
import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
				from transformers.models.esm.modeling_esm import (
				    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EsmEmbeddings,
				    create_position_ids_from_input_ids,
				)






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=33     ,	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_=512     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=None     ,	)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			seq_length
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_input_mask
													__UpperCamelCase       =			use_token_type_ids
													__UpperCamelCase       =			use_labels
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			type_vocab_size
													__UpperCamelCase       =			type_sequence_label_size
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			num_labels
													__UpperCamelCase       =			num_choices
													__UpperCamelCase       =			scope



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





													'''simple docstring'''






													__UpperCamelCase       =			ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size			)

													__UpperCamelCase       =			None
													if self.use_input_mask:
																			__UpperCamelCase       =			random_attention_mask([self.batch_size, self.seq_length]			)

													__UpperCamelCase       =			None
													__UpperCamelCase       =			None
													__UpperCamelCase       =			None
													if self.use_labels:
																			__UpperCamelCase       =			ids_tensor([self.batch_size]     ,	self.type_sequence_label_size			)
																			__UpperCamelCase       =			ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels			)
																			__UpperCamelCase       =			ids_tensor([self.batch_size]     ,	self.num_choices			)

													__UpperCamelCase       =			self.get_config()

													return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



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





													'''simple docstring'''






													return EsmConfig(
													    vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	pad_token_id=1     ,	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     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	initializer_range=self.initializer_range     ,	)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			EsmModel(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)

													self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)
													self.parent.assertEqual(result.pooler_output.shape     ,	(self.batch_size, self.hidden_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			EsmForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			self.num_labels
													__UpperCamelCase       =			EsmForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							
													)       =			config_and_inputs
													__UpperCamelCase       =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  False

							_snake_case     		=  (
							    (
							        EsmForMaskedLM,
							        EsmModel,
							        EsmForSequenceClassification,
							        EsmForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case     		=  ()
							_snake_case     		=  (
							    {
							        'feature-extraction': EsmModel,
							        'fill-mask': EsmForMaskedLM,
							        'text-classification': EsmForSequenceClassification,
							        'token-classification': EsmForTokenClassification,
							        'zero-shot': EsmForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_snake_case     		=  True



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





													'''simple docstring'''






													__UpperCamelCase       =			EsmModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	hidden_size=37			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													self.config_tester.run_common_tests()



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			__UpperCamelCase       =			type
																			self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       =			EsmModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()[0]
													__UpperCamelCase       =			EsmEmbeddings(config=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.as_tensor([[12, 31, 13, model.padding_idx]]			)
													__UpperCamelCase       =			torch.as_tensor(
													    [
													        [
													            0 + model.padding_idx + 1,
													            1 + model.padding_idx + 1,
													            2 + model.padding_idx + 1,
													            model.padding_idx,
													        ]
													    ]			)
													__UpperCamelCase       =			create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE_     ,	model.padding_idx			)
													self.assertEqual(position_ids.shape     ,	expected_positions.shape			)
													self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)			)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()[0]
													__UpperCamelCase       =			EsmEmbeddings(config=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.empty(2     ,	4     ,	30			)
													__UpperCamelCase       =			[
													    0 + embeddings.padding_idx + 1,
													    1 + embeddings.padding_idx + 1,
													    2 + embeddings.padding_idx + 1,
													    3 + embeddings.padding_idx + 1,
													]
													__UpperCamelCase       =			torch.as_tensor([expected_single_positions, expected_single_positions]			)
													__UpperCamelCase       =			embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE_			)
													self.assertEqual(position_ids.shape     ,	expected_positions.shape			)
													self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)			)			)



							@unittest.skip('''Esm does not support embedding resizing'''			)
							def     A__		(  self			)->   int:





													'''simple docstring'''






													pass



							@unittest.skip('''Esm does not support embedding resizing'''			)
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													pass



							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													pass






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@slow
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													with torch.no_grad():
																			__UpperCamelCase       =			EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''			)
																			model.eval()
																			__UpperCamelCase       =			torch.tensor([[0, 1, 2, 3, 4, 5]]			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)[0]

																			__UpperCamelCase       =			33

																			__UpperCamelCase       =			torch.Size((1, 6, vocab_size)			)
																			self.assertEqual(output.shape     ,	SCREAMING_SNAKE_CASE_			)

																			__UpperCamelCase       =			torch.tensor(
																			    [[[8.9_2_1_5, -1_0.5_8_9_8, -6.4_6_7_1], [-6.3_9_6_7, -1_3.9_1_1_4, -1.1_2_1_2], [-7.7_8_1_2, -1_3.9_5_1_6, -3.7_4_0_6]]]			)
																			self.assertTrue(torch.allclose(output[:, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4			)			)



							@slow
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													with torch.no_grad():
																			__UpperCamelCase       =			EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''			)
																			model.eval()

																			__UpperCamelCase       =			torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)[0]
																			# compare the actual values for a slice.
																			__UpperCamelCase       =			torch.tensor(
																			    [[[0.1_4_4_4, 0.5_4_1_3, 0.3_2_4_8], [0.3_0_3_4, 0.0_0_5_3, 0.3_1_0_8], [0.3_2_2_8, -0.2_4_9_9, 0.3_4_1_5]]]			)
																			self.assertTrue(torch.allclose(output[:, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4			)			)






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        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=24     ,	SCREAMING_SNAKE_CASE_=16     ,	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     ,	SCREAMING_SNAKE_CASE_=2     ,	)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			patch_size
													__UpperCamelCase       =			max_length
													__UpperCamelCase       =			num_mel_bins
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_labels
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			type_sequence_label_size
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			scope
													__UpperCamelCase       =			frequency_stride
													__UpperCamelCase       =			time_stride

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



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]			)

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

													__UpperCamelCase       =			self.get_config()

													return config, input_values, labels



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	frequency_stride=self.frequency_stride     ,	time_stride=self.time_stride     ,	)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			ASTModel(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							
													)       =			config_and_inputs
													__UpperCamelCase       =			{'''input_values''': input_values}
													return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (
							    (
							        ASTModel,
							        ASTForAudioClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case     		=  (
							    {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
							    if is_torch_available()
							    else {}
							)
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													if pipeline_test_casse_name == "AudioClassificationPipelineTests":
																			return True

													return False



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			ASTModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							@unittest.skip(reason='''AST does not use inputs_embeds'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



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





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

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



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





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCamelCase       =			['''input_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       =			ASTModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





def 	A_ (							)      ->    int:



						'''simple docstring'''

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

						__UpperCamelCase  ,							__UpperCamelCase       =			torchaudio.load(snake_case )

						return audio, sampling_rate






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

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													return (
													    ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			)
													    if is_torchaudio_available()
													    else None
													)



							@slow
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.default_feature_extractor
													__UpperCamelCase       =			ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			).to(SCREAMING_SNAKE_CASE_			)

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

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

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

													__UpperCamelCase       =			torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]			).to(SCREAMING_SNAKE_CASE_			)

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




