




'''simple docstring'''







class   _snake_case:






 def __init__(self					:     str						)					->      None:






    """simple docstring"""


    A__         =    {}
    # indexmap keys are an index and it's values are the number at that index
    A__         =    {}






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     list | str | range						,		a					:     int						)					->      list[int]:






    """simple docstring"""


    if isinstance(a						,		(range, str)						):
       A__         =    list(a						)
    elif not isinstance(a						,		a						):
       raise TypeError(
           'binary_search_delete() only accepts either a list, range or str'						)

    A__         =    0
    A__         =    len(a						) - 1

    while low <= high:
       A__         =    (low + high) // 2
       if array[mid] == item:
          array.pop(a						)
          return array
       elif array[mid] < item:
          A__         =    mid + 1
       else:
          A__         =    mid - 1
    raise ValueError(
        'Either the item is not in the array or the array was unsorted'						)






 def 	_UpperCamelCase   (self					:     Tuple						,		a					:     list | str | range						,		a					:     int						)					->      list[int]:






    """simple docstring"""


    if isinstance(a						,		(range, str)						):
       A__         =    list(a						)
    elif not isinstance(a						,		a						):
       raise TypeError(
           'binary_search_insert() only accepts either a list, range or str'						)

    A__         =    0
    A__         =    len(a						) - 1

    while low <= high:
       A__         =    (low + high) // 2
       if array[mid] == index:
          # If the item already exists in the array,
          # insert it after the existing item
          array.insert(mid + 1						,		a						)
          return array
       elif array[mid] < index:
          A__         =    mid + 1
       else:
          A__         =    mid - 1

       # If the item doesn't exist in the array, insert it at the appropriate position
    array.insert(a						,		a						)
    return array






 def 	_UpperCamelCase   (self					:     int						,		a					:     int						,		a					:     int						)					->      None:






    """simple docstring"""


    if index in self.indexmap:
       A__         =    self.indexmap[index]
       if len(self.numbermap[n]						) == 1:
          del self.numbermap[n]
       else:
          A__         =    self.binary_search_delete(self.numbermap[n]						,		a						)

       # Set new index
    A__         =    number

    # Number not seen before or empty so insert number value
    if number not in self.numbermap:
       A__         =    [index]

    # Here we need to perform a binary search insertion in order to insert
    # The item in the correct place
    else:
       A__         =    self.binary_search_insert(
           self.numbermap[number]						,		a						)






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






    """simple docstring"""


    return self.numbermap.get(a						,		[-1]						)[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()








'''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_    	=							random.Random()







def 	_A			(   UpperCAmelCase			,UpperCAmelCase=1.0			,UpperCAmelCase=None			,UpperCAmelCase=None	):






   '''simple docstring'''







   if rng is None:
      A__         =    global_rng

   A__         =    []
   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   _snake_case( unittest.TestCase			):






 def __init__(self					:     int						,		a					:     str						,		a					:     Optional[int]=7						,		a					:     Optional[Any]=4_00						,		a					:     Any=20_00						,		a					:     Optional[Any]=1						,		a					:     Optional[int]=0.0						,		a					:     Dict=2_40_00						,		a					:     Tuple=True						,		)					->      Optional[Any]:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     int						)					->      List[str]:






    """simple docstring"""


    return {
        "feature_size": self.feature_size,
        "padding_value": self.padding_value,
        "sampling_rate": self.sampling_rate,
        "return_attention_mask": self.return_attention_mask,
    }






 def 	_UpperCamelCase   (self					:     List[Any]						,		a					:     Any=False						,		a					:     Optional[Any]=False						)					->      Tuple:






    """simple docstring"""


    def _flatten(a					:     Optional[Any]						):
       return list(itertools.chain(*a						)						)

    if equal_length:
       A__         =    floats_list((self.batch_size, self.max_seq_length)						)
    else:
       # make sure that inputs increase in size
       A__         =    [
           _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:
       A__         =    [np.asarray(a						) for x in audio_inputs]

    return audio_inputs




@require_torch
class   _snake_case( UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       Union[str, Any]									=				EncodecFeatureExtractor






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






    """simple docstring"""


    A__         =    EnCodecFeatureExtractionTester(self						)






 def 	_UpperCamelCase   (self					:     Dict						)					->      Optional[Any]:






    """simple docstring"""


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

    # Test not batched input
    A__         =    feat_extract(audio_inputs[0]						,		return_tensors='np'						).input_values
    A__         =    feat_extract(np_audio_inputs[0]						,		return_tensors='np'						).input_values
    self.assertTrue(np.allclose(a						,		a						,		atol=1e-3						)						)

    # Test batched
    A__         =    feat_extract(a						,		padding=a						,		return_tensors='np'						).input_values
    A__         =    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 	_UpperCamelCase   (self					:     Tuple						)					->      Any:






    """simple docstring"""


    A__         =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
    A__         =    np.random.rand(1_00						).astype(np.floataa						)
    A__         =    np_audio_inputs.tolist()

    for inputs in [py_audio_inputs, np_audio_inputs]:
       A__         =    feature_extractor.pad([{'input_values': inputs}]						,		return_tensors='np'						)
       self.assertTrue(np_processed.input_values.dtype == np.floataa						)
       A__         =    feature_extractor.pad([{'input_values': inputs}]						,		return_tensors='pt'						)
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)






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






    """simple docstring"""


    from datasets import load_dataset

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

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






 def 	_UpperCamelCase   (self					:     Tuple						)					->      Tuple:






    """simple docstring"""


    A__         =    torch.tensor(
        [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
         3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
         2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
         4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
         7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
         4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]						)
    # fmt: on
    A__         =    self._load_datasamples(1						)
    A__         =    EncodecFeatureExtractor()
    A__         =    feature_extractor(a						,		return_tensors='pt'						).input_values
    self.assertEquals(input_values.shape						,		(1, 1, 9_36_80)						)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]						,		a						,		atol=1e-6						)						)






 def 	_UpperCamelCase   (self					:     int						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    torch.tensor(
        [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
         3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
         2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
         4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
         7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
         4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]						)
    # fmt: on
    A__         =    self._load_datasamples(1						)
    A__         =    [np.tile(input_audio[0][None]						,		reps=(2, 1)						)]
    input_audio[0][1] *= 0.5
    A__         =    EncodecFeatureExtractor(feature_size=2						)
    A__         =    feature_extractor(a						,		return_tensors='pt'						).input_values
    self.assertEquals(input_values.shape						,		(1, 2, 9_36_80)						)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]						,		a						,		atol=1e-6						)						)
    self.assertTrue(torch.allclose(input_values[0, 1, :30]						,		EXPECTED_INPUT_VALUES * 0.5						,		atol=1e-6						)						)






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






    """simple docstring"""


    A__         =    self._load_datasamples(2						)
    # would be easier if the stride was like
    A__         =    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.$'						,		):
       A__         =    feature_extractor(
           a						,		padding='max_length'						,		truncation=a						,		return_tensors='pt'						).input_values

    # truncate to chunk
    A__         =    feature_extractor(a						,		truncation=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 7_15_20)						)  # 2 chunks

    # force truncate to max_length
    A__         =    feature_extractor(
        a						,		truncation=a						,		max_length=4_80_00						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 4_80_00)						)

    # pad to chunk
    A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(padded_outputs.shape						,		(2, 1, 9_52_80)						)

    # pad to chunk
    A__         =    feature_extractor(a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 9_52_80)						)

    # force pad to max length
    A__         =    feature_extractor(
        a						,		padding='max_length'						,		max_length=10_00_00						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 10_00_00)						)

    # 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.$'						,		):
       A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values

    A__         =    feature_extractor(input_audio[0]						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(1, 1, 9_36_80)						)

    # no pad if no chunk_length_s
    A__         =    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.$'						,		):
       A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values

    A__         =    feature_extractor(input_audio[0]						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(1, 1, 9_36_80)						)

    # no pad if no overlap
    A__         =    2
    A__         =    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.$'						,		):
       A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values

    A__         =    feature_extractor(input_audio[0]						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(1, 1, 9_36_80)						)


