

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__		 =     random.Random()







def 						lowerCamelCase__      ( __A    :List[str]	,__A    :Tuple=1.0	,__A    :str=None	,__A    :int=None		):


       """simple docstring"""




       if rng is None:
              __snake_case       	=							global_rng

       __snake_case       	=							[]
       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     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=7      , _UpperCamelCase=4_00      , _UpperCamelCase=20_00      , _UpperCamelCase=24      , _UpperCamelCase=24      , _UpperCamelCase=0.0      , _UpperCamelCase=1_60_00      , _UpperCamelCase=True      , _UpperCamelCase=True      , )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							parent
              __snake_case       	=							batch_size
              __snake_case       	=							min_seq_length
              __snake_case       	=							max_seq_length
              __snake_case       	=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
              __snake_case       	=							feature_size
              __snake_case       	=							num_mel_bins
              __snake_case       	=							padding_value
              __snake_case       	=							sampling_rate
              __snake_case       	=							return_attention_mask
              __snake_case       	=							do_normalize


       def 			a		(		self   )      ->							Optional[int]:






              """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 			a		(		self      , _UpperCamelCase=False      , _UpperCamelCase=False   )      ->							List[str]:






              """simple docstring"""


              def _flatten(_UpperCamelCase   ):
                     return list(itertools.chain(*_UpperCamelCase   )   )

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






@require_torch
@require_torchaudio
class     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							SpeechaTextFeatureExtractor if is_speech_available() else None


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							SpeechaTextFeatureExtractionTester(self   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              self.assertTrue(np.all(np.mean(_UpperCamelCase      , axis=0   ) < 1E-3   )   )
              self.assertTrue(np.all(np.abs(np.var(_UpperCamelCase      , axis=0   ) - 1   ) < 1E-3   )   )


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              # create three inputs of length 800, 1000, and 1200
              __snake_case       	=							[floats_list((1, x)   )[0] for x in range(8_00      , 14_00      , 2_00   )]
              __snake_case       	=							[np.asarray(_UpperCamelCase   ) for speech_input in speech_inputs]

              # Test feature size
              __snake_case       	=							feature_extractor(_UpperCamelCase      , padding=_UpperCamelCase      , 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
              __snake_case       	=							feature_extractor(speech_inputs[0]      , return_tensors="""np"""   ).input_features
              __snake_case       	=							feature_extractor(np_speech_inputs[0]      , return_tensors="""np"""   ).input_features
              self.assertTrue(np.allclose(_UpperCamelCase      , _UpperCamelCase      , atol=1E-3   )   )

              # Test batched
              __snake_case       	=							feature_extractor(_UpperCamelCase      , return_tensors="""np"""   ).input_features
              __snake_case       	=							feature_extractor(_UpperCamelCase      , return_tensors="""np"""   ).input_features
              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.
              __snake_case       	=							[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
              __snake_case       	=							np.asarray(_UpperCamelCase   )
              __snake_case       	=							feature_extractor(_UpperCamelCase      , return_tensors="""np"""   ).input_features
              __snake_case       	=							feature_extractor(_UpperCamelCase      , return_tensors="""np"""   ).input_features
              for enc_seq_a, enc_seq_a in zip(_UpperCamelCase      , _UpperCamelCase   ):
                     self.assertTrue(np.allclose(_UpperCamelCase      , _UpperCamelCase      , atol=1E-3   )   )


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              __snake_case       	=							[floats_list((1, x)   )[0] for x in range(8_00      , 14_00      , 2_00   )]

              __snake_case       	=							["""longest""", """max_length""", """do_not_pad"""]
              __snake_case       	=							[None, 16, None]
              for max_length, padding in zip(_UpperCamelCase      , _UpperCamelCase   ):
                     __snake_case       	=							feature_extractor(
                         _UpperCamelCase      , padding=_UpperCamelCase      , max_length=_UpperCamelCase      , return_attention_mask=_UpperCamelCase   )
                     __snake_case       	=							inputs.input_features
                     __snake_case       	=							inputs.attention_mask
                     __snake_case       	=							[np.sum(_UpperCamelCase   ) 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 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              __snake_case       	=							[floats_list((1, x)   )[0] for x in range(8_00      , 14_00      , 2_00   )]

              __snake_case       	=							["""longest""", """max_length""", """do_not_pad"""]
              __snake_case       	=							[None, 16, None]
              for max_length, padding in zip(_UpperCamelCase      , _UpperCamelCase   ):
                     __snake_case       	=							feature_extractor(
                         _UpperCamelCase      , max_length=_UpperCamelCase      , padding=_UpperCamelCase      , return_tensors="""np"""      , return_attention_mask=_UpperCamelCase   )
                     __snake_case       	=							inputs.input_features
                     __snake_case       	=							inputs.attention_mask
                     __snake_case       	=							[np.sum(_UpperCamelCase   ) 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 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              __snake_case       	=							[floats_list((1, x)   )[0] for x in range(8_00      , 14_00      , 2_00   )]
              __snake_case       	=							feature_extractor(
                  _UpperCamelCase      , padding="""max_length"""      , max_length=4      , truncation=_UpperCamelCase      , return_tensors="""np"""      , return_attention_mask=_UpperCamelCase      , )
              __snake_case       	=							inputs.input_features
              __snake_case       	=							inputs.attention_mask
              __snake_case       	=							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 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              __snake_case       	=							[floats_list((1, x)   )[0] for x in range(8_00      , 14_00      , 2_00   )]
              __snake_case       	=							feature_extractor(
                  _UpperCamelCase      , padding="""longest"""      , max_length=4      , truncation=_UpperCamelCase      , return_tensors="""np"""      , return_attention_mask=_UpperCamelCase      , )
              __snake_case       	=							inputs.input_features
              __snake_case       	=							inputs.attention_mask
              __snake_case       	=							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)   )

              __snake_case       	=							[floats_list((1, x)   )[0] for x in range(8_00      , 14_00      , 2_00   )]
              __snake_case       	=							feature_extractor(
                  _UpperCamelCase      , padding="""longest"""      , max_length=16      , truncation=_UpperCamelCase      , return_tensors="""np"""      , return_attention_mask=_UpperCamelCase      , )
              __snake_case       	=							inputs.input_features
              __snake_case       	=							inputs.attention_mask
              __snake_case       	=							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 			a		(		self   )      ->							Any:






              """simple docstring"""


              import torch

              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              __snake_case       	=							np.random.rand(1_00      , 32   ).astype(np.floataa   )
              __snake_case       	=							np_speech_inputs.tolist()

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


       def 			a		(		self      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              from datasets import load_dataset

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

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






       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							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

              __snake_case       	=							self._load_datasamples(1   )
              __snake_case       	=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
              __snake_case       	=							feature_extractor(_UpperCamelCase      , return_tensors="""pt"""   ).input_features
              self.assertEquals(input_features.shape      , (1, 5_84, 24)   )
              self.assertTrue(np.allclose(input_features[0, 0, :30]      , _UpperCamelCase      , atol=1E-4   )   )




from __future__ import annotations

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							text, pattern
              __snake_case	,    __snake_case       	=							len(_UpperCamelCase   ), len(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if char == self.pattern[i]:
                            return i
              return -1


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if self.pattern[i] != self.text[current_pos + i]:
                            return current_pos + i
              return -1






       def 			a		(		self   )      ->							list[int]:






              """simple docstring"""


              __snake_case       	=							[]
              for i in range(self.textLen - self.patLen + 1   ):
                     __snake_case       	=							self.mismatch_in_text(_UpperCamelCase   )
                     if mismatch_index == -1:
                            positions.append(_UpperCamelCase   )
                     else:
                            __snake_case       	=							self.match_in_pattern(self.text[mismatch_index]   )
                            __snake_case       	=							(
                                mismatch_index - match_index
                            )  # shifting index lgtm [py/multiple-definition]
              return positions






UpperCamelCase__		 =     '''ABAABA'''
UpperCamelCase__		 =     '''AB'''
UpperCamelCase__		 =     BoyerMooreSearch(text, pattern)
UpperCamelCase__		 =     bms.bad_character_heuristic()

if len(positions) == 0:
    print('''No match found''')
else:
    print('''Pattern found in following positions: ''')
    print(positions)

