






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       =    random.Random()


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





						"""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
class __lowerCamelCase					( unittest.TestCase):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase=7			,       UpperCAmelCase=400			,       UpperCAmelCase=2000			,       UpperCAmelCase=1			,       UpperCAmelCase=0.0			,       UpperCAmelCase=2_4000			,       UpperCAmelCase=True			,       ):
							"""simple docstring"""







							_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 			UpperCamelCase       (				self       ):
							"""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 			UpperCamelCase       (				self			,       UpperCAmelCase=False			,       UpperCAmelCase=False       ):
							"""simple docstring"""







							def _flatten(UpperCAmelCase       ):
													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 __lowerCamelCase					( __UpperCAmelCase	,			unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								= EncodecFeatureExtractor

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







							_UpperCAmelCase							   =     EnCodecFeatureExtractionTester(self       )

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







							_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			,       1400			,       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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_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 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







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







							_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, 9_3680)       )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]			,       lowerCamelCase__			,       atol=1e-6       )       )

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







							_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, 9_3680)       )
							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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self._load_datasamples(2       )
							# would be easier if the stride was like
							_UpperCAmelCase							   =     EncodecFeatureExtractor(feature_size=1			,       chunk_length_s=1			,       overlap=0.01       )

							# 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, 7_1520)       )  # 2 chunks

							# force truncate to max_length
							_UpperCAmelCase							   =     feature_extractor(
							    lowerCamelCase__			,       truncation=lowerCamelCase__			,       max_length=4_8000			,       return_tensors='pt'       ).input_values
							self.assertEquals(truncated_outputs.shape			,       (2, 1, 4_8000)       )

							# pad to chunk
							_UpperCAmelCase							   =     feature_extractor(lowerCamelCase__			,       padding=lowerCamelCase__			,       return_tensors='pt'       ).input_values
							self.assertEquals(padded_outputs.shape			,       (2, 1, 9_5280)       )

							# pad to chunk
							_UpperCAmelCase							   =     feature_extractor(lowerCamelCase__			,       return_tensors='pt'       ).input_values
							self.assertEquals(truncated_outputs.shape			,       (2, 1, 9_5280)       )

							# force pad to max length
							_UpperCAmelCase							   =     feature_extractor(
							    lowerCamelCase__			,       padding='max_length'			,       max_length=10_0000			,       return_tensors='pt'       ).input_values
							self.assertEquals(truncated_outputs.shape			,       (2, 1, 10_0000)       )

							# 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, 9_3680)       )

							# 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, 9_3680)       )

							# 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, 9_3680)       )

"""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))



