



"""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)	)







"""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')


