"""simple docstring"""






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


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



def     a_		(   lowerCamelCase   ,     lowerCamelCase=1.0   ,     lowerCamelCase=None   ,     lowerCamelCase=None			):
				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
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def __init__(							self  :      List[str]       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      List[str]=7       ,lowerCamelCase__  :      List[Any]=400       ,lowerCamelCase__  :      str=2_000       ,lowerCamelCase__  :      List[Any]=1       ,lowerCamelCase__  :      Dict=0.0       ,lowerCamelCase__  :      Tuple=24_000       ,lowerCamelCase__  :      Optional[Any]=True       ,):
									UpperCAmelCase__			 =				parent
									UpperCAmelCase__			 =				batch_size
									UpperCAmelCase__			 =				min_seq_length
									UpperCAmelCase__			 =				max_seq_length
									UpperCAmelCase__			 =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									UpperCAmelCase__			 =				feature_size
									UpperCAmelCase__			 =				padding_value
									UpperCAmelCase__			 =				sampling_rate
									UpperCAmelCase__			 =				return_attention_mask







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									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  :      Dict       ,lowerCamelCase__  :      Any=False       ,lowerCamelCase__  :      Optional[Any]=False     ):
									def _flatten(lowerCamelCase__  :      int     ):
													return list(itertools.chain(*lowerCamelCase__     )     )

									if equal_length:
													UpperCAmelCase__			 =				floats_list((self.batch_size, self.max_seq_length)     )
									else:
													# make sure that inputs increase in size
													UpperCAmelCase__			 =				[
													    _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:
													UpperCAmelCase__			 =				[np.asarray(lowerCamelCase__     ) for x in audio_inputs]

									return audio_inputs






@require_torch
class 							snake_case			(					__UpperCAmelCase ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					EncodecFeatureExtractor







					def 						__lowerCAmelCase		(							self  :      str     ):
									UpperCAmelCase__			 =				EnCodecFeatureExtractionTester(self     )







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									UpperCAmelCase__			 =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									# create three inputs of length 800, 1000, and 1200
									UpperCAmelCase__			 =				[floats_list((1, x)     )[0] for x in range(800       ,1_400       ,200     )]
									UpperCAmelCase__			 =				[np.asarray(lowerCamelCase__     ) for audio_input in audio_inputs]

									# Test not batched input
									UpperCAmelCase__			 =				feat_extract(audio_inputs[0]       ,return_tensors='np'     ).input_values
									UpperCAmelCase__			 =				feat_extract(np_audio_inputs[0]       ,return_tensors='np'     ).input_values
									self.assertTrue(np.allclose(lowerCamelCase__       ,lowerCamelCase__       ,atol=1e-3     )     )

									# Test batched
									UpperCAmelCase__			 =				feat_extract(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors='np'     ).input_values
									UpperCAmelCase__			 =				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  :      Dict     ):
									UpperCAmelCase__			 =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									UpperCAmelCase__			 =				np.random.rand(100     ).astype(np.floataa     )
									UpperCAmelCase__			 =				np_audio_inputs.tolist()

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







					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      List[str]     ):
									from datasets import load_dataset

									UpperCAmelCase__			 =				load_dataset('hf-internal-testing/librispeech_asr_dummy'       ,'clean'       ,split='validation'     )
									# automatic decoding with librispeech
									UpperCAmelCase__			 =				ds.sort('id'     ).select(range(lowerCamelCase__     )     )[:num_samples]['audio']

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







					def 						__lowerCAmelCase		(							self  :      str     ):
									# fmt: off
									UpperCAmelCase__			 =				torch.tensor(
									    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
									     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
									     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
									     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
									     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
									     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]     )
									# fmt: on
									UpperCAmelCase__			 =				self._load_datasamples(1     )
									UpperCAmelCase__			 =				EncodecFeatureExtractor()
									UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(input_values.shape       ,(1, 1, 93_680)     )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,lowerCamelCase__       ,atol=1e-6     )     )







					def 						__lowerCAmelCase		(							self  :      int     ):
									# fmt: off
									UpperCAmelCase__			 =				torch.tensor(
									    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
									     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
									     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
									     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
									     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
									     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]     )
									# fmt: on
									UpperCAmelCase__			 =				self._load_datasamples(1     )
									UpperCAmelCase__			 =				[np.tile(input_audio[0][None]       ,reps=(2, 1)     )]
									input_audio[0][1] *= 0.5
									UpperCAmelCase__			 =				EncodecFeatureExtractor(feature_size=2     )
									UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(input_values.shape       ,(1, 2, 93_680)     )
									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  :      int     ):
									UpperCAmelCase__			 =				self._load_datasamples(2     )
									# would be easier if the stride was like
									UpperCAmelCase__			 =				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.$'       ,):
													UpperCAmelCase__			 =				feature_extractor(
													    lowerCamelCase__       ,padding='max_length'       ,truncation=lowerCamelCase__       ,return_tensors='pt'     ).input_values

									# truncate to chunk
									UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,truncation=lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(2, 1, 71_520)     )  # 2 chunks

									# force truncate to max_length
									UpperCAmelCase__			 =				feature_extractor(
									    lowerCamelCase__       ,truncation=lowerCamelCase__       ,max_length=48_000       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(2, 1, 48_000)     )

									# pad to chunk
									UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(padded_outputs.shape       ,(2, 1, 95_280)     )

									# pad to chunk
									UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(2, 1, 95_280)     )

									# force pad to max length
									UpperCAmelCase__			 =				feature_extractor(
									    lowerCamelCase__       ,padding='max_length'       ,max_length=100_000       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(2, 1, 100_000)     )

									# 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.$'       ,):
													UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values

									UpperCAmelCase__			 =				feature_extractor(input_audio[0]       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(1, 1, 93_680)     )

									# no pad if no chunk_length_s
									UpperCAmelCase__			 =				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.$'       ,):
													UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values

									UpperCAmelCase__			 =				feature_extractor(input_audio[0]       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(1, 1, 93_680)     )

									# no pad if no overlap
									UpperCAmelCase__			 =				2
									UpperCAmelCase__			 =				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.$'       ,):
													UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values

									UpperCAmelCase__			 =				feature_extractor(input_audio[0]       ,padding=lowerCamelCase__       ,return_tensors='pt'     ).input_values
									self.assertEquals(truncated_outputs.shape       ,(1, 1, 93_680)     )




"""simple docstring"""






def     a_		(   lowerCamelCase			):
				if num < 0:
								return -1
				return 1 if num else 0



def     a_		(   ):
				assert signum(5			) == 1
				assert signum(-5			) == -1
				assert signum(0			) == 0


if __name__ == "__main__":
						print(signum(12))
						print(signum(-12))
						print(signum(0))



