






"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
							from transformers.models.mobilevitva.modeling_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
							    make_divisible,
							)


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Dict:
											__magic_name__:	int							=       self.config_class(**self.inputs_dict					)
											self.parent.assertTrue(hasattr(__snake_case     ,		"""width_multiplier"""					)					)



class       __A		:

					def __init__(			self  :      Tuple     ,		__snake_case  :      List[str]     ,		__snake_case  :      int=1_3     ,		__snake_case  :      Optional[int]=6_4     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      Tuple=3     ,		__snake_case  :      Any="swish"     ,		__snake_case  :      int=3     ,		__snake_case  :      int=3_2     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      int=0.02     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      Optional[Any]=1_0     ,		__snake_case  :      Tuple=None     ,		__snake_case  :      Any=0.25     ,		__snake_case  :      List[Any]=0.0     ,		__snake_case  :      Dict=0.0     ,		)			->							Optional[int]:
											__magic_name__:	int							=       parent
											__magic_name__:	Union[str, Any]							=       batch_size
											__magic_name__:	Optional[Any]							=       image_size
											__magic_name__:	Optional[Any]							=       patch_size
											__magic_name__:	Union[str, Any]							=       num_channels
											__magic_name__:	Optional[Any]							=       make_divisible(5_1_2 * width_multiplier     ,		divisor=8					)
											__magic_name__:	List[Any]							=       hidden_act
											__magic_name__:	int							=       conv_kernel_size
											__magic_name__:	Tuple							=       output_stride
											__magic_name__:	Dict							=       classifier_dropout_prob
											__magic_name__:	Optional[Any]							=       use_labels
											__magic_name__:	List[str]							=       is_training
											__magic_name__:	List[str]							=       num_labels
											__magic_name__:	str							=       initializer_range
											__magic_name__:	Dict							=       scope
											__magic_name__:	str							=       width_multiplier
											__magic_name__:	str							=       ffn_dropout
											__magic_name__:	Optional[Any]							=       attn_dropout

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	Dict							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

											__magic_name__:	List[str]							=       None
											__magic_name__:	Tuple							=       None
											if self.use_labels:
																	__magic_name__:	List[str]							=       ids_tensor([self.batch_size]     ,		self.num_labels					)
																	__magic_name__:	Any							=       ids_tensor([self.batch_size, self.image_size, self.image_size]     ,		self.num_labels					)

											__magic_name__:	Dict							=       self.get_config()

											return config, pixel_values, labels, pixel_labels

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Any:
											return MobileViTVaConfig(
											    image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		hidden_act=self.hidden_act     ,		conv_kernel_size=self.conv_kernel_size     ,		output_stride=self.output_stride     ,		classifier_dropout_prob=self.classifier_dropout_prob     ,		initializer_range=self.initializer_range     ,		width_multiplier=self.width_multiplier     ,		ffn_dropout=self.ffn_dropout_prob     ,		attn_dropout=self.attn_dropout_prob     ,		)

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Dict     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      str					)			->							Optional[Any]:
											__magic_name__:	Optional[int]							=       MobileViTVaModel(config=__snake_case					)
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	Optional[int]							=       model(__snake_case					)
											self.parent.assertEqual(
											    result.last_hidden_state.shape     ,		(
											        self.batch_size,
											        self.last_hidden_size,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     ,		)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]					)			->							Any:
											__magic_name__:	Union[str, Any]							=       self.num_labels
											__magic_name__:	List[str]							=       MobileViTVaForImageClassification(__snake_case					)
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	Optional[int]							=       model(__snake_case     ,		labels=__snake_case					)
											self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Dict     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Union[str, Any]					)			->							Dict:
											__magic_name__:	Tuple							=       self.num_labels
											__magic_name__:	Any							=       MobileViTVaForSemanticSegmentation(__snake_case					)
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	List[Any]							=       model(__snake_case					)
											self.parent.assertEqual(
											    result.logits.shape     ,		(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     ,		)
											__magic_name__:	Any							=       model(__snake_case     ,		labels=__snake_case					)
											self.parent.assertEqual(
											    result.logits.shape     ,		(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     ,		)






					def   lowerCamelCase__   (			self  :      Any					)			->							Union[str, Any]:
											__magic_name__:	str							=       self.prepare_config_and_inputs()
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Optional[int]							=       config_and_inputs
											__magic_name__:	Tuple							=       {"""pixel_values""": pixel_values}
											return config, inputs_dict



@require_torch
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				(
					    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)

					UpperCAmelCase__								=				(
					    {
					        "feature-extraction": MobileViTVaModel,
					        "image-classification": MobileViTVaForImageClassification,
					        "image-segmentation": MobileViTVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Any:
											__magic_name__:	List[str]							=       MobileViTVaModelTester(self					)
											__magic_name__:	str							=       MobileViTVaConfigTester(self     ,		config_class=__snake_case     ,		has_text_modality=__snake_case					)

					def   lowerCamelCase__   (			self  :      int					)			->							Dict:
											self.config_tester.run_common_tests()

					@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""					)
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											pass

					@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""					)
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											pass

					@unittest.skip(reason="""MobileViTV2 does not output attentions"""					)
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[Any]:
											pass

					@require_torch_multi_gpu
					@unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""					)
					def   lowerCamelCase__   (			self  :      str					)			->							str:
											pass

					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
					def   lowerCamelCase__   (			self  :      str					)			->							Dict:
											pass

					def   lowerCamelCase__   (			self  :      Dict					)			->							List[str]:
											__magic_name__,			__magic_name__:	Dict							=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	__magic_name__:	Dict							=       ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[Any]:
											__magic_name__:	Any							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[Any]:
											def check_hidden_states_output(__snake_case  :      Union[str, Any]     ,		__snake_case  :      str     ,		__snake_case  :      List[Any]					):
																	__magic_name__:	Union[str, Any]							=       model_class(__snake_case					)
																	model.to(__snake_case					)
																	model.eval()

																	with torch.no_grad():
																							__magic_name__:	str							=       model(**self._prepare_for_class(__snake_case     ,		__snake_case					)					)

																	__magic_name__:	List[Any]							=       outputs.hidden_states

																	__magic_name__:	List[Any]							=       5
																	self.assertEqual(len(__snake_case					)     ,		__snake_case					)

																	# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																	# with the width and height being successively divided by 2.
																	__magic_name__:	int							=       2
																	for i in range(len(__snake_case					)					):
																							self.assertListEqual(
																							    list(hidden_states[i].shape[-2:]					)     ,		[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     ,		)
																							divisor *= 2

																	self.assertEqual(self.model_tester.output_stride     ,		divisor // 2					)

											__magic_name__,			__magic_name__:	Dict							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	__magic_name__:	Dict							=       True
																	check_hidden_states_output(__snake_case     ,		__snake_case     ,		__snake_case					)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	__magic_name__:	Optional[int]							=       True

																	check_hidden_states_output(__snake_case     ,		__snake_case     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Union[str, Any]:
											__magic_name__:	Any							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							str:
											__magic_name__:	Dict							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case					)






					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Optional[int]:
											for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Union[str, Any]							=       MobileViTVaModel.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)



def   a    (     )    ->							Any:
						__magic_name__:	Any							=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
						return image



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

					@cached_property
					def   lowerCamelCase__   (			self  :      Dict					)			->							str:
											return (
											    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""					)
											    if is_vision_available()
											    else None
											)

					@slow
					def   lowerCamelCase__   (			self  :      Dict					)			->							int:
											__magic_name__:	Any							=       MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""					).to(
											    __snake_case					)

											__magic_name__:	Optional[Any]							=       self.default_image_processor
											__magic_name__:	Tuple							=       prepare_img()
											__magic_name__:	str							=       image_processor(images=__snake_case     ,		return_tensors="""pt"""					).to(__snake_case					)

											# forward pass
											with torch.no_grad():
																	__magic_name__:	Any							=       model(**__snake_case					)

											# verify the logits
											__magic_name__:	List[Any]							=       torch.Size((1, 1_0_0_0)					)
											self.assertEqual(outputs.logits.shape     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]					).to(__snake_case					)

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

					@slow
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	Any							=       MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)
											__magic_name__:	List[Any]							=       model.to(__snake_case					)

											__magic_name__:	str							=       MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)

											__magic_name__:	str							=       prepare_img()
											__magic_name__:	str							=       image_processor(images=__snake_case     ,		return_tensors="""pt"""					).to(__snake_case					)

											# forward pass
											with torch.no_grad():
																	__magic_name__:	Any							=       model(**__snake_case					)
											__magic_name__:	Optional[Any]							=       outputs.logits

											# verify the logits
											__magic_name__:	List[str]							=       torch.Size((1, 2_1, 3_2, 3_2)					)
											self.assertEqual(logits.shape     ,		__snake_case					)

											__magic_name__:	Optional[Any]							=       torch.tensor(
											    [
											        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
											        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
											        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
											    ]     ,		device=__snake_case     ,		)

											self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,		__snake_case     ,		atol=1E-4					)					)






					@slow
					def   lowerCamelCase__   (			self  :      int					)			->							Optional[Any]:
											__magic_name__:	List[str]							=       MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)
											__magic_name__:	Union[str, Any]							=       model.to(__snake_case					)

											__magic_name__:	List[Any]							=       MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)

											__magic_name__:	str							=       prepare_img()
											__magic_name__:	List[str]							=       image_processor(images=__snake_case     ,		return_tensors="""pt"""					).to(__snake_case					)

											# forward pass
											with torch.no_grad():
																	__magic_name__:	Tuple							=       model(**__snake_case					)

											__magic_name__:	Union[str, Any]							=       outputs.logits.detach().cpu()

											__magic_name__:	Any							=       image_processor.post_process_semantic_segmentation(outputs=__snake_case     ,		target_sizes=[(5_0, 6_0)]					)
											__magic_name__:	Tuple							=       torch.Size((5_0, 6_0)					)
											self.assertEqual(segmentation[0].shape     ,		__snake_case					)

											__magic_name__:	str							=       image_processor.post_process_semantic_segmentation(outputs=__snake_case					)
											__magic_name__:	Optional[int]							=       torch.Size((3_2, 3_2)					)
											self.assertEqual(segmentation[0].shape     ,		__snake_case					)







'''simple docstring'''
import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_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 ASTForAudioClassification, ASTModel
    from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
        AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )


if is_torchaudio_available():
    import torchaudio

    from transformers import ASTFeatureExtractor






class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=13			,     _UpperCamelCase=2			,     _UpperCamelCase=24			,     _UpperCamelCase=16			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=32			,     _UpperCamelCase=5			,     _UpperCamelCase=4			,     _UpperCamelCase=37			,     _UpperCamelCase="gelu"			,     _UpperCamelCase=0.1			,     _UpperCamelCase=0.1			,     _UpperCamelCase=10			,     _UpperCamelCase=0.02			,     _UpperCamelCase=None			,     _UpperCamelCase=2			,     _UpperCamelCase=2			,     )->      Tuple:
            _A				=							parent
            _A				=							batch_size
            _A				=							patch_size
            _A				=							max_length
            _A				=							num_mel_bins
            _A				=							is_training
            _A				=							use_labels
            _A				=							hidden_size
            _A				=							num_hidden_layers
            _A				=							num_attention_heads
            _A				=							intermediate_size
            _A				=							hidden_act
            _A				=							hidden_dropout_prob
            _A				=							attention_probs_dropout_prob
            _A				=							type_sequence_label_size
            _A				=							initializer_range
            _A				=							scope
            _A				=							frequency_stride
            _A				=							time_stride

            # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
            _A				=							(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
            _A				=							(self.max_length - self.patch_size) // self.time_stride + 1
            _A				=							frequency_out_dimension * time_out_dimension
            _A				=							num_patches + 2






       def   UpperCamelCase						( self   )->      int:
            _A				=							floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]   )

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

            _A				=							self.get_config()

            return config, input_values, labels






       def   UpperCamelCase						( self   )->      int:
            return ASTConfig(
                patch_size=self.patch_size			,     max_length=self.max_length			,     num_mel_bins=self.num_mel_bins			,     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=_UpperCamelCase			,     initializer_range=self.initializer_range			,     frequency_stride=self.frequency_stride			,     time_stride=self.time_stride			,     )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Optional[Any]:
            _A				=							ASTModel(config=_UpperCamelCase   )
            model.to(_UpperCamelCase   )
            model.eval()
            _A				=							model(_UpperCamelCase   )
            self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.prepare_config_and_inputs()
            (
                (
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				
            )				=							config_and_inputs
            _A				=							{'input_values': input_values}
            return config, inputs_dict






@require_torch
class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=(
           (
               ASTModel,
               ASTForAudioClassification,
           )
           if is_torch_available()
           else ()
       )
       __UpperCAmelCase					=(
           {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
           if is_torch_available()
           else {}
       )
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Union[str, Any]:
            if pipeline_test_casse_name == "AudioClassificationPipelineTests":
                 return True

            return False






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							ASTModelTester(self   )
            _A				=							ConfigTester(self			,     config_class=_UpperCamelCase			,     has_text_modality=_UpperCamelCase			,     hidden_size=37   )






       def   UpperCamelCase						( self   )->      int:
            self.config_tester.run_common_tests()






       @unittest.skip(reason='AST does not use inputs_embeds'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            pass






       def   UpperCamelCase						( self   )->      str:
            _A		,				_A				=							self.model_tester.prepare_config_and_inputs_for_common()

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






       def   UpperCamelCase						( self   )->      List[str]:
            _A		,				_A				=							self.model_tester.prepare_config_and_inputs_for_common()

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

                 _A				=							['input_values']
                 self.assertListEqual(arg_names[:1]			,     _UpperCamelCase   )






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






       @slow
       def   UpperCamelCase						( self   )->      Tuple:
            for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A				=							ASTModel.from_pretrained(_UpperCamelCase   )
                 self.assertIsNotNone(_UpperCamelCase   )






def 					lowerCamelCase_ ( )					->	Union[str, Any]:







     """simple docstring"""
     _A				=							hf_hub_download(
         repo_id='nielsr/audio-spectogram-transformer-checkpoint'							,    filename='sample_audio.flac'							,    repo_type='dataset'		)

     _A		,				_A				=							torchaudio.load(__UpperCamelCase		)

     return audio, sampling_rate






@require_torch
@require_torchaudio
class        lowerCAmelCase_  (     unittest.TestCase	):






       @cached_property
       def   UpperCamelCase						( self   )->      Dict:
            return (
                ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'   )
                if is_torchaudio_available()
                else None
            )






       @slow
       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.default_feature_extractor
            _A				=							ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'   ).to(_UpperCamelCase   )

            _A				=							self.default_feature_extractor
            _A		,				_A				=							prepare_audio()
            _A				=							audio.squeeze().numpy()
            _A				=							feature_extractor(_UpperCamelCase			,     sampling_rate=_UpperCamelCase			,     return_tensors='pt'   ).to(_UpperCamelCase   )

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

            # verify the logits
            _A				=							torch.Size((1, 527)   )
            self.assertEqual(outputs.logits.shape			,     _UpperCamelCase   )

            _A				=							torch.tensor([-0.8760, -7.0042, -8.6602]   ).to(_UpperCamelCase   )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     _UpperCamelCase			,     atol=1e-4   )   )





