'''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      ( __snake_case		,	__snake_case		,	unittest.TestCase	):
    a__       :						int          =  IFInpaintingPipeline
    a__       :						Any          =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
    a__       :						Union[str, Any]          =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    a__       :						Optional[int]          =  PipelineTesterMixin.required_optional_params - {"""latents"""}



    def 					_lowercase						(self    :    Dict ):
         return self._get_dummy_components()



    def 					_lowercase						(self    :    str   ,			__a    :    List[Any]   ,			__a    :    Any=0 ):

         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 					_lowercase						(self    :    int ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )



    def 					_lowercase						(self    :    str ):
         self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != "cuda"   ,			reason="float16 requires CUDA" )
    def 					_lowercase						(self    :    str ):
         super().test_save_load_floataa(expected_max_diff=1E-1 )



    def 					_lowercase						(self    :    Optional[Any] ):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )



    def 					_lowercase						(self    :    Any ):
         self._test_save_load_local()






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








"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





