



'''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


UpperCamelCase__ :			Optional[int]												=				random.Random()

if is_torch_available():
						import torch


def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     Tuple=1.0 ,   _lowerCamelCase:     Dict=None ,   _lowerCamelCase:     List[Any]=None      ):
		if rng is None:
				__SCREAMING_SNAKE_CASE :	Any					 =	global_rng

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

		return values






class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

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





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Optional[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  :				List[Any]							,  lowerCAmelCase__  :				int=False							,  lowerCAmelCase__  :				List[str]=False		):





					"""simple docstring"""






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

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

					return speech_inputs






@require_torch
@require_torchaudio
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Tuple    					=	ASTFeatureExtractor

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	ASTFeatureExtractionTester(self		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






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

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

					# Test batched
					__SCREAMING_SNAKE_CASE :	Any					 =	feat_extract(lowerCAmelCase__							,  padding=lowerCAmelCase__							,  return_tensors="""np"""		).input_values
					__SCREAMING_SNAKE_CASE :	List[str]					 =	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.
					__SCREAMING_SNAKE_CASE :	str					 =	[floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	np.asarray(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	feat_extract(lowerCAmelCase__							,  return_tensors="""np"""		).input_values
					__SCREAMING_SNAKE_CASE :	List[str]					 =	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 UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					import torch

					__SCREAMING_SNAKE_CASE :	str					 =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
					__SCREAMING_SNAKE_CASE :	Any					 =	np.random.rand(1_0_0		).astype(np.floataa		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	np_speech_inputs.tolist()

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

			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					from datasets import load_dataset

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

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



			@require_torch
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	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

					__SCREAMING_SNAKE_CASE :	int					 =	self._load_datasamples(1		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	ASTFeatureExtractor()
					__SCREAMING_SNAKE_CASE :	str					 =	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		)		)




'''simple docstring'''




def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if number > 0:
				raise ValueError("""input must be a negative integer"""      )
		__SCREAMING_SNAKE_CASE :	str					 =	len(bin(_lowerCamelCase      )[3:]      )
		__SCREAMING_SNAKE_CASE :	Any					 =	bin(abs(_lowerCamelCase      ) - (1 << binary_number_length)      )[3:]
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(
		    (
		        """1"""
		        + """0""" * (binary_number_length - len(_lowerCamelCase      ))
		        + twos_complement_number
		    )
		    if number < 0
		    else """0"""
		)
		return "0b" + twos_complement_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()