


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   ):

     def   UpperCAmelCase__       (     self       :   List[Any]       ):






          '''simple docstring'''






          lowercase_  							=			torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]       )
          lowercase_  							=			get_activation("""gelu"""       )
          self.assertTrue(torch.allclose(gelu_python(UpperCamelCase__       )							,					torch_builtin(UpperCamelCase__       )       )       )
          self.assertFalse(torch.allclose(gelu_python(UpperCamelCase__       )							,					gelu_new(UpperCamelCase__       )       )       )

     def   UpperCAmelCase__       (     self       :   Any       ):






          '''simple docstring'''






          lowercase_  							=			torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]       )
          lowercase_  							=			get_activation("""gelu"""       )
          lowercase_  							=			get_activation("""gelu_10"""       )

          lowercase_  							=			torch_builtin(UpperCamelCase__       )
          lowercase_  							=			geluaa(UpperCamelCase__       )

          lowercase_  							=			torch.where(y_gelu_aa < 10.0							,					1							,					0       )

          self.assertTrue(torch.max(UpperCamelCase__       ).item() == 10.0       )
          self.assertTrue(torch.allclose(y_gelu * clipped_mask							,					y_gelu_aa * clipped_mask       )       )

     def   UpperCAmelCase__       (     self       :   Tuple       ):






          '''simple docstring'''






          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(UpperCamelCase__       ):
               get_activation("""bogus"""       )
          with self.assertRaises(UpperCamelCase__       ):
               get_activation(UpperCamelCase__       )







     def   UpperCAmelCase__       (     self       :   Dict       ):






          '''simple docstring'''






          lowercase_  							=			get_activation("""gelu"""       )
          lowercase_  							=			1
          lowercase_  							=			get_activation("""gelu"""       )
          self.assertEqual(acta.a							,					1       )
          with self.assertRaises(UpperCamelCase__       ):
               lowercase_  							=			acta.a






import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
							a					  =     {
							    'linear': PIL.Image.Resampling.BILINEAR,
							    'bilinear': PIL.Image.Resampling.BILINEAR,
							    'bicubic': PIL.Image.Resampling.BICUBIC,
							    'lanczos': PIL.Image.Resampling.LANCZOS,
							    'nearest': PIL.Image.Resampling.NEAREST,
							}
else:
							a					  =     {
							    'linear': PIL.Image.LINEAR,
							    'bilinear': PIL.Image.BILINEAR,
							    'bicubic': PIL.Image.BICUBIC,
							    'lanczos': PIL.Image.LANCZOS,
							    'nearest': PIL.Image.NEAREST,
							}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			(images / 2 + 0.5).clamp(0						,							1  )
					lowercase_  							=			images.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()
					lowercase_  							=			numpy_to_pil(UpperCAmelCase__  )
					return images



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if images.ndim == 3:
										lowercase_  							=			images[None, ...]
					lowercase_  							=			(images * 2_5_5).round().astype("""uint8"""  )
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										lowercase_  							=			[Image.fromarray(image.squeeze()						,							mode="""L"""  ) for image in images]
					else:
										lowercase_  							=			[Image.fromarray(UpperCAmelCase__  ) for image in images]

					return pil_images


