


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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 __snake_case		( __UpperCamelCase  ,				__UpperCamelCase  ,				unittest.TestCase ):





		"""simple docstring"""







		UpperCamelCase_				      =   IFInpaintingPipeline
		UpperCamelCase_				      =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
		UpperCamelCase_				      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		UpperCamelCase_				      =   PipelineTesterMixin.required_optional_params - {'latents'}



		def  UpperCAmelCase_							(							self    :   Tuple      )      ->							Optional[int]:




								'''simple docstring'''

								return self._get_dummy_components()



		def  UpperCAmelCase_							(							self    :   Optional[Any]						,lowerCAmelCase__    :   Any						,lowerCAmelCase__    :   int=0      )      ->							Tuple:




								'''simple docstring'''

								if str(lowerCAmelCase__      ).startswith("mps"      ):
														lowerCAmelCase_	:     Optional[Any]          = torch.manual_seed(lowerCAmelCase__      )
								else:
														lowerCAmelCase_	:     List[Any]          = torch.Generator(device=lowerCAmelCase__      ).manual_seed(lowerCAmelCase__      )

								lowerCAmelCase_	:     Optional[Any]          = floats_tensor((1, 3, 32, 32)						,rng=random.Random(lowerCAmelCase__      )      ).to(lowerCAmelCase__      )
								lowerCAmelCase_	:     Union[str, Any]          = floats_tensor((1, 3, 32, 32)						,rng=random.Random(lowerCAmelCase__      )      ).to(lowerCAmelCase__      )

								lowerCAmelCase_	:     Any          = {
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": 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    :   Union[str, Any]      )      ->							Tuple:




								'''simple docstring'''

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )



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




								'''simple docstring'''

								self._test_save_load_optional_components()



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




								'''simple docstring'''

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



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




								'''simple docstring'''

								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )



		def  UpperCAmelCase_							(							self    :   int      )      ->							Dict:




								'''simple docstring'''

								self._test_save_load_local()



		def  UpperCAmelCase_							(							self    :   int      )      ->							List[Any]:




								'''simple docstring'''

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




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



