
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def 	A_ (							*snake_case							:   Dict )      ->    int:



						'''simple docstring'''

						if not isinstance(snake_case ,      snake_case ):
												__UpperCamelCase       =			list(snake_case )
						for i in range(len(snake_case ) ):
												__UpperCamelCase       =			None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects




def 	A_ (							snake_case							:   Exception )      ->    bool:



						'''simple docstring'''

						__UpperCamelCase       =			[
						    '''CUDA out of memory.''',  # CUDA OOM
						    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
						    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
						]
						if isinstance(snake_case ,      snake_case ) and len(exception.args ) == 1:
												return any(err in exception.args[0] for err in _statements )
						return False




def 	A_ (							snake_case							:   callable = None ,      snake_case							:   int = 128 )      ->    int:



						'''simple docstring'''

						if function is None:
												return functools.partial(snake_case ,      starting_batch_size=snake_case )

						__UpperCamelCase       =			starting_batch_size

						def decorator(*snake_case							:   str ,      **snake_case							:   List[Any] ):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												__UpperCamelCase       =			list(inspect.signature(snake_case ).parameters.keys() )
												# Guard against user error
												if len(snake_case ) < (len(snake_case ) + 1):
																		__UpperCamelCase       =			''', '''.join([f"{arg}={value}" for arg, value in zip(params[1:] ,      args[1:] )] )
																		raise TypeError(
																		    f"Batch size was passed into `{function.__name__}` as the first argument when called."
																		    f"Remove this as the decorator already does so: `{function.__name__}({arg_str})`" )
												while True:
																		if batch_size == 0:
																								raise RuntimeError('''No executable batch size found, reached zero.''' )
																		try:
																								return function(snake_case ,      *snake_case ,      **snake_case )
																		except Exception as e:
																								if should_reduce_batch_size(snake_case ):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator






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			)			)




