
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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       UNetaDModel
		__lowercase       :       ScoreSdeVeScheduler



		def __init__( self				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				super().__init__()
				self.register_modules(unet=A_				,      scheduler=A_       )



		@torch.no_grad()
		def __call__( self				,      A_ = 1				,      A_ = 2_000				,      A_ = None				,      A_ = "pil"				,      A_ = True				,      **A_				,      )  ->					Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				UpperCamelCase		=     self.unet.config.sample_size
				UpperCamelCase		=     (batch_size, 3, img_size, img_size)

				UpperCamelCase		=     self.unet

				UpperCamelCase		=     randn_tensor(A_				,      generator=A_       ) * self.scheduler.init_noise_sigma
				UpperCamelCase		=     sample.to(self.device       )

				self.scheduler.set_timesteps(A_       )
				self.scheduler.set_sigmas(A_       )

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

						# correction step
						for _ in range(self.scheduler.config.correct_steps       ):
								UpperCamelCase		=     self.unet(A_				,      A_       ).sample
								UpperCamelCase		=     self.scheduler.step_correct(A_				,      A_				,      generator=A_       ).prev_sample

						# prediction step
						UpperCamelCase		=     model(A_				,      A_       ).sample
						UpperCamelCase		=     self.scheduler.step_pred(A_				,      A_				,      A_				,      generator=A_       )

						UpperCamelCase	,  UpperCamelCase		=     output.prev_sample, output.prev_sample_mean

				UpperCamelCase		=     sample_mean.clamp(0				,      1       )
				UpperCamelCase		=     sample.cpu().permute(0				,      2				,      3				,      1       ).numpy()
				if output_type == "pil":
						UpperCamelCase		=     self.numpy_to_pil(A_       )

				if not return_dict:
						return (sample,)

				return ImagePipelineOutput(images=A_       )


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 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    '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       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# 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       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
