

'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = False			,_snake_case = False			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      int						=  path_or_paths
				UpperCAmelCase_     :      Optional[int]						=  split if split or isinstance(_snake_case			,_snake_case     ) else "train"
				UpperCAmelCase_     :      Tuple						=  features
				UpperCAmelCase_     :      int						=  cache_dir
				UpperCAmelCase_     :      Optional[Any]						=  keep_in_memory
				UpperCAmelCase_     :      Any						=  streaming
				UpperCAmelCase_     :      List[str]						=  num_proc
				UpperCAmelCase_     :      int						=  kwargs






			@abstractmethod
			def 		UpperCamelCase__							(  self     ):
				pass




class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case = None			,_snake_case = None			,_snake_case = False			,_snake_case = False			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      Union[str, Any]						=  features
				UpperCAmelCase_     :      Dict						=  cache_dir
				UpperCAmelCase_     :      int						=  keep_in_memory
				UpperCAmelCase_     :      int						=  streaming
				UpperCAmelCase_     :      Tuple						=  num_proc
				UpperCAmelCase_     :      Optional[Any]						=  kwargs






			@abstractmethod
			def 		UpperCamelCase__							(  self     ):
				pass









'''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 __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







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



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self._get_superresolution_dummy_components()



 def    a__    (self					,  A					,  A=0			)       ->	List[Any]:


      """simple docstring"""



      if str(A			).startswith('''mps'''			):
           _a           =    torch.manual_seed(A			)
      else:
           _a           =    torch.Generator(device=A			).manual_seed(A			)

      _a           =    floats_tensor((1, 3, 16, 16)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)

      _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    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != '''cuda'''					,  reason='''float16 requires CUDA'''			)
 def    a__    (self			)       ->	str:


      """simple docstring"""



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



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



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


      """simple docstring"""



      self._test_save_load_local()



 def    a__    (self			)       ->	Any:


      """simple docstring"""



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



