





"""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 					__lowercase      (      _UpperCamelCase ,   _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	IFInpaintingPipeline
					__lowerCAmelCase	=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
					__lowerCAmelCase	=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__lowerCAmelCase	=	PipelineTesterMixin.required_optional_params - {'''latents'''}







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







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=0  ):
									if str(_UpperCAmelCase  ).startswith('''mps'''  ):
													__a			:    str      							=	torch.manual_seed(_UpperCAmelCase  )
									else:
													__a			:    List[Any]      							=	torch.Generator(device=_UpperCAmelCase  ).manual_seed(_UpperCAmelCase  )

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

									__a			:    Optional[Any]      							=	{
									    '''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 			_lowerCamelCase		(    self  ):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )







					def 			_lowerCamelCase		(    self  ):
									self._test_save_load_optional_components()







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







					def 			_lowerCamelCase		(    self  ):
									self._test_save_load_local()







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

"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




