'''simple docstring'''





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    @property
    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       3
         SCREAMING_SNAKE_CASE					:			int    	=       (32, 32)

         SCREAMING_SNAKE_CASE					:			Dict    	=       floats_tensor((batch_size, num_channels) + sizes    ,							rng=random.Random(0 ) ).to(lowercase__ )
         return image






    @property
    def       _UpperCamelCase    (       self )							-> Dict:
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Dict    	=       UNetaDConditionModel(
             block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=4    ,							out_channels=4    ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,							cross_attention_dim=32    ,							)
         return model






    @property
    def       _UpperCamelCase    (       self )							-> Any:
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       AutoencoderKL(
             block_out_channels=[32, 64]    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,							latent_channels=4    ,							)
         return model






    @property
    def       _UpperCamelCase    (       self )							-> int:
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       CLIPTextConfig(
             bos_token_id=0    ,							eos_token_id=2    ,							hidden_size=32    ,							intermediate_size=37    ,							layer_norm_eps=1E-05    ,							num_attention_heads=4    ,							num_hidden_layers=5    ,							pad_token_id=1    ,							vocab_size=1_000    ,							)
         return CLIPTextModel(lowercase__ )






    @property
    def       _UpperCamelCase    (       self )							-> List[Any]:
         def extract(*lowercase__    ,							**lowercase__ ):






              class 				UpperCAmelCase :




                  '''simple docstring'''







                  def __init__(       self )							-> Optional[int]:
                       SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.ones([0] )






                  def       _UpperCamelCase    (       self    ,							lowercase__ )							-> int:
                       self.pixel_values.to(lowercase__ )
                       return self






              return Out()

         return extract






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       'cpu'  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_cond_unet
         SCREAMING_SNAKE_CASE					:			Dict    	=       DDIMScheduler(
             beta_start=0.0_0_0_8_5    ,							beta_end=0.0_1_2    ,							beta_schedule='scaled_linear'    ,							clip_sample=lowercase__    ,							set_alpha_to_one=lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_vae
         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_text_encoder
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         # make sure here that pndm scheduler skips prk
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       StableDiffusionPipeline(
             unet=lowercase__    ,							scheduler=lowercase__    ,							vae=lowercase__    ,							text_encoder=lowercase__    ,							tokenizer=lowercase__    ,							safety_checker=lowercase__    ,							feature_extractor=self.dummy_extractor    ,							)
         SCREAMING_SNAKE_CASE					:			int    	=       sd_pipe.to(lowercase__ )
         sd_pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       'A painting of a squirrel eating a burger'

         SCREAMING_SNAKE_CASE					:			int    	=       torch.Generator(device=lowercase__ ).manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       sd_pipe([prompt]    ,							generator=lowercase__    ,							guidance_scale=6.0    ,							num_inference_steps=2    ,							output_type='np' )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       output.images

         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.Generator(device=lowercase__ ).manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=6.0    ,							num_inference_steps=2    ,							output_type='np'    ,							return_dict=lowercase__    ,							)[0]

         SCREAMING_SNAKE_CASE					:			str    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Dict    	=       'cpu'  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_cond_unet
         SCREAMING_SNAKE_CASE					:			Dict    	=       PNDMScheduler(skip_prk_steps=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_vae
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_text_encoder
         SCREAMING_SNAKE_CASE					:			List[str]    	=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         # make sure here that pndm scheduler skips prk
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       StableDiffusionPipeline(
             unet=lowercase__    ,							scheduler=lowercase__    ,							vae=lowercase__    ,							text_encoder=lowercase__    ,							tokenizer=lowercase__    ,							safety_checker=lowercase__    ,							feature_extractor=self.dummy_extractor    ,							)
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sd_pipe.to(lowercase__ )
         sd_pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger'
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.Generator(device=lowercase__ ).manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       sd_pipe([prompt]    ,							generator=lowercase__    ,							guidance_scale=6.0    ,							num_inference_steps=2    ,							output_type='np' )

         SCREAMING_SNAKE_CASE					:			Any    	=       output.images

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.Generator(device=lowercase__ ).manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=6.0    ,							num_inference_steps=2    ,							output_type='np'    ,							return_dict=lowercase__    ,							)[0]

         SCREAMING_SNAKE_CASE					:			int    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         SCREAMING_SNAKE_CASE					:			Any    	=       np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Dict    	=       StableDiffusionPipeline.from_pretrained(
             'hf-internal-testing/tiny-stable-diffusion-lms-pipe'    ,							safety_checker=lowercase__ )
         assert isinstance(lowercase__    ,							lowercase__ )
         assert isinstance(pipe.scheduler    ,							lowercase__ )
         assert pipe.safety_checker is None

         SCREAMING_SNAKE_CASE					:			int    	=       pipe('example prompt'    ,							num_inference_steps=2 ).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       StableDiffusionPipeline.from_pretrained(lowercase__ )

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pipe('example prompt'    ,							num_inference_steps=2 ).images[0]
         assert image is not None






    @unittest.skipIf(torch_device != 'cuda'    ,							'This test requires a GPU' )
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_cond_unet
         SCREAMING_SNAKE_CASE					:			int    	=       PNDMScheduler(skip_prk_steps=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_vae
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_text_encoder
         SCREAMING_SNAKE_CASE					:			Tuple    	=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         # put models in fp16
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       unet.half()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       vae.half()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       bert.half()

         # make sure here that pndm scheduler skips prk
         SCREAMING_SNAKE_CASE					:			List[str]    	=       StableDiffusionPipeline(
             unet=lowercase__    ,							scheduler=lowercase__    ,							vae=lowercase__    ,							text_encoder=lowercase__    ,							tokenizer=lowercase__    ,							safety_checker=lowercase__    ,							feature_extractor=self.dummy_extractor    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       sd_pipe.to(lowercase__ )
         sd_pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       'A painting of a squirrel eating a burger'
         SCREAMING_SNAKE_CASE					:			Dict    	=       sd_pipe([prompt]    ,							num_inference_steps=2    ,							output_type='np' ).images

         assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Dict    	=       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    ,							safety_checker=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sd_pipe.to(lowercase__ )
         sd_pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (
             'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
             ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
             ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
             ' children from bahnhof zoo, detailed '
         )
         SCREAMING_SNAKE_CASE					:			str    	=       4_003_660_346
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       7

         # without safety guidance (sld_guidance_scale = 0)
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=lowercase__    ,							num_inference_steps=50    ,							output_type='np'    ,							width=512    ,							height=512    ,							sld_guidance_scale=0    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       output.images
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			int    	=       [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

         # without safety guidance (strong configuration)
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.manual_seed(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=lowercase__    ,							num_inference_steps=50    ,							output_type='np'    ,							width=512    ,							height=512    ,							sld_guidance_scale=2_000    ,							sld_warmup_steps=7    ,							sld_threshold=0.0_2_5    ,							sld_momentum_scale=0.5    ,							sld_mom_beta=0.7    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       output.images
         SCREAMING_SNAKE_CASE					:			int    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    ,							safety_checker=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       sd_pipe.to(lowercase__ )
         sd_pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'padme amidala taking a bath artwork, safe for work, no nudity'
         SCREAMING_SNAKE_CASE					:			int    	=       2_734_971_755
         SCREAMING_SNAKE_CASE					:			List[str]    	=       7

         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=lowercase__    ,							num_inference_steps=50    ,							output_type='np'    ,							width=512    ,							height=512    ,							sld_guidance_scale=0    ,							)

         SCREAMING_SNAKE_CASE					:			Tuple    	=       output.images
         SCREAMING_SNAKE_CASE					:			int    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=lowercase__    ,							num_inference_steps=50    ,							output_type='np'    ,							width=512    ,							height=512    ,							sld_guidance_scale=2_000    ,							sld_warmup_steps=7    ,							sld_threshold=0.0_2_5    ,							sld_momentum_scale=0.5    ,							sld_mom_beta=0.7    ,							)

         SCREAMING_SNAKE_CASE					:			str    	=       output.images
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Dict    	=       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       sd_pipe.to(lowercase__ )
         sd_pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       (
             'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
             ' leyendecker'
         )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       1_044_355_234
         SCREAMING_SNAKE_CASE					:			Tuple    	=       12

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.manual_seed(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=lowercase__    ,							num_inference_steps=50    ,							output_type='np'    ,							width=512    ,							height=512    ,							sld_guidance_scale=0    ,							)

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       output.images
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			Any    	=       np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )

         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7

         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.manual_seed(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       sd_pipe(
             [prompt]    ,							generator=lowercase__    ,							guidance_scale=lowercase__    ,							num_inference_steps=50    ,							output_type='np'    ,							width=512    ,							height=512    ,							sld_guidance_scale=2_000    ,							sld_warmup_steps=7    ,							sld_threshold=0.0_2_5    ,							sld_momentum_scale=0.5    ,							sld_mom_beta=0.7    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       output.images
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE					:			str    	=       np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         get_activation('gelu' )
         get_activation('gelu_10' )
         get_activation('gelu_fast' )
         get_activation('gelu_new' )
         get_activation('gelu_python' )
         get_activation('gelu_pytorch_tanh' )
         get_activation('linear' )
         get_activation('mish' )
         get_activation('quick_gelu' )
         get_activation('relu' )
         get_activation('sigmoid' )
         get_activation('silu' )
         get_activation('swish' )
         get_activation('tanh' )
         with self.assertRaises(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



