





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

lowercase				:	Tuple      						=  random.Random()



def A_     (		A__		,				A__=1.0		,				A__=None		,				A__=None    )  ->		List[str]:
							if rng is None:
														a__						:     Dict			=    global_rng

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

							return values





class 			A__   (							unittest.TestCase     ):




			"""simple docstring"""



			def __init__( self ,  lowercase ,  lowercase=7 ,  lowercase=400 ,  lowercase=2000 ,  lowercase=2048 ,  lowercase=128 ,  lowercase=1 ,  lowercase=512 ,  lowercase=30 ,  lowercase=4_4100 ,  )      ->						Tuple:







										'''simple docstring'''

										a__						:     Dict			=    parent
										a__						:     Tuple			=    batch_size
										a__						:     Optional[Any]			=    min_seq_length
										a__						:     Dict			=    max_seq_length
										a__						:     List[Any]			=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										a__						:     int			=    spectrogram_length
										a__						:     Optional[Any]			=    feature_size
										a__						:     List[Any]			=    num_audio_channels
										a__						:     str			=    hop_length
										a__						:     str			=    chunk_length
										a__						:     Any			=    sampling_rate

			def 							__lowercase		( self)      ->						List[str]:







										'''simple docstring'''

										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 ,  lowercase=False ,  lowercase=False)      ->						int:







										'''simple docstring'''

										def _flatten(lowercase):
																	return list(itertools.chain(*lowercase))

										if equal_length:
																	a__						:     int			=    [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
										else:
																	# make sure that inputs increase in size
																	a__						:     List[Any]			=    [
																	    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__						:     Union[str, Any]			=    [np.asarray(lowercase) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




			"""simple docstring"""



			__A       :    List[str]	  =  TvltFeatureExtractor

			def 							__lowercase		( self)      ->						List[Any]:







										'''simple docstring'''

										a__						:     str			=    TvltFeatureExtractionTester(self)

			def 							__lowercase		( self)      ->						int:







										'''simple docstring'''

										a__						:     Union[str, Any]			=    self.feature_extraction_class(**self.feat_extract_dict)
										self.assertTrue(hasattr(lowercase ,  'spectrogram_length'))
										self.assertTrue(hasattr(lowercase ,  'feature_size'))
										self.assertTrue(hasattr(lowercase ,  'num_audio_channels'))
										self.assertTrue(hasattr(lowercase ,  'hop_length'))
										self.assertTrue(hasattr(lowercase ,  'chunk_length'))
										self.assertTrue(hasattr(lowercase ,  'sampling_rate'))

			def 							__lowercase		( self)      ->						str:







										'''simple docstring'''

										a__						:     Any			=    self.feature_extraction_class(**self.feat_extract_dict)

										with tempfile.TemporaryDirectory() as tmpdirname:
																	a__						:     Optional[int]			=    feat_extract_first.save_pretrained(lowercase)[0]
																	check_json_file_has_correct_format(lowercase)
																	a__						:     str			=    self.feature_extraction_class.from_pretrained(lowercase)

										a__						:     List[str]			=    feat_extract_first.to_dict()
										a__						:     int			=    feat_extract_second.to_dict()
										a__						:     Dict			=    dict_first.pop('mel_filters')
										a__						:     List[Any]			=    dict_second.pop('mel_filters')
										self.assertTrue(np.allclose(lowercase ,  lowercase))
										self.assertEqual(lowercase ,  lowercase)

			def 							__lowercase		( self)      ->						List[str]:







										'''simple docstring'''

										a__						:     List[str]			=    self.feature_extraction_class(**self.feat_extract_dict)

										with tempfile.TemporaryDirectory() as tmpdirname:
																	a__						:     Optional[int]			=    os.path.join(lowercase ,  'feat_extract.json')
																	feat_extract_first.to_json_file(lowercase)
																	a__						:     Any			=    self.feature_extraction_class.from_json_file(lowercase)

										a__						:     Tuple			=    feat_extract_first.to_dict()
										a__						:     Optional[Any]			=    feat_extract_second.to_dict()
										a__						:     Optional[Any]			=    dict_first.pop('mel_filters')
										a__						:     Optional[int]			=    dict_second.pop('mel_filters')
										self.assertTrue(np.allclose(lowercase ,  lowercase))
										self.assertEqual(lowercase ,  lowercase)

			def 							__lowercase		( self)      ->						Any:







										'''simple docstring'''

										a__						:     Optional[Any]			=    self.feature_extraction_class(**self.feat_extract_dict)

										# create three inputs of length 800, 1000, and 1200
										a__						:     Tuple			=    [floats_list((1, x))[0] for x in range(800 ,  1400 ,  200)]
										a__						:     List[str]			=    [np.asarray(lowercase) for speech_input in speech_inputs]

										# Test not batched input
										a__						:     Tuple			=    feature_extractor(np_speech_inputs[0] ,  return_tensors='np' ,  sampling_rate=4_4100).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
										a__						:     Dict			=    feature_extractor(lowercase ,  return_tensors='np' ,  sampling_rate=4_4100).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
										a__						:     Tuple			=    feature_extractor(
										    lowercase ,  return_tensors='np' ,  sampling_rate=4_4100 ,  mask_audio=lowercase).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.
										a__						:     List[Any]			=    [floats_list((1, x))[0] for x in (800, 800, 800)]
										a__						:     Optional[Any]			=    np.asarray(lowercase)
										a__						:     Dict			=    feature_extractor(lowercase ,  return_tensors='np' ,  sampling_rate=4_4100).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 ,  lowercase)      ->						Union[str, Any]:







										'''simple docstring'''

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

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

			def 							__lowercase		( self)      ->						Optional[Any]:







										'''simple docstring'''

										a__						:     List[str]			=    self._load_datasamples(1)
										a__						:     Dict			=    TvltFeatureExtractor()
										a__						:     List[Any]			=    feature_extractor(lowercase ,  return_tensors='pt').audio_values

										self.assertEquals(audio_values.shape ,  (1, 1, 192, 128))

										a__						:     Optional[Any]			=    torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]])
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,  lowercase ,  atol=1e-4))









def A_     (		A__    )  ->		List[str]:  # noqa: E741
       a__						:     Dict			=    len(A__    )
       a__						:     str			=    0
       a__						:     Any			=    [0] * n
       a__						:     int			=    [False] * n
       a__						:     Optional[Any]			=    [False] * n

       def dfs(A__		,				A__		,				A__		,				A__    ):
              if parent == root:
                     out_edge_count += 1
              a__						:     Union[str, Any]			=    True
              a__						:     Optional[Any]			=    at

              for to in l[at]:
                     if to == parent:
                            pass
                     elif not visited[to]:
                            a__						:     List[Any]			=    dfs(A__		,				A__		,				A__		,				A__    )
                            a__						:     Dict			=    min(low[at]		,				low[to]    )

                            # AP found via bridge
                            if at < low[to]:
                                   a__						:     Dict			=    True
                            # AP found via cycle
                            if at == low[to]:
                                   a__						:     List[Any]			=    True
                     else:
                            a__						:     Optional[int]			=    min(low[at]		,				A__    )
              return out_edge_count

       for i in range(A__    ):
              if not visited[i]:
                     a__						:     Tuple			=    0
                     a__						:     Any			=    dfs(A__		,				A__		,				-1		,				A__    )
                     a__						:     List[Any]			=    out_edge_count > 1

       for x in range(len(A__    )    ):
              if is_art[x] is True:
                     print(A__    )


# Adjacency list of graph
lowercase				:	List[Any]      						=  {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)


