"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


SCREAMING_SNAKE_CASE									=						random.Random()

if is_torch_available():
						import torch


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=1.0     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None			):






	"""simple docstring"""





	if rng is None:
		_lowercase :    List[str]							=							global_rng

	_lowercase :    Dict							=							[]
	for batch_idx in range(shape[0]			):
		values.append([]			)
		for _ in range(shape[1]			):
			values[-1].append(rng.random() * scale			)

	return values



class  _lowerCamelCase			(unittest.TestCase     ):







				def __init__( self    :       Optional[int]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       List[str]=7			,					lowerCamelCase_    :       Any=4_0_0			,					lowerCamelCase_    :       Tuple=2_0_0_0			,					lowerCamelCase_    :       Union[str, Any]=1			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       Union[str, Any]=1_6_0_0_0			,					lowerCamelCase_    :       int=True			,					lowerCamelCase_    :       Optional[int]=True			,					):

					"""simple docstring"""






					_lowercase :    List[str]							=							parent
					_lowercase :    Tuple							=							batch_size
					_lowercase :    Dict							=							min_seq_length
					_lowercase :    Optional[int]							=							max_seq_length
					_lowercase :    List[Any]							=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					_lowercase :    str							=							feature_size
					_lowercase :    List[str]							=							padding_value
					_lowercase :    List[str]							=							sampling_rate
					_lowercase :    Optional[Any]							=							return_attention_mask
					_lowercase :    Any							=							do_normalize







				def 							__UpperCAmelCase					( 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,
					    "do_normalize": self.do_normalize,
					}







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       str=False			,					lowerCamelCase_    :       Any=False						):

					"""simple docstring"""






					def _flatten(lowerCamelCase_    :       Union[str, Any]						):
						return list(itertools.chain(*__A						)						)

					if equal_length:
						_lowercase :    Optional[Any]							=							floats_list((self.batch_size, self.max_seq_length)						)
					else:
						# make sure that inputs increase in size
						_lowercase :    Optional[int]							=							[
						    _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 :    Optional[int]							=							[np.asarray(__A						) for x in speech_inputs]

					return speech_inputs



@require_torch
@require_torchaudio
class  _lowerCamelCase			(__lowerCamelCase						,							unittest.TestCase     ):
				_snake_case     =  ASTFeatureExtractor







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							ASTFeatureExtractionTester(self						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    List[str]							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					# create three inputs of length 800, 1000, and 1200
					_lowercase :    Union[str, Any]							=							[floats_list((1, x)						)[0] for x in range(8_0_0			,					1_4_0_0			,					2_0_0						)]
					_lowercase :    str							=							[np.asarray(__A						) for speech_input in speech_inputs]

					# Test not batched input
					_lowercase :    Any							=							feat_extract(speech_inputs[0]			,					return_tensors='np'						).input_values
					_lowercase :    List[Any]							=							feat_extract(np_speech_inputs[0]			,					return_tensors='np'						).input_values
					self.assertTrue(np.allclose(__A			,					__A			,					atol=1E-3						)						)

					# Test batched
					_lowercase :    Dict							=							feat_extract(__A			,					padding=__A			,					return_tensors='np'						).input_values
					_lowercase :    str							=							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						)						)

					# Test 2-D numpy arrays are batched.
					_lowercase :    Dict							=							[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
					_lowercase :    int							=							np.asarray(__A						)
					_lowercase :    Dict							=							feat_extract(__A			,					return_tensors='np'						).input_values
					_lowercase :    Optional[int]							=							feat_extract(__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						)						)







				@require_torch
				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					import torch

					_lowercase :    List[str]							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					_lowercase :    List[str]							=							np.random.rand(1_0_0						).astype(np.floataa						)
					_lowercase :    Optional[int]							=							np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						_lowercase :    int							=							feature_extractor.pad([{'input_values': inputs}]			,					return_tensors='np'						)
						self.assertTrue(np_processed.input_values.dtype == np.floataa						)
						_lowercase :    Tuple							=							feature_extractor.pad([{'input_values': inputs}]			,					return_tensors='pt'						)
						self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					from datasets import load_dataset

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

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







				@require_torch
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							torch.tensor(
					    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
					     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
					     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
					     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]						)
					# fmt: on

					_lowercase :    Any							=							self._load_datasamples(1						)
					_lowercase :    int							=							ASTFeatureExtractor()
					_lowercase :    str							=							feature_extractor(__A			,					return_tensors='pt'						).input_values
					self.assertEquals(input_values.shape			,					(1, 1_0_2_4, 1_2_8)						)
					self.assertTrue(torch.allclose(input_values[0, 0, :3_0]			,					__A			,					atol=1E-4						)						)







"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	stooge(__UpperCAmelCase     ,0     ,len(__UpperCAmelCase			) - 1			)
	return arr


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		_lowercase				,      _lowercase :    List[str]							=							arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		_lowercase :    Optional[int]							=							(int)((h - i + 1) / 3			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)

		# Recursively sort last 2/3 elements
		stooge(__UpperCAmelCase     ,i + t     ,(__UpperCAmelCase)			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)


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(stooge_sort(unsorted))



