






import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
		pass

if is_speech_available():
		from transformers import EncodecFeatureExtractor, MusicgenProcessor


_a       =    random.Random()


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase=1.0					,						__lowerCAmelCase=None					,						__lowerCAmelCase=None      )->    Dict:





						"""simple docstring"""



						if rng is None:
												_UpperCAmelCase							   =     global_rng

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

						return values





@require_torch
@require_sentencepiece
class __lowerCamelCase					( unittest.TestCase):





	"""simple docstring"""






	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     'facebook/musicgen-small'
							_UpperCAmelCase							   =     tempfile.mkdtemp()

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return TaTokenizer.from_pretrained(self.checkpoint			,       **lowercase_       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **lowercase_       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()
							_UpperCAmelCase							   =     self.get_feature_extractor()

							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=lowercase_			,       feature_extractor=lowercase_       )

							processor.save_pretrained(self.tmpdirname       )
							_UpperCAmelCase							   =     MusicgenProcessor.from_pretrained(self.tmpdirname       )

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

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()       )
							processor.save_pretrained(self.tmpdirname       )

							_UpperCAmelCase							   =     self.get_tokenizer(bos_token='(BOS)'			,       eos_token='(EOS)'       )
							_UpperCAmelCase							   =     self.get_feature_extractor(do_normalize=lowercase_			,       padding_value=1.0       )

							_UpperCAmelCase							   =     MusicgenProcessor.from_pretrained(
							    self.tmpdirname			,       bos_token='(BOS)'			,       eos_token='(EOS)'			,       do_normalize=lowercase_			,       padding_value=1.0       )

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

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_feature_extractor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=lowercase_			,       feature_extractor=lowercase_       )

							_UpperCAmelCase							   =     floats_list((3, 1000)       )

							_UpperCAmelCase							   =     feature_extractor(lowercase_			,       return_tensors='np'       )
							_UpperCAmelCase							   =     processor(lowercase_			,       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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_feature_extractor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=lowercase_			,       feature_extractor=lowercase_       )

							_UpperCAmelCase							   =     'This is a test string'

							_UpperCAmelCase							   =     processor(text=lowercase_       )

							_UpperCAmelCase							   =     tokenizer(lowercase_       )

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_feature_extractor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=lowercase_			,       feature_extractor=lowercase_       )

							_UpperCAmelCase							   =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							_UpperCAmelCase							   =     processor.batch_decode(sequences=lowercase_       )
							_UpperCAmelCase							   =     tokenizer.batch_decode(lowercase_       )

							self.assertListEqual(lowercase_			,       lowercase_       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_feature_extractor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=lowercase_			,       feature_extractor=lowercase_       )

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_feature_extractor(padding_side='left'       )
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     MusicgenProcessor(tokenizer=lowercase_			,       feature_extractor=lowercase_       )

							_UpperCAmelCase							   =     [floats_list((1, x)       )[0] for x in range(5			,       20			,       5       )]
							_UpperCAmelCase							   =     processor(lowercase_       ).padding_mask

							_UpperCAmelCase							   =     np.asarray(floats_list((3, 20)       )       )[:, None, :]
							_UpperCAmelCase							   =     processor.batch_decode(lowercase_			,       padding_mask=lowercase_       )

							self.assertIsInstance(lowercase_			,       lowercase_       )

							for audio in decoded_audios:
													self.assertIsInstance(lowercase_			,       np.ndarray       )

							self.assertTrue(decoded_audios[0].shape == (1, 10)       )
							self.assertTrue(decoded_audios[1].shape == (1, 15)       )
							self.assertTrue(decoded_audios[2].shape == (1, 20)       )



'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\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"


lowercase__            =     "\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"


lowercase__            =     "\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 			A_					(							datasets.Metric				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      str:
      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   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




