
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   __lowercase						(						__snake_case   , __snake_case   , unittest.TestCase       ):
	_A       =   IFInpaintingSuperResolutionPipeline
	_A       =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	_A       =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
	_A       =   PipelineTesterMixin.required_optional_params - {"latents"}






	def       _a(self				: Union[str, Any]    ) ->			int:
							return self._get_superresolution_dummy_components()






	def       _a(self				: Tuple      ,	snake_case				: List[str]      ,	snake_case				: Optional[int]=0    ) ->			Optional[Any]:
							if str(snake_case    ).startswith("mps"    ):
													_lowercase       :	List[str]           =   torch.manual_seed(snake_case    )
							else:
													_lowercase       :	List[Any]           =   torch.Generator(device=snake_case    ).manual_seed(snake_case    )

							_lowercase       :	Union[str, Any]           =   floats_tensor((1, 3, 16, 16)      ,	rng=random.Random(snake_case    )    ).to(snake_case    )
							_lowercase       :	Dict           =   floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(snake_case    )    ).to(snake_case    )
							_lowercase       :	Optional[Any]           =   floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(snake_case    )    ).to(snake_case    )

							_lowercase       :	Tuple           =   {
							    "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				: Dict    ) ->			Optional[Any]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )






	def       _a(self				: Any    ) ->			int:
							self._test_save_load_optional_components()






	@unittest.skipIf(torch_device != "cuda"      ,	reason="float16 requires CUDA"    )
	def       _a(self				: List[str]    ) ->			List[str]:
							# 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       _a(self				: List[str]    ) ->			Union[str, Any]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )






	def       _a(self				: List[str]    ) ->			Optional[Any]:
							self._test_save_load_local()






	def       _a(self				: Optional[Any]    ) ->			str:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2      ,	)






import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or not all(
						    isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and len(_UpperCAmelCase  ) > 0 for item in words  ):
												raise ValueError("the words should be a list of non-empty strings"  )

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

						# Dynamic programming method
						@functools.cache
						def is_breakable(_UpperCAmelCase  :   int  ) -> bool:
												if index == len_string:
																		return True

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

																		if trie_node.get(_UpperCAmelCase ,			_UpperCAmelCase  ) and is_breakable(i + 1  ):
																								return True

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




