






'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Dict			  =    IFInpaintingPipeline
 __a      :    int			  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 __a      :    Any			  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __a      :    Optional[int]			  =    PipelineTesterMixin.required_optional_params - {'''latents'''}
 def 	_UpperCAmelCase							(				self			):
       return self._get_dummy_components()
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0			):
       if str(__lowerCAmelCase			).startswith("""mps"""			):
             UpperCamelCase_					: Optional[int]            =				torch.manual_seed(__lowerCAmelCase			)
       else:
             UpperCamelCase_					: Dict            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)

       UpperCamelCase_					: str            =				floats_tensor((1, 3, 32, 32)      ,  rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
       UpperCamelCase_					: Optional[Any]            =				floats_tensor((1, 3, 32, 32)      ,  rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)

       UpperCamelCase_					: Dict            =				{
           """prompt""": """A painting of a squirrel eating a burger""",
           """image""": image,
           """mask_image""": mask_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 	_UpperCAmelCase							(				self			):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)
 def 	_UpperCAmelCase							(				self			):
       self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""      ,  reason="""float16 requires CUDA"""			)
 def 	_UpperCAmelCase							(				self			):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1			)
 def 	_UpperCAmelCase							(				self			):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)
 def 	_UpperCAmelCase							(				self			):
       self._test_save_load_local()





 def 	_UpperCAmelCase							(				self			):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2      ,  )














'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






