code: 
  
  
  
  """simple docstring"""
  import itertools
  import random
  import unittest
  
  import numpy as np
  
  from transformers import EncodecFeatureExtractor
  from transformers.testing_utils import require_torch
  from transformers.utils.import_utils import is_torch_available
  
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
  
  
  if is_torch_available():
    import torch
  
  
  _lowerCAmelCase					:List[Any]  =      random.Random()
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Dict  , UpperCamelCase__	:	Any=1.0  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	int=None							):
      if rng is None:
          _UpperCAmelCase							:						Optional[Any]       							=					global_rng
  
      _UpperCAmelCase							:						Optional[int]       							=					[]
      for batch_idx in range(shape[0]							):
          values.append([]							)
          for _ in range(shape[1]							):
              values[-1].append(rng.random() * scale							)
  
      return values
  @require_torch
  class 							_UpperCAmelCase							(				unittest.TestCase   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A=7 ,			A=4_0_0 ,			A=2_0_0_0 ,			A=1 ,			A=0.0 ,			A=2_4_0_0_0 ,			A=True ,			)							->      List[Any]:
           _UpperCAmelCase							:						List[str]       							=					parent
           _UpperCAmelCase							:						str       							=					batch_size
           _UpperCAmelCase							:						List[Any]       							=					min_seq_length
           _UpperCAmelCase							:						List[Any]       							=					max_seq_length
           _UpperCAmelCase							:						Optional[Any]       							=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _UpperCAmelCase							:						List[Any]       							=					feature_size
           _UpperCAmelCase							:						List[str]       							=					padding_value
           _UpperCAmelCase							:						Optional[Any]       							=					sampling_rate
           _UpperCAmelCase							:						Tuple       							=					return_attention_mask
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A=False ,			A=False	)							->      Dict:
           def _flatten(A	):
               return list(itertools.chain(*A	)	)
  
           if equal_length:
               _UpperCAmelCase							:						Optional[Any]       							=					floats_list((self.batch_size, self.max_seq_length)	)
           else:
               # make sure that inputs increase in size
               _UpperCAmelCase							:						Optional[int]       							=					[
                   _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:
               _UpperCAmelCase							:						Optional[int]       							=					[np.asarray(A	) for x in audio_inputs]
  
           return audio_inputs
  
  
  
  @require_torch
  class 							_UpperCAmelCase							(				a	,unittest.TestCase   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =EncodecFeatureExtractor
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           _UpperCAmelCase							:						Union[str, Any]       							=					EnCodecFeatureExtractionTester(self	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           # Tests that all call wrap to encode_plus and batch_encode_plus
           _UpperCAmelCase							:						Dict       							=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           # create three inputs of length 800, 1000, and 1200
           _UpperCAmelCase							:						Union[str, Any]       							=					[floats_list((1, x)	)[0] for x in range(8_0_0 ,			1_4_0_0 ,			2_0_0	)]
           _UpperCAmelCase							:						Optional[int]       							=					[np.asarray(A	) for audio_input in audio_inputs]
  
           # Test not batched input
           _UpperCAmelCase							:						str       							=					feat_extract(audio_inputs[0] ,			return_tensors='''np'''	).input_values
           _UpperCAmelCase							:						List[str]       							=					feat_extract(np_audio_inputs[0] ,			return_tensors='''np'''	).input_values
           self.assertTrue(np.allclose(A ,			A ,			atol=1E-3	)	)
  
           # Test batched
           _UpperCAmelCase							:						Optional[int]       							=					feat_extract(A ,			padding=A ,			return_tensors='''np'''	).input_values
           _UpperCAmelCase							:						str       							=					feat_extract(A ,			padding=A ,			return_tensors='''np'''	).input_values
           for enc_seq_a, enc_seq_a in zip(A ,			A	):
               self.assertTrue(np.allclose(A ,			A ,			atol=1E-3	)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						int       							=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           _UpperCAmelCase							:						Dict       							=					np.random.rand(1_0_0	).astype(np.floataa	)
           _UpperCAmelCase							:						str       							=					np_audio_inputs.tolist()
  
           for inputs in [py_audio_inputs, np_audio_inputs]:
               _UpperCAmelCase							:						Union[str, Any]       							=					feature_extractor.pad([{'''input_values''': inputs}] ,			return_tensors='''np'''	)
               self.assertTrue(np_processed.input_values.dtype == np.floataa	)
               _UpperCAmelCase							:						int       							=					feature_extractor.pad([{'''input_values''': inputs}] ,			return_tensors='''pt'''	)
               self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
           from datasets import load_dataset
  
           _UpperCAmelCase							:						Union[str, Any]       							=					load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,			'''clean''' ,			split='''validation'''	)
           # automatic decoding with librispeech
           _UpperCAmelCase							:						Dict       							=					ds.sort('''id'''	).select(range(A	)	)[:num_samples]['''audio''']
  
           return [x["array"] for x in audio_samples]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      str:
           # fmt: off
           _UpperCAmelCase							:						Optional[int]       							=					torch.tensor(
               [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
                3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
                2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
                4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
                7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
                4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]	)
           # fmt: on
           _UpperCAmelCase							:						Optional[int]       							=					self._load_datasamples(1	)
           _UpperCAmelCase							:						Optional[Any]       							=					EncodecFeatureExtractor()
           _UpperCAmelCase							:						List[str]       							=					feature_extractor(A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(input_values.shape ,			(1, 1, 9_3_6_8_0)	)
           self.assertTrue(torch.allclose(input_values[0, 0, :3_0] ,			A ,			atol=1E-6	)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           # fmt: off
           _UpperCAmelCase							:						Union[str, Any]       							=					torch.tensor(
               [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
                3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
                2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
                4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
                7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
                4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]	)
           # fmt: on
           _UpperCAmelCase							:						List[str]       							=					self._load_datasamples(1	)
           _UpperCAmelCase							:						Optional[Any]       							=					[np.tile(input_audio[0][None] ,			reps=(2, 1)	)]
           input_audio[0][1] *= 0.5
           _UpperCAmelCase							:						Dict       							=					EncodecFeatureExtractor(feature_size=2	)
           _UpperCAmelCase							:						str       							=					feature_extractor(A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(input_values.shape ,			(1, 2, 9_3_6_8_0)	)
           self.assertTrue(torch.allclose(input_values[0, 0, :3_0] ,			A ,			atol=1E-6	)	)
           self.assertTrue(torch.allclose(input_values[0, 1, :3_0] ,			EXPECTED_INPUT_VALUES * 0.5 ,			atol=1E-6	)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           _UpperCAmelCase							:						Optional[Any]       							=					self._load_datasamples(2	)
           # would be easier if the stride was like
           _UpperCAmelCase							:						Union[str, Any]       							=					EncodecFeatureExtractor(feature_size=1 ,			chunk_length_s=1 ,			overlap=0.01	)
  
           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               A ,			'''^Both padding and truncation were set. Make sure you only set one.$''' ,			):
               _UpperCAmelCase							:						Union[str, Any]       							=					feature_extractor(
                   A ,			padding='''max_length''' ,			truncation=A ,			return_tensors='''pt'''	).input_values
  
           # truncate to chunk
           _UpperCAmelCase							:						List[str]       							=					feature_extractor(A ,			truncation=A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(2, 1, 7_1_5_2_0)	)  # 2 chunks
  
           # force truncate to max_length
           _UpperCAmelCase							:						int       							=					feature_extractor(
               A ,			truncation=A ,			max_length=4_8_0_0_0 ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(2, 1, 4_8_0_0_0)	)
  
           # pad to chunk
           _UpperCAmelCase							:						List[str]       							=					feature_extractor(A ,			padding=A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(padded_outputs.shape ,			(2, 1, 9_5_2_8_0)	)
  
           # pad to chunk
           _UpperCAmelCase							:						Optional[int]       							=					feature_extractor(A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(2, 1, 9_5_2_8_0)	)
  
           # force pad to max length
           _UpperCAmelCase							:						List[Any]       							=					feature_extractor(
               A ,			padding='''max_length''' ,			max_length=1_0_0_0_0_0 ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(2, 1, 1_0_0_0_0_0)	)
  
           # force no pad
           with self.assertRaisesRegex(
               A ,			'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$''' ,			):
               _UpperCAmelCase							:						List[Any]       							=					feature_extractor(A ,			padding=A ,			return_tensors='''pt'''	).input_values
  
           _UpperCAmelCase							:						List[Any]       							=					feature_extractor(input_audio[0] ,			padding=A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(1, 1, 9_3_6_8_0)	)
  
           # no pad if no chunk_length_s
           _UpperCAmelCase							:						List[str]       							=					None
           with self.assertRaisesRegex(
               A ,			'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$''' ,			):
               _UpperCAmelCase							:						Union[str, Any]       							=					feature_extractor(A ,			padding=A ,			return_tensors='''pt'''	).input_values
  
           _UpperCAmelCase							:						List[Any]       							=					feature_extractor(input_audio[0] ,			padding=A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(1, 1, 9_3_6_8_0)	)
  
           # no pad if no overlap
           _UpperCAmelCase							:						str       							=					2
           _UpperCAmelCase							:						str       							=					None
           with self.assertRaisesRegex(
               A ,			'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$''' ,			):
               _UpperCAmelCase							:						str       							=					feature_extractor(A ,			padding=A ,			return_tensors='''pt'''	).input_values
  
           _UpperCAmelCase							:						Dict       							=					feature_extractor(input_audio[0] ,			padding=A ,			return_tensors='''pt'''	).input_values
           self.assertEquals(truncated_outputs.shape ,			(1, 1, 9_3_6_8_0)	)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	int							):
      sequence.append(UpperCamelCase__							)
  
      _UpperCAmelCase							:						str       							=					0
      while sequence[index] != target:
          index += 1
  
      sequence.pop()
  
      if index == len(UpperCamelCase__							):
          return None
  
      return index
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Dict  =      input('Enter numbers separated by comma:\n').strip()
    _lowerCAmelCase					:List[Any]  =      [int(item) for item in user_input.split(',')]
  
    _lowerCAmelCase					:int  =      input('Enter a single number to be found in the list:\n')
    _lowerCAmelCase					:List[Any]  =      int(target_input)
    _lowerCAmelCase					:Optional[int]  =      sentinel_linear_search(sequence, target)
    if result is not None:
      print(f"{target} found at positions: {result}")
    else:
      print('Not found')
  
  
  