


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__       			=				random.Random()




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Any  ,			__lowerCamelCase:		List[str]=1.0  ,			__lowerCamelCase:		List[Any]=None  ,			__lowerCamelCase:		Dict=None  ):







	'''simple docstring'''



	if rng is None:
		lowercase_			 = global_rng

	lowercase_			 = []
	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=24000						,       UpperCAmelCase=True						,       )     ->       int:






								'''simple docstring'''






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




							def 				A__      (  self  )     ->       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 				A__      (  self						,       UpperCAmelCase=False						,       UpperCAmelCase=False  )     ->       Tuple:






								'''simple docstring'''






								def _flatten(UpperCAmelCase  ):
									return list(itertools.chain(*UpperCAmelCase  )  )

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

								return audio_inputs







@require_torch
class     __lowerCamelCase     (  snake_case_							,   unittest.TestCase     ):


							"""simple docstring"""

							lowerCAmelCase__          =				EncodecFeatureExtractor




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






								'''simple docstring'''






								lowercase_			 = EnCodecFeatureExtractionTester(self  )




							def 				A__      (  self  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								# create three inputs of length 800, 1000, and 1200
								lowercase_			 = [floats_list((1, x)  )[0] for x in range(800						,       1400						,       200  )]
								lowercase_			 = [np.asarray(UpperCAmelCase  ) for audio_input in audio_inputs]

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

								# Test batched
								lowercase_			 = feat_extract(UpperCAmelCase						,       padding=UpperCAmelCase						,       return_tensors="np"  ).input_values
								lowercase_			 = 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 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






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

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




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								from datasets import load_dataset

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

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




							def 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






								lowercase_			 = 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
								lowercase_			 = self._load_datasamples(1  )
								lowercase_			 = EncodecFeatureExtractor()
								lowercase_			 = feature_extractor(UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(input_values.shape						,       (1, 1, 93680)  )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]						,       UpperCAmelCase						,       atol=1e-6  )  )




							def 				A__      (  self  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = 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
								lowercase_			 = self._load_datasamples(1  )
								lowercase_			 = [np.tile(input_audio[0][None]						,       reps=(2, 1)  )]
								input_audio[0][1] *= 0.5
								lowercase_			 = EncodecFeatureExtractor(feature_size=2  )
								lowercase_			 = feature_extractor(UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(input_values.shape						,       (1, 2, 93680)  )
								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 				A__      (  self  )     ->       str:






								'''simple docstring'''






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

								# truncate to chunk
								lowercase_			 = feature_extractor(UpperCAmelCase						,       truncation=UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (2, 1, 71520)  )  # 2 chunks

								# force truncate to max_length
								lowercase_			 = feature_extractor(
								    UpperCAmelCase						,       truncation=UpperCAmelCase						,       max_length=48000						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (2, 1, 48000)  )

								# pad to chunk
								lowercase_			 = feature_extractor(UpperCAmelCase						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(padded_outputs.shape						,       (2, 1, 95280)  )

								# pad to chunk
								lowercase_			 = feature_extractor(UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (2, 1, 95280)  )

								# force pad to max length
								lowercase_			 = feature_extractor(
								    UpperCAmelCase						,       padding="max_length"						,       max_length=100000						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (2, 1, 100000)  )

								# 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.$"						,       ):
									lowercase_			 = feature_extractor(UpperCAmelCase						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values

								lowercase_			 = feature_extractor(input_audio[0]						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (1, 1, 93680)  )

								# no pad if no chunk_length_s
								lowercase_			 = 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.$"						,       ):
									lowercase_			 = feature_extractor(UpperCAmelCase						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values

								lowercase_			 = feature_extractor(input_audio[0]						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (1, 1, 93680)  )

								# no pad if no overlap
								lowercase_			 = 2
								lowercase_			 = 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.$"						,       ):
									lowercase_			 = feature_extractor(UpperCAmelCase						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values

								lowercase_			 = feature_extractor(input_audio[0]						,       padding=UpperCAmelCase						,       return_tensors="pt"  ).input_values
								self.assertEquals(truncated_outputs.shape						,       (1, 1, 93680)  )





from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	return [*quick_sort(__lowerCamelCase  ), pivot, *quick_sort(__lowerCamelCase  )]


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

