


'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class   __lowerCAmelCase						(lowercase_					):





      '''simple docstring'''






      def __init__(self   :   List[str]       ,						UpperCamelCase   :   Union[str, Any]       ,						UpperCamelCase   :   Dict			):

             '''simple docstring'''







             super().__init__()
             self.register_modules(unet=UpperCamelCase       ,						scheduler=UpperCamelCase			)






      @torch.no_grad()
      def __call__(self   :   Optional[Any]       ,						UpperCamelCase   :   int = 1       ,						UpperCamelCase   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,						UpperCamelCase   :   int = 1000       ,						UpperCamelCase   :   Optional[str] = "pil"       ,						UpperCamelCase   :   bool = True       ,						):

             '''simple docstring'''







             if isinstance(self.unet.config.sample_size       ,						UpperCamelCase			):
                    lowercase__				      =			(
                        batch_size,
                        self.unet.config.in_channels,
                        self.unet.config.sample_size,
                        self.unet.config.sample_size,
                    )
             else:
                    lowercase__				      =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase			)








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

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

             lowercase__				      =			{
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''image''': image,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }

             return inputs



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             self._test_save_load_optional_components()



      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()       ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,						)
      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







             super().test_save_load_floataa(expected_max_diff=1E-1			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             super().test_floataa_inference(expected_max_diff=1E-1			)



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







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




