"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__lowerCamelCase											=  {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  [
				    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ASTForAudioClassification",
				    "ASTModel",
				    "ASTPreTrainedModel",
				]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  ["ASTFeatureExtractor"]

if TYPE_CHECKING:
				from .configuration_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ASTConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_audio_spectrogram_transformer import (
								    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ASTForAudioClassification,
								    ASTModel,
								    ASTPreTrainedModel,
								)

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
				import sys

				__lowerCamelCase											=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_activation('swish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_activation('silu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('mish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.Mish     )

													self.assertEqual(act(torch.tensor(-2_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('gelu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.GELU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





