






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						= random.Random()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase=1.0    ,	UpperCAmelCase=None    ,	UpperCAmelCase=None     )     ->		List[str]:







							"""simple docstring"""





							if rng is None:
														_a  :    Optional[Any]			=						global_rng

							_a  :    Dict			=						[]
							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      UpperCamelCase_							(				unittest.TestCase       ):




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




						def      snake_case__(    self       )			->						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      snake_case__(    self		,	lowercase=False		,	lowercase=False       )			->						Any:
													def _flatten(lowercase       ):
																				return list(itertools.chain(*lowercase       )       )

													if equal_length:
																				_a  :    Dict			=						floats_list((self.batch_size, self.max_seq_length)       )
													else:
																				# make sure that inputs increase in size
																				_a  :    Union[str, Any]			=						[
																				    _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:
																				_a  :    List[Any]			=						[np.asarray(lowercase       ) for x in audio_inputs]

													return audio_inputs

@require_torch
class      UpperCamelCase_							(				UpperCamelCase ,       unittest.TestCase       ):
						lowercase									=  EncodecFeatureExtractor




						def      snake_case__(    self       )			->						List[str]:
													_a  :    Optional[int]			=						EnCodecFeatureExtractionTester(self       )




						def      snake_case__(    self       )			->						str:
													# Tests that all call wrap to encode_plus and batch_encode_plus
													_a  :    Dict			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
													# create three inputs of length 800, 1000, and 1200
													_a  :    Dict			=						[floats_list((1, x)       )[0] for x in range(800		,	1_400		,	200       )]
													_a  :    int			=						[np.asarray(lowercase       ) for audio_input in audio_inputs]

													# Test not batched input
													_a  :    Union[str, Any]			=						feat_extract(audio_inputs[0]		,	return_tensors='''np'''       ).input_values
													_a  :    Any			=						feat_extract(np_audio_inputs[0]		,	return_tensors='''np'''       ).input_values
													self.assertTrue(np.allclose(lowercase		,	lowercase		,	atol=1e-3       )       )

													# Test batched
													_a  :    List[str]			=						feat_extract(lowercase		,	padding=lowercase		,	return_tensors='''np'''       ).input_values
													_a  :    Any			=						feat_extract(lowercase		,	padding=lowercase		,	return_tensors='''np'''       ).input_values
													for enc_seq_a, enc_seq_a in zip(lowercase		,	lowercase       ):
																				self.assertTrue(np.allclose(lowercase		,	lowercase		,	atol=1e-3       )       )




						def      snake_case__(    self       )			->						List[Any]:
													_a  :    List[Any]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
													_a  :    Union[str, Any]			=						np.random.rand(100       ).astype(np.floataa       )
													_a  :    Optional[Any]			=						np_audio_inputs.tolist()

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




						def      snake_case__(    self		,	lowercase       )			->						List[Any]:
													from datasets import load_dataset

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

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




						def      snake_case__(    self       )			->						List[Any]:
													# fmt: off
													_a  :    List[str]			=						torch.tensor(
													    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
													     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
													     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
													     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
													     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
													     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]       )
													# fmt: on
													_a  :    Optional[Any]			=						self._load_datasamples(1       )
													_a  :    Dict			=						EncodecFeatureExtractor()
													_a  :    Union[str, Any]			=						feature_extractor(lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(input_values.shape		,	(1, 1, 93_680)       )
													self.assertTrue(torch.allclose(input_values[0, 0, :30]		,	lowercase		,	atol=1e-6       )       )




						def      snake_case__(    self       )			->						Any:
													# fmt: off
													_a  :    List[str]			=						torch.tensor(
													    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
													     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
													     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
													     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
													     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
													     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]       )
													# fmt: on
													_a  :    List[str]			=						self._load_datasamples(1       )
													_a  :    List[Any]			=						[np.tile(input_audio[0][None]		,	reps=(2, 1)       )]
													input_audio[0][1] *= 0.5
													_a  :    Dict			=						EncodecFeatureExtractor(feature_size=2       )
													_a  :    str			=						feature_extractor(lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(input_values.shape		,	(1, 2, 93_680)       )
													self.assertTrue(torch.allclose(input_values[0, 0, :30]		,	lowercase		,	atol=1e-6       )       )
													self.assertTrue(torch.allclose(input_values[0, 1, :30]		,	EXPECTED_INPUT_VALUES * 0.5		,	atol=1e-6       )       )







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Optional[Any]			=						self._load_datasamples(2       )
													# would be easier if the stride was like
													_a  :    List[str]			=						EncodecFeatureExtractor(feature_size=1		,	chunk_length_s=1		,	overlap=0.01       )

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

													# truncate to chunk
													_a  :    List[str]			=						feature_extractor(lowercase		,	truncation=lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(truncated_outputs.shape		,	(2, 1, 71_520)       )  # 2 chunks

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

													# pad to chunk
													_a  :    List[str]			=						feature_extractor(lowercase		,	padding=lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(padded_outputs.shape		,	(2, 1, 95_280)       )

													# pad to chunk
													_a  :    Dict			=						feature_extractor(lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(truncated_outputs.shape		,	(2, 1, 95_280)       )

													# force pad to max length
													_a  :    Optional[Any]			=						feature_extractor(
													    lowercase		,	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(
													    lowercase		,	'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''		,	):
																				_a  :    str			=						feature_extractor(lowercase		,	padding=lowercase		,	return_tensors='''pt'''       ).input_values

													_a  :    Tuple			=						feature_extractor(input_audio[0]		,	padding=lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(truncated_outputs.shape		,	(1, 1, 93_680)       )

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

													_a  :    Union[str, Any]			=						feature_extractor(input_audio[0]		,	padding=lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(truncated_outputs.shape		,	(1, 1, 93_680)       )

													# no pad if no overlap
													_a  :    Optional[Any]			=						2
													_a  :    Tuple			=						None
													with self.assertRaisesRegex(
													    lowercase		,	'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''		,	):
																				_a  :    int			=						feature_extractor(lowercase		,	padding=lowercase		,	return_tensors='''pt'''       ).input_values

													_a  :    List[Any]			=						feature_extractor(input_audio[0]		,	padding=lowercase		,	return_tensors='''pt'''       ).input_values
													self.assertEquals(truncated_outputs.shape		,	(1, 1, 93_680)       )







def    UpperCamelCase__  (   UpperCAmelCase     )     ->		tuple[int, int]:







							"""simple docstring"""





							if len(UpperCAmelCase     ) == 1:
														return a[0], a[0]
							else:
														# split A into half.
														_a  :    Union[str, Any]			=						a[: len(UpperCAmelCase     ) // 2]
														_a  :    Optional[Any]			=						a[len(UpperCAmelCase     ) // 2 :]

														# 2 sub problems, 1/2 of original size.
														_a		,					_a  :    Union[str, Any]			=						max_difference(UpperCAmelCase     )
														_a		,					_a  :    List[Any]			=						max_difference(UpperCAmelCase     )

														# get min of first and max of second
														# linear time
														_a  :    Union[str, Any]			=						min(UpperCAmelCase     )
														_a  :    Any			=						max(UpperCAmelCase     )

														# 3 cases, either (small1, big1),
														# (min_first, max_second), (small2, big2)
														# constant comparisons
														if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																					return smalla, biga
														elif biga - smalla > max_second - min_first:
																					return smalla, biga
														else:
																					return min_first, max_second


if __name__ == "__main__":
						import doctest

						doctest.testmod()