


'''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

__snake_case											=					random.Random()


def       a				(      __a			, __a=1.0			, __a=None			, __a=None       )  ->     Any:



					'''simple docstring'''






					if rng is None:
										UpperCamelCase__       :Dict							 =			global_rng

					UpperCamelCase__       :Optional[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   ,       ):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''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    lowerCAmelCase__    (		self   ,       UpperCamelCase_=False   ,       UpperCamelCase_=False							):

							'''simple docstring'''


							def _flatten(UpperCamelCase_							):
												return list(itertools.chain(*_a							)							)

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



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

		"""simple docstring"""

		_a   			=    TvltFeatureExtractor




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			TvltFeatureExtractionTester(self							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.feature_extraction_class(**self.feat_extract_dict							)
							self.assertTrue(hasattr(_a   ,       '''spectrogram_length'''							)							)
							self.assertTrue(hasattr(_a   ,       '''feature_size'''							)							)
							self.assertTrue(hasattr(_a   ,       '''num_audio_channels'''							)							)
							self.assertTrue(hasattr(_a   ,       '''hop_length'''							)							)
							self.assertTrue(hasattr(_a   ,       '''chunk_length'''							)							)
							self.assertTrue(hasattr(_a   ,       '''sampling_rate'''							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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

							with tempfile.TemporaryDirectory() as tmpdirname:
												UpperCamelCase__       :int							 =			feat_extract_first.save_pretrained(_a							)[0]
												check_json_file_has_correct_format(_a							)
												UpperCamelCase__       :Dict							 =			self.feature_extraction_class.from_pretrained(_a							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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

							with tempfile.TemporaryDirectory() as tmpdirname:
												UpperCamelCase__       :Optional[int]							 =			os.path.join(_a   ,       '''feat_extract.json'''							)
												feat_extract_first.to_json_file(_a							)
												UpperCamelCase__       :List[str]							 =			self.feature_extraction_class.from_json_file(_a							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.feature_extraction_class(**self.feat_extract_dict							)

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

							# Test not batched input
							UpperCamelCase__       :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
							UpperCamelCase__       :Dict							 =			feature_extractor(_a   ,       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
							UpperCamelCase__       :Union[str, Any]							 =			feature_extractor(
							    _a   ,       return_tensors='''np'''   ,       sampling_rate=44100   ,       mask_audio=_a							).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.
							UpperCamelCase__       :Optional[Any]							 =			[floats_list((1, x)							)[0] for x in (800, 800, 800)]
							UpperCamelCase__       :int							 =			np.asarray(_a							)
							UpperCamelCase__       :Tuple							 =			feature_extractor(_a   ,       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_							):

							'''simple docstring'''


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

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self._load_datasamples(1							)
							UpperCamelCase__       :int							 =			TvltFeatureExtractor()
							UpperCamelCase__       :Union[str, Any]							 =			feature_extractor(_a   ,       return_tensors='''pt'''							).audio_values

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

							UpperCamelCase__       :Union[str, Any]							 =			torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]							)
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,       _a   ,       atol=1e-4							)							)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import _LazyModule


__snake_case											=					{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)