
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

_UpperCAmelCase      :				int          = random.Random()


def    A (      lowercase					, lowercase=1.0					, lowercase=None					, lowercase=None				)     ->						List[Any]:
		'''simple docstring'''

		if rng is None:
				UpperCamelCase						=  global_rng

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

		return values


@require_torch
@require_torchaudio
class        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=400       ,   A_=2_000       ,   A_=24       ,   A_=24       ,   A_=0.0       ,   A_=16_000       ,   A_=True       ,   A_=True       ,   )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  min_seq_length
				UpperCamelCase						=  max_seq_length
				UpperCamelCase						=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCamelCase						=  feature_size
				UpperCamelCase						=  num_mel_bins
				UpperCamelCase						=  padding_value
				UpperCamelCase						=  sampling_rate
				UpperCamelCase						=  return_attention_mask
				UpperCamelCase						=  do_normalize



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "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       ,   A_=False       ,   A_=False )						->      Optional[int]:
				"""simple docstring"""
				def _flatten(A_ ):
						return list(itertools.chain(*A_ ) )

				if equal_length:
						UpperCamelCase						=  [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
				else:
						# make sure that inputs increase in size
						UpperCamelCase						=  [
						    floats_list((x, self.feature_size) )
						    for x in range(self.min_seq_length       ,   self.max_seq_length       ,   self.seq_length_diff )
						]
				if numpify:
						UpperCamelCase						=  [np.asarray(A_ ) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       SpeechaTextFeatureExtractor if is_speech_available() else None



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  SpeechaTextFeatureExtractionTester(self )



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				self.assertTrue(np.all(np.mean(A_       ,   axis=0 ) < 1e-3 ) )
				self.assertTrue(np.all(np.abs(np.var(A_       ,   axis=0 ) - 1 ) < 1e-3 ) )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				# Tests that all call wrap to encode_plus and batch_encode_plus
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				# create three inputs of length 800, 1000, and 1200
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  [np.asarray(A_ ) for speech_input in speech_inputs]

				# Test feature size
				UpperCamelCase						=  feature_extractor(A_       ,   padding=A_       ,   return_tensors='np' ).input_features
				self.assertTrue(input_features.ndim == 3 )
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )

				# Test not batched input
				UpperCamelCase						=  feature_extractor(speech_inputs[0]       ,   return_tensors='np' ).input_features
				UpperCamelCase						=  feature_extractor(np_speech_inputs[0]       ,   return_tensors='np' ).input_features
				self.assertTrue(np.allclose(A_       ,   A_       ,   atol=1e-3 ) )

				# Test batched
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				for enc_seq_a, enc_seq_a in zip(A_       ,   A_ ):
						self.assertTrue(np.allclose(A_       ,   A_       ,   atol=1e-3 ) )

				# Test 2-D numpy arrays are batched.
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in (800, 800, 800)]
				UpperCamelCase						=  np.asarray(A_ )
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='np' ).input_features
				for enc_seq_a, enc_seq_a in zip(A_       ,   A_ ):
						self.assertTrue(np.allclose(A_       ,   A_       ,   atol=1e-3 ) )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]

				UpperCamelCase						=  ['longest', 'max_length', 'do_not_pad']
				UpperCamelCase						=  [None, 16, None]
				for max_length, padding in zip(A_       ,   A_ ):
						UpperCamelCase						=  feature_extractor(
						    A_       ,   padding=A_       ,   max_length=A_       ,   return_attention_mask=A_ )
						UpperCamelCase						=  inputs.input_features
						UpperCamelCase						=  inputs.attention_mask
						UpperCamelCase						=  [np.sum(A_ ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]

				UpperCamelCase						=  ['longest', 'max_length', 'do_not_pad']
				UpperCamelCase						=  [None, 16, None]
				for max_length, padding in zip(A_       ,   A_ ):
						UpperCamelCase						=  feature_extractor(
						    A_       ,   max_length=A_       ,   padding=A_       ,   return_tensors='np'       ,   return_attention_mask=A_ )
						UpperCamelCase						=  inputs.input_features
						UpperCamelCase						=  inputs.attention_mask
						UpperCamelCase						=  [np.sum(A_ ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  feature_extractor(
				    A_       ,   padding='max_length'       ,   max_length=4       ,   truncation=A_       ,   return_tensors='np'       ,   return_attention_mask=A_       ,   )
				UpperCamelCase						=  inputs.input_features
				UpperCamelCase						=  inputs.attention_mask
				UpperCamelCase						=  np.sum(attention_mask == 1       ,   axis=1 )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
				self._check_zero_mean_unit_variance(input_features[1] )
				self._check_zero_mean_unit_variance(input_features[2] )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  feature_extractor(
				    A_       ,   padding='longest'       ,   max_length=4       ,   truncation=A_       ,   return_tensors='np'       ,   return_attention_mask=A_       ,   )
				UpperCamelCase						=  inputs.input_features
				UpperCamelCase						=  inputs.attention_mask
				UpperCamelCase						=  np.sum(attention_mask == 1       ,   axis=1 )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
				self._check_zero_mean_unit_variance(input_features[2] )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape       ,   (3, 4, 24) )

				UpperCamelCase						=  [floats_list((1, x) )[0] for x in range(800       ,   1_400       ,   200 )]
				UpperCamelCase						=  feature_extractor(
				    A_       ,   padding='longest'       ,   max_length=16       ,   truncation=A_       ,   return_tensors='np'       ,   return_attention_mask=A_       ,   )
				UpperCamelCase						=  inputs.input_features
				UpperCamelCase						=  inputs.attention_mask
				UpperCamelCase						=  np.sum(attention_mask == 1       ,   axis=1 )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
				self._check_zero_mean_unit_variance(input_features[2] )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape       ,   (3, 6, 24) )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				import torch

				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  np.random.rand(100       ,   32 ).astype(np.floataa )
				UpperCamelCase						=  np_speech_inputs.tolist()

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



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				from datasets import load_dataset

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

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



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# fmt: off
				UpperCamelCase						=  np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				] )
				# fmt: on

				UpperCamelCase						=  self._load_datasamples(1 )
				UpperCamelCase						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
				UpperCamelCase						=  feature_extractor(A_       ,   return_tensors='pt' ).input_features
				self.assertEquals(input_features.shape       ,   (1, 584, 24) )
				self.assertTrue(np.allclose(input_features[0, 0, :30]       ,   A_       ,   atol=1e-4 ) )


import re


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
