
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


A		: Any								=     random.Random()

if is_torch_available():
		import torch




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]=1.0		,   SCREAMING_SNAKE_CASE_	:	int=None		,   SCREAMING_SNAKE_CASE_	:	Optional[int]=None       )  ->     List[str]:
						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



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=400				,			__UpperCamelCase=2_000				,			__UpperCamelCase=1				,			__UpperCamelCase=0.0				,			__UpperCamelCase=16_000				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			):
									_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
									_lowercase					       =					do_normalize






			def    UpperCamelCase_				(      self  ):
									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				,			__UpperCamelCase=False				,			__UpperCamelCase=False  ):
									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 speech_inputs]

									return speech_inputs







@require_torch
@require_torchaudio
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			Any					   =							ASTFeatureExtractor






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					ASTFeatureExtractionTester(self  )






			def    UpperCamelCase_				(      self  ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									_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				,			1_400				,			200  )]
									_lowercase					       =					[np.asarray(__UpperCamelCase  ) for speech_input in speech_inputs]

									# Test not batched input
									_lowercase					       =					feat_extract(speech_inputs[0]				,			return_tensors="""np"""  ).input_values
									_lowercase					       =					feat_extract(np_speech_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  )  )

									# Test 2-D numpy arrays are batched.
									_lowercase					       =					[floats_list((1, x)  )[0] for x in (800, 800, 800)]
									_lowercase					       =					np.asarray(__UpperCamelCase  )
									_lowercase					       =					feat_extract(__UpperCamelCase				,			return_tensors="""np"""  ).input_values
									_lowercase					       =					feat_extract(__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  )  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									import torch

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

									for inputs in [py_speech_inputs, np_speech_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    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									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 speech_samples]






			@require_torch
			def    UpperCamelCase_				(      self  ):
									# fmt: off
									_lowercase					       =					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

									_lowercase					       =					self._load_datasamples(1  )
									_lowercase					       =					ASTFeatureExtractor()
									_lowercase					       =					feature_extractor(__UpperCamelCase				,			return_tensors="""pt"""  ).input_values
									self.assertEquals(input_values.shape				,			(1, 1_024, 128)  )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]				,			__UpperCamelCase				,			atol=1E-4  )  )

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     bool:
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_lowercase					       =					True

						# sum is not zero and set is empty then false
						for i in range(1		,   required_sum + 1       ):
												_lowercase					       =					False

						for i in range(1		,   arr_len + 1       ):
												for j in range(1		,   required_sum + 1       ):
																		if arr[i - 1] > j:
																								_lowercase					       =					subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_lowercase					       =					subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()