


"""simple docstring"""






import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
					from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class      A:

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=13					,				SCREAMING_SNAKE_CASE__=30					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=5					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=37					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=10					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=2					,				)      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	parent
					_UpperCamelCase					:int					     =	batch_size
					_UpperCamelCase					:Dict					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	patch_size
					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Optional[Any]					     =	is_training
					_UpperCamelCase					:Union[str, Any]					     =	use_labels
					_UpperCamelCase					:Optional[int]					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	num_hidden_layers
					_UpperCamelCase					:Dict					     =	num_attention_heads
					_UpperCamelCase					:Tuple					     =	intermediate_size
					_UpperCamelCase					:List[str]					     =	hidden_act
					_UpperCamelCase					:Any					     =	hidden_dropout_prob
					_UpperCamelCase					:List[Any]					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	type_sequence_label_size
					_UpperCamelCase					:Optional[int]					     =	initializer_range
					_UpperCamelCase					:str					     =	scope
					_UpperCamelCase					:Any					     =	encoder_stride

					# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
					_UpperCamelCase					:Dict					     =	(image_size // patch_size) ** 2
					_UpperCamelCase					:List[Any]					     =	num_patches + 1
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					_UpperCamelCase					:Tuple					     =	None
					if self.use_labels:
								_UpperCamelCase					:List[Any]					     =	ids_tensor([self.batch_size]					,				self.type_sequence_label_size   )

					_UpperCamelCase					:Tuple					     =	self.get_config()

					return config, pixel_values, labels
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					return ViTConfig(
					    image_size=self.image_size					,				patch_size=self.patch_size					,				num_channels=self.num_channels					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				intermediate_size=self.intermediate_size					,				hidden_act=self.hidden_act					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				is_decoder=__lowerCamelCase					,				initializer_range=self.initializer_range					,				encoder_stride=self.encoder_stride					,				)
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	ViTModel(config=__lowerCamelCase   )
					model.to(__lowerCamelCase   )
					model.eval()
					_UpperCamelCase					:Tuple					     =	model(__lowerCamelCase   )
					self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	ViTForMaskedImageModeling(config=__lowerCamelCase   )
					model.to(__lowerCamelCase   )
					model.eval()
					_UpperCamelCase					:List[Any]					     =	model(__lowerCamelCase   )
					self.parent.assertEqual(
					    result.reconstruction.shape					,				(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

					# test greyscale images
					_UpperCamelCase					:str					     =	1
					_UpperCamelCase					:int					     =	ViTForMaskedImageModeling(__lowerCamelCase   )
					model.to(__lowerCamelCase   )
					model.eval()

					_UpperCamelCase					:Optional[Any]					     =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
					_UpperCamelCase					:str					     =	model(__lowerCamelCase   )
					self.parent.assertEqual(result.reconstruction.shape					,				(self.batch_size, 1, self.image_size, self.image_size)   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.type_sequence_label_size
					_UpperCamelCase					:str					     =	ViTForImageClassification(__lowerCamelCase   )
					model.to(__lowerCamelCase   )
					model.eval()
					_UpperCamelCase					:Optional[Any]					     =	model(__lowerCamelCase					,				labels=__lowerCamelCase   )
					self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)   )

					# test greyscale images
					_UpperCamelCase					:List[Any]					     =	1
					_UpperCamelCase					:Any					     =	ViTForImageClassification(__lowerCamelCase   )
					model.to(__lowerCamelCase   )
					model.eval()

					_UpperCamelCase					:int					     =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
					_UpperCamelCase					:Any					     =	model(__lowerCamelCase   )
					self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)   )





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

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.prepare_config_and_inputs()
					(
					    (
					    _UpperCamelCase     
					)     ,      (
					    _UpperCamelCase     
					)     ,      (
					    _UpperCamelCase     
					)     ,           
					)					:Optional[Any]					     =	config_and_inputs
					_UpperCamelCase					:List[str]					     =	{'''pixel_values''': pixel_values}
					return config, inputs_dict






@require_torch
class      A(  __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							(
		    (
		        ViTModel,
		        ViTForImageClassification,
		        ViTForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		A    =							(
		    {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
		    if is_torch_available()
		    else {}
		)
		A    =							True

		A    =							False
		A    =							False
		A    =							False
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	ViTModelTester(self   )
					_UpperCamelCase					:Tuple					     =	ConfigTester(self					,				config_class=__lowerCamelCase					,				has_text_modality=__lowerCamelCase					,				hidden_size=37   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					self.config_tester.run_common_tests()
		@unittest.skip(reason='''ViT does not use inputs_embeds'''   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					pass
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								_UpperCamelCase					:List[Any]					     =	model_class(__lowerCamelCase   )
								self.assertIsInstance(model.get_input_embeddings()					,				(nn.Module)   )
								_UpperCamelCase					:List[str]					     =	model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(__lowerCamelCase					,				nn.Linear   )   )
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								_UpperCamelCase					:Optional[int]					     =	model_class(__lowerCamelCase   )
								_UpperCamelCase					:Any					     =	inspect.signature(model.forward   )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								_UpperCamelCase					:Any					     =	[*signature.parameters.keys()]

								_UpperCamelCase					:str					     =	['''pixel_values''']
								self.assertListEqual(arg_names[:1]					,				__lowerCamelCase   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCamelCase   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )





		@slow
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_UpperCamelCase					:Any					     =	ViTModel.from_pretrained(__lowerCamelCase   )
								self.assertIsNotNone(__lowerCamelCase   )





def 				A_       ( )					->  Any:
			_UpperCamelCase					:str					     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image






@require_torch
@require_vision
class      A(  unittest.TestCase		):

		"""simple docstring"""





		@cached_property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''   ) if is_vision_available() else None
		@slow
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''   ).to(__lowerCamelCase   )

					_UpperCamelCase					:List[Any]					     =	self.default_image_processor
					_UpperCamelCase					:Tuple					     =	prepare_img()
					_UpperCamelCase					:Dict					     =	image_processor(images=__lowerCamelCase					,				return_tensors='''pt'''   ).to(__lowerCamelCase   )

					# forward pass
					with torch.no_grad():
								_UpperCamelCase					:int					     =	model(**__lowerCamelCase   )

					# verify the logits
					_UpperCamelCase					:str					     =	torch.Size((1, 10_00)   )
					self.assertEqual(outputs.logits.shape					,				__lowerCamelCase   )

					_UpperCamelCase					:Optional[int]					     =	torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]   ).to(__lowerCamelCase   )

					self.assertTrue(torch.allclose(outputs.logits[0, :3]					,				__lowerCamelCase					,				atol=1E-4   )   )
		@slow
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	ViTModel.from_pretrained('''facebook/dino-vits8'''   ).to(__lowerCamelCase   )

					_UpperCamelCase					:List[Any]					     =	ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''					,				size=4_80   )
					_UpperCamelCase					:List[Any]					     =	prepare_img()
					_UpperCamelCase					:int					     =	image_processor(images=__lowerCamelCase					,				return_tensors='''pt'''   )
					_UpperCamelCase					:str					     =	inputs.pixel_values.to(__lowerCamelCase   )

					# forward pass
					with torch.no_grad():
								_UpperCamelCase					:Tuple					     =	model(__lowerCamelCase					,				interpolate_pos_encoding=__lowerCamelCase   )

					# verify the logits
					_UpperCamelCase					:List[Any]					     =	torch.Size((1, 36_01, 3_84)   )
					self.assertEqual(outputs.last_hidden_state.shape					,				__lowerCamelCase   )

					_UpperCamelCase					:Optional[int]					     =	torch.tensor(
					    [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]   ).to(__lowerCamelCase   )

					self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,				__lowerCamelCase					,				atol=1E-4   )   )





		@slow
		@require_accelerate
		@require_torch_gpu
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	ViTModel.from_pretrained('''facebook/dino-vits8'''					,				torch_dtype=torch.floataa					,				device_map='''auto'''   )
					_UpperCamelCase					:Any					     =	self.default_image_processor

					_UpperCamelCase					:Any					     =	prepare_img()
					_UpperCamelCase					:Dict					     =	image_processor(images=__lowerCamelCase					,				return_tensors='''pt'''   )
					_UpperCamelCase					:List[Any]					     =	inputs.pixel_values.to(__lowerCamelCase   )

					# forward pass to make sure inference works in fp16
					with torch.no_grad():
								_UpperCamelCase					:str					     =	model(__lowerCamelCase   )






"""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 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1.0  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[str]:
  if rng is None:
    _snake_case   	=      global_rng

  _snake_case   	=      []
  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  ):



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[Any]=7							,       __lowerCamelCase    :	List[Any]=4_0_0							,       __lowerCamelCase    :	Any=2_0_0_0							,       __lowerCamelCase    :	Any=2_0_4_8							,       __lowerCamelCase    :	Any=1_2_8							,       __lowerCamelCase    :	Any=1							,       __lowerCamelCase    :	Optional[int]=5_1_2							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	List[Any]=4_4_1_0_0							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      min_seq_length
     _snake_case   	=      max_seq_length
     _snake_case   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     _snake_case   	=      spectrogram_length
     _snake_case   	=      feature_size
     _snake_case   	=      num_audio_channels
     _snake_case   	=      hop_length
     _snake_case   	=      chunk_length
     _snake_case   	=      sampling_rate



   def    __UpperCAmelCase    (       self    :	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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	int=False				):



     """simple docstring"""
     def _flatten(__lowerCamelCase    :	List[str]				):
       return list(itertools.chain(*__lowerCamelCase				)				)

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



@require_torch
@require_torchaudio
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  TvltFeatureExtractor



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      TvltFeatureExtractionTester(self				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''spectrogram_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''feature_size'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''num_audio_channels'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''hop_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''chunk_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''sampling_rate'''				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

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

     _snake_case   	=      feat_extract_first.to_dict()
     _snake_case   	=      feat_extract_second.to_dict()
     _snake_case   	=      dict_first.pop('''mel_filters'''				)
     _snake_case   	=      dict_second.pop('''mel_filters'''				)
     self.assertTrue(np.allclose(__lowerCamelCase							,       __lowerCamelCase				)				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

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

     _snake_case   	=      feat_extract_first.to_dict()
     _snake_case   	=      feat_extract_second.to_dict()
     _snake_case   	=      dict_first.pop('''mel_filters'''				)
     _snake_case   	=      dict_second.pop('''mel_filters'''				)
     self.assertTrue(np.allclose(__lowerCamelCase							,       __lowerCamelCase				)				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     # Initialize feature_extractor
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     # create three inputs of length 800, 1000, and 1200
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      [np.asarray(__lowerCamelCase				) for speech_input in speech_inputs]

     # Test not batched input
     _snake_case   	=      feature_extractor(np_speech_inputs[0]							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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
     _snake_case   	=      feature_extractor(
         __lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0							,       mask_audio=__lowerCamelCase				).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.
     _snake_case   	=      [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     _snake_case   	=      np.asarray(__lowerCamelCase				)
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,       '''clean'''							,       split='''validation'''				)
     # automatic decoding with librispeech
     _snake_case   	=      ds.sort('''id'''				).select(range(__lowerCamelCase				)				)[:num_samples]['''audio''']

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





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self._load_datasamples(1				)
     _snake_case   	=      TvltFeatureExtractor()
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''pt'''				).audio_values

     self.assertEquals(audio_values.shape							,       (1, 1, 1_9_2, 1_2_8)				)

     _snake_case   	=      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]							,       __lowerCamelCase							,       atol=1E-4				)				)





