





"""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       	=     random.Random()

def 			__A					(	a_  :str      ,				a_  :int=1.0      ,				a_  :List[Any]=None      ,				a_  :Dict=None)						->						Tuple:
				if rng is None:
								__a       :  Optional[int]					=     global_rng

				__a       :  Union[str, Any]					=     []
				for batch_idx in range(shape[0]):
								values.append([])
								for _ in range(shape[1]):
												values[-1].append(rng.random() * scale)

				return values




class  __lowercase	(	unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,	_UpperCAmelCase		,	_UpperCAmelCase=7		,	_UpperCAmelCase=400		,	_UpperCAmelCase=2000		,	_UpperCAmelCase=2048		,	_UpperCAmelCase=128		,	_UpperCAmelCase=1		,	_UpperCAmelCase=512		,	_UpperCAmelCase=30		,	_UpperCAmelCase=44100		,	):
									__a       :  Union[str, Any]					=     parent
									__a       :  Optional[int]					=     batch_size
									__a       :  Dict					=     min_seq_length
									__a       :  Optional[Any]					=     max_seq_length
									__a       :  Tuple					=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__a       :  Any					=     spectrogram_length
									__a       :  Tuple					=     feature_size
									__a       :  List[str]					=     num_audio_channels
									__a       :  int					=     hop_length
									__a       :  Optional[Any]					=     chunk_length
									__a       :  List[Any]					=     sampling_rate







					def        _lowerCamelCase						(  self   ):
									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        _lowerCamelCase						(  self		,	_UpperCAmelCase=False		,	_UpperCAmelCase=False   ):







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

									if equal_length:
													__a       :  Tuple					=     [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
									else:
													# make sure that inputs increase in size
													__a       :  str					=     [
													    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       :  Dict					=     [np.asarray(_UpperCAmelCase   ) for x in speech_inputs]
									return speech_inputs






@require_torch
@require_torchaudio
class  __lowercase	(	_UpperCamelCase      ,					unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase     =     TvltFeatureExtractor







					def        _lowerCamelCase						(  self   ):
									__a       :  Union[str, Any]					=     TvltFeatureExtractionTester(self   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Tuple					=     self.feature_extraction_class(**self.feat_extract_dict   )
									self.assertTrue(hasattr(_UpperCAmelCase		,	'''spectrogram_length'''   )   )
									self.assertTrue(hasattr(_UpperCAmelCase		,	'''feature_size'''   )   )
									self.assertTrue(hasattr(_UpperCAmelCase		,	'''num_audio_channels'''   )   )
									self.assertTrue(hasattr(_UpperCAmelCase		,	'''hop_length'''   )   )
									self.assertTrue(hasattr(_UpperCAmelCase		,	'''chunk_length'''   )   )
									self.assertTrue(hasattr(_UpperCAmelCase		,	'''sampling_rate'''   )   )







					def        _lowerCamelCase						(  self   ):
									__a       :  List[str]					=     self.feature_extraction_class(**self.feat_extract_dict   )

									with tempfile.TemporaryDirectory() as tmpdirname:
													__a       :  List[Any]					=     feat_extract_first.save_pretrained(_UpperCAmelCase   )[0]
													check_json_file_has_correct_format(_UpperCAmelCase   )
													__a       :  Optional[int]					=     self.feature_extraction_class.from_pretrained(_UpperCAmelCase   )

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







					def        _lowerCamelCase						(  self   ):
									__a       :  str					=     self.feature_extraction_class(**self.feat_extract_dict   )

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

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







					def        _lowerCamelCase						(  self   ):
									__a       :  List[str]					=     self.feature_extraction_class(**self.feat_extract_dict   )

									# create three inputs of length 800, 1000, and 1200
									__a       :  Any					=     [floats_list((1, x)   )[0] for x in range(800		,	1400		,	200   )]
									__a       :  Optional[int]					=     [np.asarray(_UpperCAmelCase   ) for speech_input in speech_inputs]

									# Test not batched input
									__a       :  Tuple					=     feature_extractor(np_speech_inputs[0]		,	return_tensors='''np'''		,	sampling_rate=44100   ).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       :  List[str]					=     feature_extractor(_UpperCAmelCase		,	return_tensors='''np'''		,	sampling_rate=44100   ).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       :  Optional[Any]					=     feature_extractor(
									    _UpperCAmelCase		,	return_tensors='''np'''		,	sampling_rate=44100		,	mask_audio=_UpperCAmelCase   ).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       :  Optional[int]					=     [floats_list((1, x)   )[0] for x in (800, 800, 800)]
									__a       :  Any					=     np.asarray(_UpperCAmelCase   )
									__a       :  Tuple					=     feature_extractor(_UpperCAmelCase		,	return_tensors='''np'''		,	sampling_rate=44100   ).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        _lowerCamelCase						(  self		,	_UpperCAmelCase   ):
									__a       :  Union[str, Any]					=     load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,	'''clean'''		,	split='''validation'''   )
									# automatic decoding with librispeech
									__a       :  List[str]					=     ds.sort('''id'''   ).select(range(_UpperCAmelCase   )   )[:num_samples]['''audio''']

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[int]					=     self._load_datasamples(1   )
									__a       :  int					=     TvltFeatureExtractor()
									__a       :  Any					=     feature_extractor(_UpperCAmelCase		,	return_tensors='''pt'''   ).audio_values

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

									__a       :  Any					=     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]		,	_UpperCAmelCase		,	atol=1e-4   )   )





from __future__ import annotations

from collections.abc import Iterator

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							value
								a										=							None
								a										=							None







class 		__lowerCAmelCase	:
							def __init__(						self						:str		,      __magic_name__						:Node				):







								'''simple docstring'''







								a										=							tree
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Node | None				):







								'''simple docstring'''







								if node is None:
									return 0
								return node.value + (
								    self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
								)





							def __iter__(						self						:Tuple				):







								'''simple docstring'''







								yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

