


'''simple docstring'''



import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

A__						:   Any				=    random.Random()



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Dict=1.0 ,       UpperCAmelCase_       :							str=None ,       UpperCAmelCase_       :							List[Any]=None				)	->						Union[str, Any]:
     if rng is None:
          __lowerCamelCase  :      Any														=						global_rng

     __lowerCamelCase  :      List[Any]														=						[]
     for batch_idx in range(shape[0]				):
          values.append([]				)
          for _ in range(shape[1]				):
               values[-1].append(rng.random() * scale				)

     return values





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=20_00     ,	SCREAMING_SNAKE_CASE_=20_48     ,	SCREAMING_SNAKE_CASE_=1_28     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=4_41_00     ,	)							->	str:
         __lowerCamelCase  :      Dict														=						parent
         __lowerCamelCase  :      Union[str, Any]														=						batch_size
         __lowerCamelCase  :      Any														=						min_seq_length
         __lowerCamelCase  :      List[Any]														=						max_seq_length
         __lowerCamelCase  :      List[Any]														=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __lowerCamelCase  :      Any														=						spectrogram_length
         __lowerCamelCase  :      Any														=						feature_size
         __lowerCamelCase  :      List[str]														=						num_audio_channels
         __lowerCamelCase  :      Dict														=						hop_length
         __lowerCamelCase  :      Optional[Any]														=						chunk_length
         __lowerCamelCase  :      Optional[Any]														=						sampling_rate







    def  lowercase_							(				self      )							->	List[str]:
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         def _flatten(SCREAMING_SNAKE_CASE_      ):
              return list(itertools.chain(*SCREAMING_SNAKE_CASE_      )      )

         if equal_length:
              __lowerCamelCase  :      Tuple														=						[floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
         else:
              # make sure that inputs increase in size
              __lowerCamelCase  :      Dict														=						[
                  floats_list((x, self.feature_size)      )
                  for x in range(self.min_seq_length     ,	self.max_seq_length     ,	self.seq_length_diff      )
              ]
         if numpify:
              __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     TvltFeatureExtractor







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						TvltFeatureExtractionTester(self      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Tuple														=						self.feature_extraction_class(**self.feat_extract_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'spectrogram_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'feature_size'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'num_audio_channels'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'hop_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'chunk_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'sampling_rate'      )      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      List[str]														=						feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_      )[0]
              check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[int]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      List[Any]														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      int														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	'feat_extract.json'      )
              feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Any														=						self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[Any]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      str														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[str]:
         # Initialize feature_extractor
         __lowerCamelCase  :      Dict														=						self.feature_extraction_class(**self.feat_extract_dict      )

         # create three inputs of length 800, 1000, and 1200
         __lowerCamelCase  :      int														=						[floats_list((1, x)      )[0] for x in range(8_00     ,	14_00     ,	2_00      )]
         __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for speech_input in speech_inputs]

         # Test not batched input
         __lowerCamelCase  :      Union[str, Any]														=						feature_extractor(np_speech_inputs[0]     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test batched
         __lowerCamelCase  :      Any														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test audio masking
         __lowerCamelCase  :      Dict														=						feature_extractor(
             SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00     ,	mask_audio=SCREAMING_SNAKE_CASE_      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test 2-D numpy arrays are batched.
         __lowerCamelCase  :      Optional[Any]														=						[floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
         __lowerCamelCase  :      int														=						np.asarray(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values
         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,	'clean'     ,	split='validation'      )
         # automatic decoding with librispeech
         __lowerCamelCase  :      Union[str, Any]														=						ds.sort('id'      ).select(range(SCREAMING_SNAKE_CASE_      )      )[:num_samples]['audio']

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







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						self._load_datasamples(1      )
         __lowerCamelCase  :      List[Any]														=						TvltFeatureExtractor()
         __lowerCamelCase  :      List[str]														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).audio_values

         self.assertEquals(audio_values.shape     ,	(1, 1, 1_92, 1_28)      )

         __lowerCamelCase  :      str														=						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]      )
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )










from copy import deepcopy
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:int     ,		_a:list[int] | None = None     ,		_a:int | None = None   ):

       if arr is None and size is not None:
         snake_case__						= size
         snake_case__						= [0] * size
       elif arr is not None:
         self.init(_a   )
       else:
         raise ValueError('''Either arr or size must be specified'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:list[int]   ):
       snake_case__						= len(_a   )
       snake_case__						= deepcopy(_a   )
       for i in range(1     ,		self.size   ):
         snake_case__						= self.next_(_a   )
         if j < self.size:
           self.tree[j] += self.tree[i]

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.tree[:]
       for i in range(self.size - 1     ,		0     ,		-1   ):
         snake_case__						= self.next_(_a   )
         if j < self.size:
           arr[j] -= arr[i]
       return arr

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return index + (index & (-index))

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return index - (index & (-index))

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int     ,		_a:int   ):
       if index == 0:
         self.tree[0] += value
         return
       while index < self.size:
         self.tree[index] += value
         snake_case__						= self.next_(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:int   ):
       self.add(_a     ,		value - self.get(_a   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int   ):
       if right == 0:
         return 0
       snake_case__						= self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
         result += self.tree[right]
         snake_case__						= self.prev(_a   )
       return result

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:int   ):
       return self.prefix(_a   ) - self.prefix(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int   ):
       return self.query(_a     ,		index + 1   )







     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int   ):
       value -= self.tree[0]
       if value < 0:
         return -1

       snake_case__						= 1  # Largest power of 2 <= size
       while j * 2 < self.size:
         j *= 2

       snake_case__						= 0

       while j > 0:
         if i + j < self.size and self.tree[i + j] <= value:
           value -= self.tree[i + j]
           i += j
         j //= 2
       return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()

