



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


UpperCAmelCase__  =			random.Random()





def 				A      (	_UpperCAmelCase							:  str				,  _UpperCAmelCase							:  List[Any]=1.0				,  _UpperCAmelCase							:  Dict=None				,  _UpperCAmelCase							:  Dict=None       )							->					Optional[int]:







			'''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		):



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



						"""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        _lowerCamelCase			(		self  :    Optional[int])  ->       str:



						"""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  :    Dict					,						A  :    List[Any]=False					,						A  :    List[Any]=False)  ->       Dict:



						"""simple docstring"""






						def _flatten(A  :    str):
									return list(itertools.chain(*A))

						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(A) for x in audio_inputs]

						return audio_inputs







@require_torch
class      __lowerCAmelCase       (     A							,		unittest.TestCase		):
			UpperCamelCase     =	EncodecFeatureExtractor



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



						"""simple docstring"""






						_UpperCAmelCase									=  EnCodecFeatureExtractionTester(self)



			def        _lowerCamelCase			(		self  :    str)  ->       str:



						"""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(8_00					,						14_00					,						2_00)]
						_UpperCAmelCase									=  [np.asarray(A) 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(A					,						A					,						atol=1E-3))

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



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



						"""simple docstring"""






						_UpperCAmelCase									=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						_UpperCAmelCase									=  np.random.rand(1_00).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        _lowerCamelCase			(		self  :    Optional[Any]					,						A  :    List[Any])  ->       int:



						"""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(A))[:num_samples]['audio']

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



			def        _lowerCamelCase			(		self  :    Any)  ->       Tuple:



						"""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(A					,						return_tensors='pt').input_values
						self.assertEquals(input_values.shape					,						(1, 1, 9_36_80))
						self.assertTrue(torch.allclose(input_values[0, 0, :30]					,						A					,						atol=1E-6))



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



						"""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(A					,						return_tensors='pt').input_values
						self.assertEquals(input_values.shape					,						(1, 2, 9_36_80))
						self.assertTrue(torch.allclose(input_values[0, 0, :30]					,						A					,						atol=1E-6))
						self.assertTrue(torch.allclose(input_values[0, 1, :30]					,						EXPECTED_INPUT_VALUES * 0.5					,						atol=1E-6))



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



						"""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=0.0_1)

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

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

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

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

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

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

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

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

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

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

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





def 				A      (	_UpperCAmelCase							:  List[Any]       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  len(_UpperCAmelCase       )
			for _ in range(
			    0				,  int(((no_of_elements - 1) / 2) + 1       )       ):  # we don't need to traverse to end of list as
						for j in range(0				,  no_of_elements - 1       ):
									if (
									    lst[j + 1] < lst[j]
									):  # applying bubble sort algorithm from left to right (or forwards)
												_UpperCAmelCase									=  lst[j + 1]
												_UpperCAmelCase									=  lst[j]
												_UpperCAmelCase									=  temp
									if (
									    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
									):  # applying bubble sort algorithm from right to left (or backwards)
												_UpperCAmelCase									=  lst[no_of_elements - 1 - j]
												_UpperCAmelCase									=  lst[no_of_elements - 2 - j]
												_UpperCAmelCase									=  temp
			return lst


if __name__ == "__main__":
							print("enter the list to be sorted")
							UpperCAmelCase__  =			[int(x) for x in input().split()]  # inputing elements of the list in one line
							UpperCAmelCase__  =			double_sort(lst)
							print("the sorted list is")
							print(sorted_lst)
