



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  :						Dict    							=      random.Random()

if is_torch_available():
			import torch



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							int=1.0						,   lowercase_     :							List[Any]=None						,   lowercase_     :							str=None							)	->       Tuple:
				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 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=7				,       lowerCamelCase__=4_0_0				,       lowerCamelCase__=2_0_0_0				,       lowerCamelCase__=1				,       lowerCamelCase__=0.0				,       lowerCamelCase__=1_6_0_0_0				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       ):
									_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 			snake_case__      (    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 			snake_case__      (    self				,       lowerCamelCase__=False				,       lowerCamelCase__=False   ):
									def _flatten(lowerCamelCase__   ):
													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 							lowerCamelCase_(   a__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[int]										= ASTFeatureExtractor

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ASTFeatureExtractionTester(self   )

					def 			snake_case__      (    self   ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									_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 			snake_case__      (    self   ):
									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 			snake_case__      (    self				,       lowerCamelCase__   ):
									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 			snake_case__      (    self   ):
									# fmt: off
									_lowerCamelCase        =				torch.tensor(
									    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
									     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
									     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
									     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]   )
									# 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   )   )





"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str							)	->       bool:
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
				_lowerCamelCase        =				True
				for i in range(lowercase_							):
								for j in range(m + 1							):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				_lowerCamelCase        =				True
																if a[i].islower():
																				_lowerCamelCase        =				True
				return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
