


"""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


__SCREAMING_SNAKE_CASE				:     Optional[int]		=     random.Random()







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE=1.0					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					)     ->					int:
							if rng is None:
														snake_case_     							=		global_rng

							snake_case_     							=		[]
							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   __A  (unittest.TestCase):





							'''simple docstring'''

							def __init__(					self				:   List[Any]		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   List[Any]=7		,							UpperCAmelCase_				:   int=400		,							UpperCAmelCase_				:   Dict=2_000		,							UpperCAmelCase_				:   str=1		,							UpperCAmelCase_				:   Optional[Any]=0.0		,							UpperCAmelCase_				:   List[Any]=24_000		,							UpperCAmelCase_				:   List[str]=True		,							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		parent
														snake_case_     							=		batch_size
														snake_case_     							=		min_seq_length
														snake_case_     							=		max_seq_length
														snake_case_     							=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														snake_case_     							=		feature_size
														snake_case_     							=		padding_value
														snake_case_     							=		sampling_rate
														snake_case_     							=		return_attention_mask



							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Optional[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[Any]		,							UpperCAmelCase_				:   Optional[int]=False		,							UpperCAmelCase_				:   List[Any]=False							)       ->Dict:



														"""simple docstring"""



														def _flatten(UpperCAmelCase_				:   List[str]							):
																					return list(itertools.chain(*UpperCAmelCase_							)							)

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

														return audio_inputs





@require_torch
class   __A  (snake_case__    ,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Any				   =     EncodecFeatureExtractor



							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		EnCodecFeatureExtractionTester(self							)



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
														# create three inputs of length 800, 1000, and 1200
														snake_case_     							=		[floats_list((1, x)							)[0] for x in range(800		,							1_400		,							200							)]
														snake_case_     							=		[np.asarray(UpperCAmelCase_							) for audio_input in audio_inputs]

														# Test not batched input
														snake_case_     							=		feat_extract(audio_inputs[0]		,							return_tensors="""np"""							).input_values
														snake_case_     							=		feat_extract(np_audio_inputs[0]		,							return_tensors="""np"""							).input_values
														self.assertTrue(np.allclose(UpperCAmelCase_		,							UpperCAmelCase_		,							atol=1E-3							)							)

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



							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
														snake_case_     							=		np.random.rand(100							).astype(np.floataa							)
														snake_case_     							=		np_audio_inputs.tolist()

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



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   Optional[Any]							)       ->Dict:



														"""simple docstring"""



														from datasets import load_dataset

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

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



							def 		lowerCAmelCase							(					self				:   int							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		torch.tensor(
														    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
														     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
														     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
														     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
														     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
														     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]							)
														# fmt: on
														snake_case_     							=		self._load_datasamples(1							)
														snake_case_     							=		EncodecFeatureExtractor()
														snake_case_     							=		feature_extractor(UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(input_values.shape		,							(1, 1, 93_680)							)
														self.assertTrue(torch.allclose(input_values[0, 0, :30]		,							UpperCAmelCase_		,							atol=1E-6							)							)



							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		torch.tensor(
														    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
														     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
														     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
														     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
														     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
														     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]							)
														# fmt: on
														snake_case_     							=		self._load_datasamples(1							)
														snake_case_     							=		[np.tile(input_audio[0][None]		,							reps=(2, 1)							)]
														input_audio[0][1] *= 0.5
														snake_case_     							=		EncodecFeatureExtractor(feature_size=2							)
														snake_case_     							=		feature_extractor(UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(input_values.shape		,							(1, 2, 93_680)							)
														self.assertTrue(torch.allclose(input_values[0, 0, :30]		,							UpperCAmelCase_		,							atol=1E-6							)							)
														self.assertTrue(torch.allclose(input_values[0, 1, :30]		,							EXPECTED_INPUT_VALUES * 0.5		,							atol=1E-6							)							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Optional[Any]:



														"""simple docstring"""



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

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

														# truncate to chunk
														snake_case_     							=		feature_extractor(UpperCAmelCase_		,							truncation=UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(truncated_outputs.shape		,							(2, 1, 71_520)							)  # 2 chunks

														# force truncate to max_length
														snake_case_     							=		feature_extractor(
														    UpperCAmelCase_		,							truncation=UpperCAmelCase_		,							max_length=48_000		,							return_tensors="""pt"""							).input_values
														self.assertEquals(truncated_outputs.shape		,							(2, 1, 48_000)							)

														# pad to chunk
														snake_case_     							=		feature_extractor(UpperCAmelCase_		,							padding=UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(padded_outputs.shape		,							(2, 1, 95_280)							)

														# pad to chunk
														snake_case_     							=		feature_extractor(UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(truncated_outputs.shape		,							(2, 1, 95_280)							)

														# force pad to max length
														snake_case_     							=		feature_extractor(
														    UpperCAmelCase_		,							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(
														    UpperCAmelCase_		,							"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,							):
																					snake_case_     							=		feature_extractor(UpperCAmelCase_		,							padding=UpperCAmelCase_		,							return_tensors="""pt"""							).input_values

														snake_case_     							=		feature_extractor(input_audio[0]		,							padding=UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(truncated_outputs.shape		,							(1, 1, 93_680)							)

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

														snake_case_     							=		feature_extractor(input_audio[0]		,							padding=UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(truncated_outputs.shape		,							(1, 1, 93_680)							)

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

														snake_case_     							=		feature_extractor(input_audio[0]		,							padding=UpperCAmelCase_		,							return_tensors="""pt"""							).input_values
														self.assertEquals(truncated_outputs.shape		,							(1, 1, 93_680)							)







"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



