





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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 SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     str	 =    IFImgaImgSuperResolutionPipeline
						__magic_name__:     Union[str, Any]	 =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
						__magic_name__:     Optional[Any]	 =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}   )
						__magic_name__:     str	 =    PipelineTesterMixin.required_optional_params - {"latents"}






						def UpperCAmelCase_			( self : Tuple		)							->      str:







							"""simple docstring"""


							return self._get_superresolution_dummy_components()






						def UpperCAmelCase_			( self : Dict						,				_A : Dict						,				_A : Optional[int]=0		)							->      str:







							"""simple docstring"""


							if str(__UpperCAmelCase		).startswith('mps'		):
								snake_case_						:     Any     =		torch.manual_seed(__UpperCAmelCase		)
							else:
								snake_case_						:     List[str]     =		torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)

							snake_case_						:     str     =		floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
							snake_case_						:     Optional[int]     =		floats_tensor((1, 3, 16, 16)						,				rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)

							snake_case_						:     int     =		{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'original_image': original_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 UpperCAmelCase_			( self : Tuple		)							->      List[str]:







							"""simple docstring"""


							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      Any:







							"""simple docstring"""


							self._test_save_load_optional_components()






						@unittest.skipIf(torch_device != 'cuda'						,				reason='float16 requires CUDA'		)
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Union[str, Any]:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : Optional[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)






						def UpperCAmelCase_			( self : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							self._test_save_load_local()






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Tuple:







							"""simple docstring"""


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












from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

