"""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	):
     _UpperCamelCase							:    int	      =	IFInpaintingPipeline
     _UpperCamelCase							:    str	      =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
     _UpperCamelCase							:    List[str]	      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _UpperCamelCase							:    Dict	      =	PipelineTesterMixin.required_optional_params - {"latents"}






     def 					__A						(	self						):
       return self._get_dummy_components()






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Dict						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Optional[Any]						    =  torch.Generator(device=a__						).manual_seed(a__						)

       _lowerCAmelCase		: Dict						    =  floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(a__						)						).to(a__						)
       _lowerCAmelCase		: List[str]						    =  floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(a__						)						).to(a__						)

       _lowerCAmelCase		: str						    =  {
           """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 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)






     def 					__A						(	self						):
       self._test_save_load_optional_components()






     @unittest.skipIf(torch_device != """cuda"""       ,  reason="""float16 requires CUDA"""						)
     def 					__A						(	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 					__A						(	self						):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)






     def 					__A						(	self						):
       self._test_save_load_local()






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2       ,  )


"""simple docstring"""




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




