




'''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


_snake_case      :      Any   							=		random.Random()
def 		snake_case_    (UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Optional[int]=1.0 ,						UpperCamelCase       :  Union[str, Any]=None ,						UpperCamelCase       :  List[str]=None      ):





							'''simple docstring'''


							if rng is None:
														_a	     =				global_rng

							_a	     =				[]
							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    ):







							def __init__(  self       :  Tuple							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  int=7							,			lowerCAmelCase_       :  Optional[int]=4_00							,			lowerCAmelCase_       :  Optional[Any]=20_00							,			lowerCAmelCase_       :  Union[str, Any]=1							,			lowerCAmelCase_       :  List[Any]=0.0							,			lowerCAmelCase_       :  int=2_40_00							,			lowerCAmelCase_       :  str=True							,			)			->							int:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Dict					)			->							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       :  Tuple							,			lowerCAmelCase_       :  Dict=False							,			lowerCAmelCase_       :  Union[str, Any]=False					)			->							Dict:


														"""simple docstring"""


														def _flatten(lowerCAmelCase_       :  int					):
																					return list(itertools.chain(*lowerCAmelCase_					)					)

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

														return audio_inputs



@require_torch
class 			A    (		_a     ,unittest.TestCase    ):
							lowercase_							=				EncodecFeatureExtractor







							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Tuple:


														"""simple docstring"""


														_a	     =				EnCodecFeatureExtractionTester(self					)







							def    __lowerCAmelCase			(  self       :  Any					)			->							Dict:


														"""simple docstring"""


														_a	     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														# create three inputs of length 800, 1000, and 1200
														_a	     =				[floats_list((1, x)					)[0] for x in range(8_00							,			14_00							,			2_00					)]
														_a	     =				[np.asarray(lowerCAmelCase_					) for audio_input in audio_inputs]

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

														# Test batched
														_a	     =				feat_extract(lowerCAmelCase_							,			padding=lowerCAmelCase_							,			return_tensors='''np'''					).input_values
														_a	     =				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       :  Optional[int]					)			->							Dict:


														"""simple docstring"""


														_a	     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														_a	     =				np.random.rand(1_00					).astype(np.floataa					)
														_a	     =				np_audio_inputs.tolist()

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







							def    __lowerCAmelCase			(  self       :  str							,			lowerCAmelCase_       :  Tuple					)			->							List[Any]:


														"""simple docstring"""


														from datasets import load_dataset

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

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







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


														"""simple docstring"""


														_a	     =				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
														_a	     =				self._load_datasamples(1					)
														_a	     =				EncodecFeatureExtractor()
														_a	     =				feature_extractor(lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(input_values.shape							,			(1, 1, 9_36_80)					)
														self.assertTrue(torch.allclose(input_values[0, 0, :30]							,			lowerCAmelCase_							,			atol=1e-6					)					)







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


														"""simple docstring"""


														_a	     =				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
														_a	     =				self._load_datasamples(1					)
														_a	     =				[np.tile(input_audio[0][None]							,			reps=(2, 1)					)]
														input_audio[0][1] *= 0.5
														_a	     =				EncodecFeatureExtractor(feature_size=2					)
														_a	     =				feature_extractor(lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(input_values.shape							,			(1, 2, 9_36_80)					)
														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       :  Dict					)			->							Optional[Any]:


														"""simple docstring"""


														_a	     =				self._load_datasamples(2					)
														# would be easier if the stride was like
														_a	     =				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.$'''							,			):
																					_a	     =				feature_extractor(
																					    lowerCAmelCase_							,			padding='''max_length'''							,			truncation=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values

														# truncate to chunk
														_a	     =				feature_extractor(lowerCAmelCase_							,			truncation=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(2, 1, 7_15_20)					)  # 2 chunks

														# force truncate to max_length
														_a	     =				feature_extractor(
														    lowerCAmelCase_							,			truncation=lowerCAmelCase_							,			max_length=4_80_00							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(2, 1, 4_80_00)					)

														# pad to chunk
														_a	     =				feature_extractor(lowerCAmelCase_							,			padding=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(padded_outputs.shape							,			(2, 1, 9_52_80)					)

														# pad to chunk
														_a	     =				feature_extractor(lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(2, 1, 9_52_80)					)

														# force pad to max length
														_a	     =				feature_extractor(
														    lowerCAmelCase_							,			padding='''max_length'''							,			max_length=10_00_00							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(2, 1, 10_00_00)					)

														# 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.$'''							,			):
																					_a	     =				feature_extractor(lowerCAmelCase_							,			padding=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values

														_a	     =				feature_extractor(input_audio[0]							,			padding=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(1, 1, 9_36_80)					)

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

														_a	     =				feature_extractor(input_audio[0]							,			padding=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(1, 1, 9_36_80)					)

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

														_a	     =				feature_extractor(input_audio[0]							,			padding=lowerCAmelCase_							,			return_tensors='''pt'''					).input_values
														self.assertEquals(truncated_outputs.shape							,			(1, 1, 9_36_80)					)







'''simple docstring'''


def 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				index
							_a	     =				2 * index + 1
							_a	     =				2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														_a	     =				left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														_a	     =				right_index

							if largest != index:
														_a     ,							_a	     =				unsorted[index], unsorted[largest]
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				len(UpperCamelCase      )
							for i in range(n // 2 - 1 ,						-1 ,						-1      ):
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
							for i in range(n - 1 ,						0 ,						-1      ):
														_a     ,							_a	     =				unsorted[i], unsorted[0]
														heapify(UpperCamelCase ,						0 ,						UpperCamelCase      )
							return unsorted


if __name__ == "__main__":
				_snake_case      :      int   							=		input('Enter numbers separated by a comma:\n').strip()
				_snake_case      :      Optional[Any]   							=		[int(item) for item in user_input.split(',')]
				print(heap_sort(unsorted))

