


'''simple docstring'''

import re

def 							__magic_name__(						lowerCamelCase):





      if len(re.findall('''[ATCG]''',				SCREAMING_SNAKE_CASE__)) != len(SCREAMING_SNAKE_CASE__):
            raise ValueError('''Invalid Strand''')

      return dna.translate(dna.maketrans('''ATCG''',				'''TAGC'''))


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''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				: Tuple							    =				random.Random()

if is_torch_available():
	import torch

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=1.0,				lowerCamelCase=None,				lowerCamelCase=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      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase=7       ,			__lowercase=4_00       ,			__lowercase=20_00       ,			__lowercase=1       ,			__lowercase=0.0       ,			__lowercase=1_60_00       ,			__lowercase=True       ,			__lowercase=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       ,			__lowercase=False       ,			__lowercase=False		):
										def _flatten(__lowercase		):
																return list(itertools.chain(*__lowercase		)		)

										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(__lowercase		) for x in speech_inputs]

										return speech_inputs


@require_torch
@require_torchaudio
class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Union[str, Any]	=	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_00       ,			14_00       ,			2_00		)]
										__lowerCAmelCase							       =				[np.asarray(__lowercase		) 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(__lowercase       ,			__lowercase       ,			atol=1e-3		)		)

										# Test batched
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			padding=__lowercase       ,			return_tensors='''np'''		).input_values
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			padding=__lowercase       ,			return_tensors='''np'''		).input_values
										for enc_seq_a, enc_seq_a in zip(__lowercase       ,			__lowercase		):
																self.assertTrue(np.allclose(__lowercase       ,			__lowercase       ,			atol=1e-3		)		)

										# Test 2-D numpy arrays are batched.
										__lowerCAmelCase							       =				[floats_list((1, x)		)[0] for x in (8_00, 8_00, 8_00)]
										__lowerCAmelCase							       =				np.asarray(__lowercase		)
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			return_tensors='''np'''		).input_values
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			return_tensors='''np'''		).input_values
										for enc_seq_a, enc_seq_a in zip(__lowercase       ,			__lowercase		):
																self.assertTrue(np.allclose(__lowercase       ,			__lowercase       ,			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_00		).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       ,			__lowercase		):
										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(__lowercase		)		)[: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(__lowercase       ,			return_tensors='''pt'''		).input_values
										self.assertEquals(input_values.shape       ,			(1, 10_24, 1_28)		)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,			__lowercase       ,			atol=1e-4		)		)

