



import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation







class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						str)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_activation('swish')

						self.assertIsInstance(A				,	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 					_lowerCamelCase (		self     :						Optional[Any])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_activation('silu')

						self.assertIsInstance(A				,	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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_activation('mish')

						self.assertIsInstance(A				,	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 					_lowerCamelCase (		self     :						str)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_activation('gelu')

						self.assertIsInstance(A				,	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)





from typing import TYPE_CHECKING

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


UpperCAmelCase__     = {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CTRLForSequenceClassification",
							    "CTRLLMHeadModel",
							    "CTRLModel",
							    "CTRLPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCTRLForSequenceClassification",
							    "TFCTRLLMHeadModel",
							    "TFCTRLModel",
							    "TFCTRLPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

else:
							import sys

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