




from __future__ import annotations





class 		A__				:






	def __init__(	self				,     lowerCamelCase = 0						)      ->   None:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							value
						__magic_name__					:							Node | None		   =							None
						__magic_name__					:							Node | None		   =							None







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Node | None:






					"""simple docstring"""






					if treea is None:
										return treea
					if treea is None:
										return treea

					__magic_name__					:							Tuple		   =							treea.value + treea.value
					__magic_name__					:							List[Any]		   =							merge_two_binary_trees(treea.left,	treea.left       )
					__magic_name__					:							int		   =							merge_two_binary_trees(treea.right,	treea.right       )
					return treea







def  lowerCAmelCase  (	UpperCAmelCase       )  ->None:






					"""simple docstring"""






					if root:
										print(root.value       )
										print_preorder(root.left       )
										print_preorder(root.right       )


if __name__ == "__main__":
				lowercase_		=							Node(1)
				lowercase_		=							Node(2)
				lowercase_		=							Node(3)
				lowercase_		=							Node(4)

				lowercase_		=							Node(2)
				lowercase_		=							Node(4)
				lowercase_		=							Node(6)
				lowercase_		=							Node(9)
				lowercase_		=							Node(5)

				print('''Tree1 is: ''')
				print_preorder(treea)
				print('''Tree2 is: ''')
				print_preorder(treea)
				lowercase_		=							merge_two_binary_trees(treea, treea)
				print('''Merged Tree is: ''')
				print_preorder(merged_tree)











"""simple docstring"""


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 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

      a     :			Dict				=   {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''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 				lowerCamelCase__      (							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 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




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





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


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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




