import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 		a__						( A__   ,		A__   ,		unittest.TestCase ):
					A     	=							IFImgaImgPipeline
					A     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
					A     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					A     	=							PipelineTesterMixin.required_optional_params - {'latents'}

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							return self._get_dummy_components()

					def     __UpperCamelCase       (   self				:	List[str],_A				:	List[str],_A				:	List[Any]=0   ):







							"""simple docstring"""






							if str(_A   ).startswith("mps"   ):
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.manual_seed(_A   )
							else:
									SCREAMING_SNAKE_CASE_  :		Any    				=   torch.Generator(device=_A   ).manual_seed(_A   )

							SCREAMING_SNAKE_CASE_  :		int    				=   floats_tensor((1, 3, 32, 32),rng=random.Random(_A   )   ).to(_A   )

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}

							return inputs

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							self._test_save_load_optional_components()

					@unittest.skipIf(
					    torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )

					@unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA"   )
					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






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

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







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							self._test_save_load_local()






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






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

from __future__ import annotations






class 		a__						:

					def __init__(   self				:	Union[str, Any],_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   value
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None







class 		a__						:
					A     	=							42

					def __init__(   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   0

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	Node | None,_A				:	int   ):







							"""simple docstring"""






							if node is None:
									return

							if path_sum == self.target:
									self.paths += 1

							if node.left:
									self.depth_first_search(node.left,path_sum + node.left.value   )
							if node.right:
									self.depth_first_search(node.right,path_sum + node.right.value   )






					def     __UpperCamelCase       (   self				:	List[str],_A				:	Node | None,_A				:	int | None = None   ):







							"""simple docstring"""






							if node is None:
									return 0
							if target is not None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   target

							self.depth_first_search(_A,node.value   )
							self.path_sum(node.left   )
							self.path_sum(node.right   )

							return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
