
'''simple docstring'''



import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf

						from transformers.activations_tf import get_tf_activation




@require_tf
class   UpperCAmelCase_     (		unittest.TestCase ):






						'''simple docstring'''






						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]      )
												_lowerCAmelCase  						= get_tf_activation("""gelu"""      )
												_lowerCAmelCase  						= get_tf_activation("""gelu_10"""      )

												_lowerCAmelCase  						= gelu(A__      )
												_lowerCAmelCase  						= geluaa(A__      )

												_lowerCAmelCase  						= tf.where(y_gelu_aa < 10.0    ,   1.0    ,   0.0      )

												self.assertEqual(tf.math.reduce_max(A__      ).numpy().item()    ,   10.0      )
												self.assertTrue(np.allclose(y_gelu * clipped_mask    ,   y_gelu_aa * clipped_mask      )      )




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												get_tf_activation("""gelu"""      )
												get_tf_activation("""gelu_10"""      )
												get_tf_activation("""gelu_fast"""      )
												get_tf_activation("""gelu_new"""      )
												get_tf_activation("""glu"""      )
												get_tf_activation("""mish"""      )
												get_tf_activation("""quick_gelu"""      )
												get_tf_activation("""relu"""      )
												get_tf_activation("""sigmoid"""      )
												get_tf_activation("""silu"""      )
												get_tf_activation("""swish"""      )
												get_tf_activation("""tanh"""      )
												with self.assertRaises(A__      ):
																		get_tf_activation("""bogus"""      )
												with self.assertRaises(A__      ):
																		get_tf_activation(A__      )


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

          # set step values
          self.scheduler.set_timesteps(A__					)

          for t in self.progress_bar(self.scheduler.timesteps					):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE          = self.unet(A__				,							A__					).sample

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

          _SCREAMING_SNAKE_CASE          = (image / 2 + 0.5).clamp(0				,							1					)
          _SCREAMING_SNAKE_CASE          = image.cpu().permute(0				,							2				,							3				,							1					).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE          = self.numpy_to_pil(A__					)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






