




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



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





				@torch.no_grad()
				def __call__(				self    :					List[Any] ,				__lowerCamelCase    :					int = 1 ,				__lowerCamelCase    :					Optional[torch.Generator] = None ,				__lowerCamelCase    :					int = 50 ,				__lowerCamelCase    :					Optional[str] = "pil" ,				__lowerCamelCase    :					bool = True ,				**__lowerCamelCase    :					Optional[int] ,				) ->					Union[ImagePipelineOutput, Tuple]:
							a   =      torch.randn(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,				generator=__lowerCamelCase ,				)
							a   =      image.to(self.device							)

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

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

										# 2. predict previous mean of image x_t-1 and add variance depending on eta
										# eta corresponds to η in paper and should be between [0, 1]
										# do x_t -> x_t-1
										a   =      self.scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							).prev_sample

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

							if not return_dict:
										return (image,), "This is a local test"

							return ImagePipelineOutput(images=__lowerCamelCase							), "This is a local test"












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






