowerCamelCase							:      Tuple=2000 ,				__lowerCamelCase							:      int=1 ,				__lowerCamelCase							:      int=0.0 ,				__lowerCamelCase							:      int=24000 ,				__lowerCamelCase							:      Union[str, Any]=True ,				):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  parent
             lowerCamelCase__		:    Union[str, Any]   =  batch_size
             lowerCamelCase__		:    str   =  min_seq_length
             lowerCamelCase__		:    int   =  max_seq_length
             lowerCamelCase__		:    List[Any]   =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             lowerCamelCase__		:    List[str]   =  feature_size
             lowerCamelCase__		:    List[Any]   =  padding_value
             lowerCamelCase__		:    Union[str, Any]   =  sampling_rate
             lowerCamelCase__		:    Tuple   =  return_attention_mask





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
             }





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Tuple=False ,				__lowerCamelCase							:      Optional[int]=False	):


             '''simple docstring'''



             def _flatten(__lowerCamelCase							:      Dict	):
                   return list(itertools.chain(*__lowerCamelCase	)	)

             if equal_length:
                   lowerCamelCase__		:    List[Any]   =  floats_list((self.batch_size, self.max_seq_length)	)
             else:
                   # make sure that inputs increase in size
                   lowerCamelCase__		:    Dict   =  [
                       _flatten(floats_list((x, self.feature_size)	)	)
                       for x in range(self.min_seq_length ,				self.max_seq_length ,				self.seq_length_diff	)
                   ]

             if numpify:
                   lowerCamelCase__		:    Optional[int]   =  [np.asarray(__lowerCamelCase	) for x in audio_inputs]

             return audio_inputs







@require_torch
class       _lowercase			(							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      EncodecFeatureExtractor





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  EnCodecFeatureExtractionTester(self	)





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             # create three inputs of length 800, 1000, and 1200
             lowerCamelCase__		:    Union[str, Any]   =  [floats_list((1, x)	)[0] for x in range(800 ,				1400 ,				200	)]
             lowerCamelCase__		:    Dict   =  [np.asarray(__lowerCamelCase	) for audio_input in audio_inputs]

             # Test not batched input
             lowerCamelCase__		:    Dict   =  feat_extract(audio_inputs[0] ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Optional[int]   =  feat_extract(np_audio_inputs[0] ,				return_tensors="np"	).input_values
             self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)

             # Test batched
             lowerCamelCase__		:    Dict   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Any   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             for enc_seq_a, enc_seq_a in zip(__lowerCamelCase ,				__lowerCamelCase	):
                   self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             lowerCamelCase__		:    Tuple   =  np.random.rand(100	).astype(np.floataa	)
             lowerCamelCase__		:    str   =  np_audio_inputs.tolist()

             for inputs in [py_audio_inputs, np_audio_inputs]:
                   lowerCamelCase__		:    List[Any]   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="np"	)
                   self.assertTrue(np_processed.input_values.dtype == np.floataa	)
                   lowerCamelCase__		:    int   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="pt"	)
                   self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)





       def 						lowerCAmelCase					(       self							:      str ,				__lowerCamelCase							:      Any	):


             '''simple docstring'''



             from datasets import load_dataset

             lowerCamelCase__		:    List[Any]   =  load_dataset("hf-internal-testing/librispeech_asr_dummy" ,				"clean" ,				split="validation"	)
             # automatic decoding with librispeech
             lowerCamelCase__		:    Union[str, Any]   =  ds.sort("id"	).select(range(__lowerCamelCase	)	)[:num_samples]["audio"]

             return [x["array"] for x in audio_samples]





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    str   =  self._load_datasamples(1	)
             lowerCamelCase__		:    Any   =  EncodecFeatureExtractor()
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 1, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    Any   =  self._load_datasamples(1	)
             lowerCamelCase__		:    int   =  [np.tile(input_audio[0][None] ,				reps=(2, 1)	)]
             input_audio[0][1] *= 0.5
             lowerCamelCase__		:    List[str]   =  EncodecFeatureExtractor(feature_size=2	)
             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 2, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)
             self.assertTrue(torch.allclose(input_values[0, 1, :30] ,				EXPECTED_INPUT_VALUES * 0.5 ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self._load_datasamples(2	)
             # would be easier if the stride was like
             lowerCamelCase__		:    Optional[Any]   =  EncodecFeatureExtractor(feature_size=1 ,				chunk_length_s=1 ,				overlap=0.0_1	)

             # pad and trunc raise an error ?
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Both padding and truncation were set. Make sure you only set one.$" ,				):
                   lowerCamelCase__		:    str   =  feature_extractor(
                       __lowerCamelCase ,				padding="max_length" ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values

             # truncate to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 71520)	)  # 2 chunks

             # force truncate to max_length
             lowerCamelCase__		:    List[str]   =  feature_extractor(
                 __lowerCamelCase ,				truncation=__lowerCamelCase ,				max_length=48000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 48000)	)

             # pad to chunk
             lowerCamelCase__		:    int   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(padded_outputs.shape ,				(2, 1, 95280)	)

             # pad to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 95280)	)

             # force pad to max length
             lowerCamelCase__		:    Tuple   =  feature_extractor(
                 __lowerCamelCase ,				padding="max_length" ,				max_length=100000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 100000)	)

             # force no pad
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    str   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no chunk_length_s
             lowerCamelCase__		:    Tuple   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    List[str]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Tuple   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no overlap
             lowerCamelCase__		:    List[Any]   =  2
             lowerCamelCase__		:    List[Any]   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)


