





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  :     Any   			=logging.get_logger(__name__)

class  __A       (  _UpperCamelCase      ):
				UpperCAmelCase_								=			'encoder-decoder'
				UpperCAmelCase_								=			True


				def __init__(						self				,	**UpperCAmelCase_							):
								super().__init__(**_UpperCAmelCase							)
								assert (
								    "encoder" in kwargs and "decoder" in kwargs
								), "Config has to be initialized with encoder and decoder config"
								lowerCamelCase			=kwargs.pop("""encoder"""							)
								lowerCamelCase			=encoder_config.pop("""model_type"""							)
								lowerCamelCase			=kwargs.pop("""decoder"""							)
								lowerCamelCase			=decoder_config.pop("""model_type"""							)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase			=AutoConfig.for_model(_UpperCAmelCase				,	**_UpperCAmelCase							)
								lowerCamelCase			=AutoConfig.for_model(_UpperCAmelCase				,	**_UpperCAmelCase							)
								lowerCamelCase			=True


				@classmethod
				def _snake_case    (						cls				,	UpperCAmelCase_				,	UpperCAmelCase_				,	**UpperCAmelCase_							):
								logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""							)
								lowerCamelCase			=True
								lowerCamelCase			=True

								return cls(encoder=encoder_config.to_dict()				,	decoder=decoder_config.to_dict()				,	**_UpperCAmelCase							)


				def _snake_case    (						self							):
								lowerCamelCase			=copy.deepcopy(self.__dict__							)
								lowerCamelCase			=self.encoder.to_dict()
								lowerCamelCase			=self.decoder.to_dict()
								lowerCamelCase			=self.__class__.model_type
								return output












import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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       (  a      ,   a      ,   unittest.TestCase      ):
				__A								=			IFInpaintingSuperResolutionPipeline
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}      )
				__A								=			PipelineTesterMixin.required_optional_params - {"""latents"""}


				def _snake_case    (						self							):
								return self._get_superresolution_dummy_components()


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=0							):
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase			=torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase			=torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)

								lowerCamelCase			=floats_tensor((1, 3, 16, 16)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)

								lowerCamelCase			={
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": image,
								    """original_image""": original_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 _snake_case    (						self							):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


				def _snake_case    (						self							):
								self._test_save_load_optional_components()


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


				def _snake_case    (						self							):
								self._test_save_load_local()


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





