


"""simple docstring"""




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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				IFInpaintingSuperResolutionPipeline
		__UpperCAmelCase		:			Optional[int]										=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
		__UpperCAmelCase		:			int										=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}       )
		__UpperCAmelCase		:			Optional[Any]										=				PipelineTesterMixin.required_optional_params - {'latents'}

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

		def 			__UpperCAmelCase       (  self							, _a							, _a=0      ):
				if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
						__a           =			torch.manual_seed(SCREAMING_SNAKE_CASE_      )
				else:
						__a           =			torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )

				__a           =			floats_tensor((1, 3, 16, 16)							, rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )
				__a           =			floats_tensor((1, 3, 32, 32)							, rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )
				__a           =			floats_tensor((1, 3, 32, 32)							, rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )

				__a           =			{
				    '''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 			__UpperCAmelCase       (  self      ):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )

		def 			__UpperCAmelCase       (  self      ):
				self._test_save_load_optional_components()

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

		def 			__UpperCAmelCase       (  self      ):
				self._test_save_load_local()





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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




