


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)	)






from sklearn.metrics import roc_auc_score

import datasets


A				:   Optional[Any]		= "\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n"

A				:   str		= "\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:\n    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.\n    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results['roc_auc'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')\n        >>> print(round(results['roc_auc'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results['roc_auc']])\n        [0.83, 0.38, 0.94]\n"

A				:   Optional[Any]		= "\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={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.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n"







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


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Optional[int]="macro" ,				__lowerCamelCase							:      Tuple=None ,				__lowerCamelCase							:      Any=None ,				__lowerCamelCase							:      Tuple="raise" ,				__lowerCamelCase							:      Tuple=None ,				):


             '''simple docstring'''



             return {
                 "roc_auc": roc_auc_score(
                     __lowerCamelCase ,				__lowerCamelCase ,				average=__lowerCamelCase ,				sample_weight=__lowerCamelCase ,				max_fpr=__lowerCamelCase ,				multi_class=__lowerCamelCase ,				labels=__lowerCamelCase ,				)
             }


