



import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Tuple      ,				__lowercase		:		Tuple      ,				__lowercase		:		Optional[Any]=13      ,				__lowercase		:		Dict=32      ,				__lowercase		:		int=2      ,				__lowercase		:		str=3      ,				__lowercase		:		Optional[int]=16      ,				__lowercase		:		Any=[1, 2, 1]      ,				__lowercase		:		Optional[Any]=[2, 2, 4]      ,				__lowercase		:		Optional[int]=2      ,				__lowercase		:		Optional[int]=2.0      ,				__lowercase		:		str=True      ,				__lowercase		:		List[Any]=0.0      ,				__lowercase		:		Optional[Any]=0.0      ,				__lowercase		:		Any=0.1      ,				__lowercase		:		Tuple="gelu"      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		List[str]=True      ,				__lowercase		:		Any=0.02      ,				__lowercase		:		Dict=1E-5      ,				__lowercase		:		List[str]=True      ,				__lowercase		:		str=None      ,				__lowercase		:		Tuple=True      ,				__lowercase		:		Tuple=10      ,				__lowercase		:		Any=8      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		batch_size
											__a       =		image_size
											__a       =		patch_size
											__a       =		num_channels
											__a       =		embed_dim
											__a       =		depths
											__a       =		num_heads
											__a       =		window_size
											__a       =		mlp_ratio
											__a       =		qkv_bias
											__a       =		hidden_dropout_prob
											__a       =		attention_probs_dropout_prob
											__a       =		drop_path_rate
											__a       =		hidden_act
											__a       =		use_absolute_embeddings
											__a       =		patch_norm
											__a       =		layer_norm_eps
											__a       =		initializer_range
											__a       =		is_training
											__a       =		scope
											__a       =		use_labels
											__a       =		type_sequence_label_size
											__a       =		encoder_stride





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

											__a       =		None
											if self.use_labels:
															__a       =		ids_tensor([self.batch_size]      ,				self.type_sequence_label_size	)

											__a       =		self.get_config()

											return config, pixel_values, labels





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											return SwinvaConfig(
											    image_size=self.image_size      ,				patch_size=self.patch_size      ,				num_channels=self.num_channels      ,				embed_dim=self.embed_dim      ,				depths=self.depths      ,				num_heads=self.num_heads      ,				window_size=self.window_size      ,				mlp_ratio=self.mlp_ratio      ,				qkv_bias=self.qkv_bias      ,				hidden_dropout_prob=self.hidden_dropout_prob      ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,				drop_path_rate=self.drop_path_rate      ,				hidden_act=self.hidden_act      ,				use_absolute_embeddings=self.use_absolute_embeddings      ,				path_norm=self.patch_norm      ,				layer_norm_eps=self.layer_norm_eps      ,				initializer_range=self.initializer_range      ,				encoder_stride=self.encoder_stride      ,				)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Tuple      ,				__lowercase		:		str      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		SwinvaModel(config=__lowercase	)
											model.to(__lowercase	)
											model.eval()
											__a       =		model(__lowercase	)

											__a       =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
											__a       =		int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

											self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, expected_seq_len, expected_dim)	)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		SwinvaForMaskedImageModeling(config=__lowercase	)
											model.to(__lowercase	)
											model.eval()
											__a       =		model(__lowercase	)
											self.parent.assertEqual(
											    result.logits.shape      ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

											# test greyscale images
											__a       =		1
											__a       =		SwinvaForMaskedImageModeling(__lowercase	)
											model.to(__lowercase	)
											model.eval()

											__a       =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
											__a       =		model(__lowercase	)
											self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, 1, self.image_size, self.image_size)	)





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Tuple      ,				__lowercase		:		Dict      ,				__lowercase		:		int	):

											'''simple docstring'''
											__a       =		self.type_sequence_label_size
											__a       =		SwinvaForImageClassification(__lowercase	)
											model.to(__lowercase	)
											model.eval()
											__a       =		model(__lowercase      ,				labels=__lowercase	)
											self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)	)







							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											__a     ,					__a     ,					__a       =		config_and_inputs
											__a       =		{"""pixel_values""": pixel_values}
											return config, inputs_dict






@require_torch
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Union[str, Any]    =(
							    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
							)
							__lowerCamelCase	:	int    =(
							    {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowerCamelCase	:	Tuple    =False
							__lowerCamelCase	:	Tuple    =False
							__lowerCamelCase	:	Union[str, Any]    =False
							__lowerCamelCase	:	Dict    =False





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		SwinvaModelTester(self	)
											__a       =		ConfigTester(self      ,				config_class=__lowercase      ,				embed_dim=37	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase	)





							@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""	)
							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											pass





							@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""	)
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											pass





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__a       =		model_class(__lowercase	)
															self.assertIsInstance(model.get_input_embeddings()      ,				(nn.Module)	)
															__a       =		model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(__lowercase      ,				nn.Linear	)	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()

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

															__a       =		["""pixel_values"""]
															self.assertListEqual(arg_names[:1]      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()
											__a       =		True

											for model_class in self.all_model_classes:
															__a       =		True
															__a       =		False
															__a       =		True
															__a       =		model_class(__lowercase	)
															model.to(__lowercase	)
															model.eval()
															with torch.no_grad():
																			__a       =		model(**self._prepare_for_class(__lowercase      ,				__lowercase	)	)
															__a       =		outputs.attentions
															__a       =		len(self.model_tester.depths	)
															self.assertEqual(len(__lowercase	)      ,				__lowercase	)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															__a       =		True
															__a       =		config.window_size**2
															__a       =		model_class(__lowercase	)
															model.to(__lowercase	)
															model.eval()
															with torch.no_grad():
																			__a       =		model(**self._prepare_for_class(__lowercase      ,				__lowercase	)	)
															__a       =		outputs.attentions
															self.assertEqual(len(__lowercase	)      ,				__lowercase	)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]	)      ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,				)
															__a       =		len(__lowercase	)

															# Check attention is always last and order is fine
															__a       =		True
															__a       =		True
															__a       =		model_class(__lowercase	)
															model.to(__lowercase	)
															model.eval()
															with torch.no_grad():
																			__a       =		model(**self._prepare_for_class(__lowercase      ,				__lowercase	)	)

															if hasattr(self.model_tester      ,				"""num_hidden_states_types"""	):
																			__a       =		self.model_tester.num_hidden_states_types
															else:
																			# also another +1 for reshaped_hidden_states
																			__a       =		2
															self.assertEqual(out_len + added_hidden_states      ,				len(__lowercase	)	)

															__a       =		outputs.attentions

															self.assertEqual(len(__lowercase	)      ,				__lowercase	)

															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]	)      ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,				)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[str]      ,				__lowercase		:		Tuple      ,				__lowercase		:		str      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		model_class(__lowercase	)
											model.to(__lowercase	)
											model.eval()

											with torch.no_grad():
															__a       =		model(**self._prepare_for_class(__lowercase      ,				__lowercase	)	)

											__a       =		outputs.hidden_states

											__a       =		getattr(
											    self.model_tester      ,				"""expected_num_hidden_layers"""      ,				len(self.model_tester.depths	) + 1	)
											self.assertEqual(len(__lowercase	)      ,				__lowercase	)

											# Swinv2 has a different seq_length
											__a       =		(
											    config.patch_size
											    if isinstance(config.patch_size      ,				collections.abc.Iterable	)
											    else (config.patch_size, config.patch_size)
											)

											__a       =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]	)      ,				[num_patches, self.model_tester.embed_dim]      ,				)

											__a       =		outputs.reshaped_hidden_states
											self.assertEqual(len(__lowercase	)      ,				__lowercase	)

											__a     ,					__a     ,					__a     ,					__a       =		reshaped_hidden_states[0].shape
											__a       =		(
											    reshaped_hidden_states[0].view(__lowercase      ,				__lowercase      ,				height * width	).permute(0      ,				2      ,				1	)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]	)      ,				[num_patches, self.model_tester.embed_dim]      ,				)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()

											__a       =		(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size      ,				collections.abc.Iterable	)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes:
															__a       =		True
															self.check_hidden_states_output(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__a       =		True

															self.check_hidden_states_output(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()
											__a       =		3

											__a       =		(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size      ,				collections.abc.Iterable	)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)
											__a       =		(
											    config.patch_size
											    if isinstance(config.patch_size      ,				collections.abc.Iterable	)
											    else (config.patch_size, config.patch_size)
											)

											__a       =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											__a       =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
															__a       =		True
															self.check_hidden_states_output(__lowercase      ,				__lowercase      ,				__lowercase      ,				(padded_height, padded_width)	)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__a       =		True
															self.check_hidden_states_output(__lowercase      ,				__lowercase      ,				__lowercase      ,				(padded_height, padded_width)	)





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase	)





							@slow
							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a       =		SwinvaModel.from_pretrained(__lowercase	)
															self.assertIsNotNone(__lowercase	)







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()

											__a       =		_config_zero_init(__lowercase	)
											for model_class in self.all_model_classes:
															__a       =		model_class(config=__lowercase	)
															for name, param in model.named_parameters():
																			if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																							self.assertIn(
																							    ((param.data.mean() * 1E9).round() / 1E9).item()      ,				[0.0, 1.0]      ,				msg=F"Parameter {name} of model {model_class} seems not properly initialized"      ,				)






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





							@cached_property
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											return (
											    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""	)
											    if is_vision_available()
											    else None
											)







							@slow
							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""	).to(
											    __lowercase	)
											__a       =		self.default_image_processor

											__a       =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
											__a       =		image_processor(images=__lowercase      ,				return_tensors="""pt"""	).to(__lowercase	)

											# forward pass
											with torch.no_grad():
															__a       =		model(**__lowercase	)

											# verify the logits
											__a       =		torch.Size((1, 1000)	)
											self.assertEqual(outputs.logits.shape      ,				__lowercase	)
											__a       =		torch.tensor([-0.3947, -0.4306, 0.0026]	).to(__lowercase	)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				__lowercase      ,				atol=1E-4	)	)





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__			= random.Random()

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			int=1.0				,     _SCREAMING_SNAKE_CASE       :			Any=None				,     _SCREAMING_SNAKE_CASE       :			Any=None    ):
				"""simple docstring"""







				if rng is None:
								__a       =		global_rng

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

				return values






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def __init__(   self		:		Dict      ,				__lowercase		:		Tuple      ,				__lowercase		:		Tuple=7      ,				__lowercase		:		Optional[int]=400      ,				__lowercase		:		int=2000      ,				__lowercase		:		List[Any]=2048      ,				__lowercase		:		List[str]=128      ,				__lowercase		:		Union[str, Any]=1      ,				__lowercase		:		str=512      ,				__lowercase		:		List[str]=30      ,				__lowercase		:		Tuple=44100      ,				):

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





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''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		:		Optional[int]      ,				__lowercase		:		Optional[int]=False      ,				__lowercase		:		Any=False	):

											'''simple docstring'''
											def _flatten(__lowercase		:		Tuple	):
															return list(itertools.chain(*__lowercase	)	)

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






@require_torch
@require_torchaudio
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Dict    =TvltFeatureExtractor





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		TvltFeatureExtractionTester(self	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		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 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															__a       =		os.path.join(__lowercase      ,				"""feat_extract.json"""	)
															feat_extract_first.to_json_file(__lowercase	)
															__a       =		self.feature_extraction_class.from_json_file(__lowercase	)

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											# Initialize feature_extractor
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											# Test not batched input
											__a       =		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       =		feature_extractor(__lowercase      ,				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       =		feature_extractor(
											    __lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100      ,				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       =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											__a       =		np.asarray(__lowercase	)
											__a       =		feature_extractor(__lowercase      ,				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 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		str	):

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

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







							def 						UpperCamelCase_					(   self		:		str	):

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

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

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