





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 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    IFInpaintingSuperResolutionPipeline
     __snake_case					:			Union[str, Any]				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
     __snake_case					:			str				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}     )
     __snake_case					:			str				=    PipelineTesterMixin.required_optional_params - {"latents"}






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Tuple:




           '''simple docstring'''


           return self._get_superresolution_dummy_components()






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Union[str, Any]=0 )  ->	int:




           '''simple docstring'''


           if str(lowerCamelCase__ ).startswith("""mps""" ):
                 SCREAMING_SNAKE_CASE	=		torch.manual_seed(lowerCamelCase__ )
           else:
                 SCREAMING_SNAKE_CASE	=		torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 16, 16)		,rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 32, 32)		,rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 32, 32)		,rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		{
               """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        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Tuple:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )






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




           '''simple docstring'''


           self._test_save_load_optional_components()






     @unittest.skipIf(torch_device != """cuda"""		,reason="""float16 requires CUDA""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Dict:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )






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




           '''simple docstring'''


           self._test_save_load_local()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	List[str]:




           '''simple docstring'''


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










import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					int		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					List[Any] )  ->	None:




           '''simple docstring'''


           warnings.warn(
               """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
               """ Please use DeformableDetrImageProcessor instead."""		,lowerCamelCase__		,)
           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )


