


'''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


A   :  Any       		=				random.Random()






def    lowerCAmelCase__ (       lowerCamelCase  :    int						,lowerCamelCase  :    Union[str, Any]=1.0						,lowerCamelCase  :    str=None						,lowerCamelCase  :    int=None     ):
       if rng is None:
              _A  :			List[Any]											=      global_rng

       _A  :			Any											=      []
       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 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    def __init__(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=7   ,							SCREAMING_SNAKE_CASE    :							Any=400   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=2000   ,							SCREAMING_SNAKE_CASE    :							int=1   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=0.0   ,							SCREAMING_SNAKE_CASE    :							Tuple=24000   ,							SCREAMING_SNAKE_CASE    :							Any=True   ,							):
           _A  :			Optional[Any]											=      parent
           _A  :			int											=      batch_size
           _A  :			Dict											=      min_seq_length
           _A  :			List[str]											=      max_seq_length
           _A  :			str											=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A  :			int											=      feature_size
           _A  :			List[Any]											=      padding_value
           _A  :			Dict											=      sampling_rate
           _A  :			List[str]											=      return_attention_mask


    def      A					(						self    :							Optional[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      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							List[str]=False   ,							SCREAMING_SNAKE_CASE    :							str=False):
           def _flatten(SCREAMING_SNAKE_CASE    :							str):
                  return list(itertools.chain(*SCREAMING_SNAKE_CASE))

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

           return audio_inputs




@require_torch
class 				__lowerCamelCase				( a_       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					EncodecFeatureExtractor


    def      A					(						self    :							List[Any]):
           _A  :			Dict											=      EnCodecFeatureExtractionTester(self)


    def      A					(						self    :							List[str]):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           _A  :			str											=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           _A  :			Dict											=      [floats_list((1, x))[0] for x in range(800   ,							1400   ,							200)]
           _A  :			List[Any]											=      [np.asarray(SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

           # Test not batched input
           _A  :			List[Any]											=      feat_extract(audio_inputs[0]   ,							return_tensors='np').input_values
           _A  :			Dict											=      feat_extract(np_audio_inputs[0]   ,							return_tensors='np').input_values
           self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							atol=1e-3))

           # Test batched
           _A  :			List[Any]											=      feat_extract(SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='np').input_values
           _A  :			str											=      feat_extract(SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='np').input_values
           for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							atol=1e-3))


    def      A					(						self    :							Tuple):
           _A  :			str											=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A  :			Any											=      np.random.rand(100).astype(np.floataa)
           _A  :			List[str]											=      np_audio_inputs.tolist()

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


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]):
           from datasets import load_dataset

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

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


    def      A					(						self    :							int):
           # fmt: off
           _A  :			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
           _A  :			Tuple											=      self._load_datasamples(1)
           _A  :			int											=      EncodecFeatureExtractor()
           _A  :			str											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(input_values.shape   ,							(1, 1, 93680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,							SCREAMING_SNAKE_CASE   ,							atol=1e-6))


    def      A					(						self    :							Tuple):
           # fmt: off
           _A  :			List[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
           _A  :			int											=      self._load_datasamples(1)
           _A  :			Optional[Any]											=      [np.tile(input_audio[0][None]   ,							reps=(2, 1))]
           input_audio[0][1] *= 0.5
           _A  :			int											=      EncodecFeatureExtractor(feature_size=2)
           _A  :			Dict											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(input_values.shape   ,							(1, 2, 93680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,							SCREAMING_SNAKE_CASE   ,							atol=1e-6))
           self.assertTrue(torch.allclose(input_values[0, 1, :30]   ,							EXPECTED_INPUT_VALUES * 0.5   ,							atol=1e-6))


    def      A					(						self    :							int):
           _A  :			Optional[Any]											=      self._load_datasamples(2)
           # would be easier if the stride was like
           _A  :			int											=      EncodecFeatureExtractor(feature_size=1   ,							chunk_length_s=1   ,							overlap=0.01)

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE   ,							'^Both padding and truncation were set. Make sure you only set one.$'   ,							):
                  _A  :			List[Any]											=      feature_extractor(
                      SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							truncation=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values

           # truncate to chunk
           _A  :			Tuple											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							truncation=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(2, 1, 71520))  # 2 chunks

           # force truncate to max_length
           _A  :			Dict											=      feature_extractor(
               SCREAMING_SNAKE_CASE   ,							truncation=SCREAMING_SNAKE_CASE   ,							max_length=48000   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(2, 1, 48000))

           # pad to chunk
           _A  :			str											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(padded_outputs.shape   ,							(2, 1, 95280))

           # pad to chunk
           _A  :			Optional[Any]											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(2, 1, 95280))

           # force pad to max length
           _A  :			str											=      feature_extractor(
               SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							max_length=100000   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(2, 1, 100000))

           # force no pad
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE   ,							'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'   ,							):
                  _A  :			Union[str, Any]											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values

           _A  :			List[str]											=      feature_extractor(input_audio[0]   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(1, 1, 93680))

           # no pad if no chunk_length_s
           _A  :			str											=      None
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE   ,							'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'   ,							):
                  _A  :			List[str]											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values

           _A  :			Optional[Any]											=      feature_extractor(input_audio[0]   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(1, 1, 93680))

           # no pad if no overlap
           _A  :			Optional[int]											=      2
           _A  :			Union[str, Any]											=      None
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE   ,							'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'   ,							):
                  _A  :			Union[str, Any]											=      feature_extractor(SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values

           _A  :			Union[str, Any]											=      feature_extractor(input_audio[0]   ,							padding=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape   ,							(1, 1, 93680))










'''simple docstring'''







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))






