




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

_lowerCamelCase      :  List[str]      						=						random.Random()




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : str=1.0      ,    UpperCamelCase_ : Optional[int]=None      ,    UpperCamelCase_ : Optional[int]=None       ):
				'''simple docstring'''
				if rng is None:
								_lowerCAmelCase    :    List[Any]										=      global_rng

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

				return values
class        __snake_case  (unittest.TestCase		):



				def __init__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]=7		,	_UpperCAmelCase      :		Dict=400		,	_UpperCAmelCase      :		Optional[int]=2000		,	_UpperCAmelCase      :		Optional[Any]=2048		,	_UpperCAmelCase      :		Tuple=128		,	_UpperCAmelCase      :		Dict=1		,	_UpperCAmelCase      :		Any=512		,	_UpperCAmelCase      :		Dict=30		,	_UpperCAmelCase      :		Dict=4_4100		,	)  ->    Dict:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[Any]:





								'''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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Any=False		,	_UpperCAmelCase      :		Union[str, Any]=False      )  ->    Optional[Any]:





								'''simple docstring'''




								def _flatten(_UpperCAmelCase      :		Union[str, Any]      ):
												return list(itertools.chain(*_UpperCAmelCase      )      )

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






@require_torch
@require_torchaudio
class        __snake_case  (_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     TvltFeatureExtractor



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





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      TvltFeatureExtractionTester(self      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Tuple:





								'''simple docstring'''




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

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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.feature_extraction_class(**self.feat_extract_dict      )

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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




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

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

								# Test not batched input
								_lowerCAmelCase    :    Optional[int]										=      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
								_lowerCAmelCase    :    Tuple										=      feature_extractor(_UpperCAmelCase		,	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
								_lowerCAmelCase    :    Optional[Any]										=      feature_extractor(
								    _UpperCAmelCase		,	return_tensors="""np"""		,	sampling_rate=4_4100		,	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.
								_lowerCAmelCase    :    str										=      [floats_list((1, x)      )[0] for x in (800, 800, 800)]
								_lowerCAmelCase    :    str										=      np.asarray(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      feature_extractor(_UpperCAmelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		List[Any]      )  ->    Dict:





								'''simple docstring'''




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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self._load_datasamples(1      )
								_lowerCAmelCase    :    Dict										=      TvltFeatureExtractor()
								_lowerCAmelCase    :    str										=      feature_extractor(_UpperCAmelCase		,	return_tensors="""pt"""      ).audio_values

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

								_lowerCAmelCase    :    str										=      torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]      )
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,	_UpperCAmelCase		,	atol=1E-4      )      )







import logging

from transformers.configuration_utils import PretrainedConfig


_lowerCamelCase      :  Union[str, Any]      						=						logging.getLogger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     "masked_bert"



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Union[str, Any]=3_0522		,	_UpperCAmelCase      :		Optional[Any]=768		,	_UpperCAmelCase      :		List[Any]=12		,	_UpperCAmelCase      :		Dict=12		,	_UpperCAmelCase      :		List[str]=3072		,	_UpperCAmelCase      :		List[Any]="gelu"		,	_UpperCAmelCase      :		Optional[Any]=0.1		,	_UpperCAmelCase      :		Optional[Any]=0.1		,	_UpperCAmelCase      :		List[Any]=512		,	_UpperCAmelCase      :		int=2		,	_UpperCAmelCase      :		Optional[int]=0.02		,	_UpperCAmelCase      :		str=1E-12		,	_UpperCAmelCase      :		Tuple=0		,	_UpperCAmelCase      :		Dict="topK"		,	_UpperCAmelCase      :		List[str]="constant"		,	_UpperCAmelCase      :		Optional[Any]=0.0		,	**_UpperCAmelCase      :		str		,	)  ->    Tuple:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase		,	**_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      vocab_size
								_lowerCAmelCase    :    List[str]										=      hidden_size
								_lowerCAmelCase    :    Any										=      num_hidden_layers
								_lowerCAmelCase    :    Optional[Any]										=      num_attention_heads
								_lowerCAmelCase    :    Optional[Any]										=      hidden_act
								_lowerCAmelCase    :    Union[str, Any]										=      intermediate_size
								_lowerCAmelCase    :    Tuple										=      hidden_dropout_prob
								_lowerCAmelCase    :    Optional[int]										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    str										=      max_position_embeddings
								_lowerCAmelCase    :    int										=      type_vocab_size
								_lowerCAmelCase    :    List[str]										=      initializer_range
								_lowerCAmelCase    :    Optional[Any]										=      layer_norm_eps
								_lowerCAmelCase    :    Tuple										=      pruning_method
								_lowerCAmelCase    :    str										=      mask_init
								_lowerCAmelCase    :    List[str]										=      mask_scale

