




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


UpperCamelCase__    			=			random.Random()

if is_torch_available():
					import torch







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				Any=1.0      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]=None      ,      SCREAMING_SNAKE_CASE_							:				List[str]=None     ):



					if rng is None:
										__lowerCAmelCase				   =       global_rng

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

					return values
class 			a__    (					unittest.TestCase      ):






						def __init__(   self		,			_A		,			_A=7		,			_A=4_0_0		,			_A=2_0_0_0		,			_A=1		,			_A=0.0		,			_A=1_6_0_0_0		,			_A=True		,			_A=True		,			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""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 				__SCREAMING_SNAKE_CASE(   self		,			_A=False		,			_A=False			):




											"""simple docstring"""



											def _flatten(_A			):
																return list(itertools.chain(*lowerCAmelCase__			)			)

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

											return speech_inputs






@require_torch
@require_torchaudio
class 			a__    (					UpperCamelCase__ ,				unittest.TestCase      ):
						_a							:    int					=    ASTFeatureExtractor






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ASTFeatureExtractionTester(self			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

											# Test not batched input
											__lowerCAmelCase				   =       feat_extract(speech_inputs[0]		,			return_tensors="np"			).input_values
											__lowerCAmelCase				   =       feat_extract(np_speech_inputs[0]		,			return_tensors="np"			).input_values
											self.assertTrue(np.allclose(lowerCAmelCase__		,			lowerCAmelCase__		,			atol=1E-3			)			)

											# Test batched
											__lowerCAmelCase				   =       feat_extract(lowerCAmelCase__		,			padding=lowerCAmelCase__		,			return_tensors="np"			).input_values
											__lowerCAmelCase				   =       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			)			)

											# Test 2-D numpy arrays are batched.
											__lowerCAmelCase				   =       [floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
											__lowerCAmelCase				   =       np.asarray(lowerCAmelCase__			)
											__lowerCAmelCase				   =       feat_extract(lowerCAmelCase__		,			return_tensors="np"			).input_values
											__lowerCAmelCase				   =       feat_extract(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			)			)






						@require_torch
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											import torch

											__lowerCAmelCase				   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
											__lowerCAmelCase				   =       np.random.rand(1_0_0			).astype(np.floataa			)
											__lowerCAmelCase				   =       np_speech_inputs.tolist()

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											from datasets import load_dataset

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

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






						@require_torch
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch.tensor(
											    [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
											     -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
											     -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
											     -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]			)
											# fmt: on

											__lowerCAmelCase				   =       self._load_datasamples(1			)
											__lowerCAmelCase				   =       ASTFeatureExtractor()
											__lowerCAmelCase				   =       feature_extractor(lowerCAmelCase__		,			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]		,			lowerCAmelCase__		,			atol=1E-4			)			)











from __future__ import annotations
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if len(SCREAMING_SNAKE_CASE						) == 0:
    return []
  a__					,							a__	:					int										=min(SCREAMING_SNAKE_CASE						), max(SCREAMING_SNAKE_CASE						)
  a__	:					Optional[int]										=int(max_value - min_value						) + 1
  a__	:					list[list]										=[[] for _ in range(SCREAMING_SNAKE_CASE						)]

  for i in my_list:
    buckets[int(i - min_value						)].append(SCREAMING_SNAKE_CASE						)

  return [v for bucket in buckets for v in sorted(SCREAMING_SNAKE_CASE						)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






