

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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    a__  (   snake_case							,		snake_case							,		unittest.TestCase     ):
			"""simple docstring"""







			__lowerCamelCase	    =    IFSuperResolutionPipeline
			__lowerCamelCase	    =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
			__lowerCamelCase	    =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__lowerCamelCase	    =    PipelineTesterMixin.required_optional_params - {'latents'}


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							return self._get_superresolution_dummy_components()


			def        UpperCamelCase						(      self , lowercase , lowercase=0    )					->  Dict:
							'''simple docstring'''






							if str(lowercase    ).startswith("mps"    ):
											A__            =     torch.manual_seed(lowercase    )
							else:
											A__            =     torch.Generator(device=lowercase    ).manual_seed(lowercase    )

							A__            =     floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase    )    ).to(lowercase    )

							A__            =     {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": 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    )					->  str:
							'''simple docstring'''






							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							self._test_save_load_optional_components()


			@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA"    )
			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							self._test_save_load_local()


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__						=	{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """NatForImageClassification""",
	    """NatModel""",
	    """NatPreTrainedModel""",
	    """NatBackbone""",
	]

if TYPE_CHECKING:
	from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nat import (
		    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NatBackbone,
		    NatForImageClassification,
		    NatModel,
		    NatPreTrainedModel,
		)

else:
	import sys

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

