

'''simple docstring'''



import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation

class       SCREAMING_SNAKE_CASE       (				unittest.TestCase       ):






      '''simple docstring'''






      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		get_activation('swish' )

          self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,       nn.SiLU )

          self.assertEqual(act(torch.tensor(-100  ,       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 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          _snake_case			:			int				       =		get_activation('silu' )

          self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,       nn.SiLU )

          self.assertEqual(act(torch.tensor(-100  ,       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 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			int				       =		get_activation('mish' )

          self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,       nn.Mish )

          self.assertEqual(act(torch.tensor(-200  ,       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 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          _snake_case			:			Tuple				       =		get_activation('gelu' )

          self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,       nn.GELU )

          self.assertEqual(act(torch.tensor(-100  ,       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 )





"""simple docstring"""




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     lowercase(_lowercase    ,						_lowercase    ,						unittest.TestCase       ):
     __snake_case:	Optional[int]						=     IFImgaImgSuperResolutionPipeline
     __snake_case:	Dict						=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     __snake_case:	Any						=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}       )
     __snake_case:	List[Any]						=     PipelineTesterMixin.required_optional_params - {'latents'}



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         return self._get_superresolution_dummy_components()



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     Union[str, Any]:







         """simple docstring"""



         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )

         a__		=			floats_tensor((1, 3, 3_2, 3_2)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			floats_tensor((1, 3, 1_6, 1_6)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )

         a__		=			{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs



     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()     ,				reason='XFormers attention is only available with CUDA and `xformers` installed'     ,				)
     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != 'cuda'     ,				reason='float16 requires CUDA'   )
     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         super().test_save_load_floataa(expected_max_diff=1e-1   )



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







         """simple docstring"""



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         self._test_save_load_local()




     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2     ,				)


