






import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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_      (  __lowercase	,				__lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   IFSuperResolutionPipeline
 _SCREAMING_SNAKE_CASE							:     Tuple	=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 _SCREAMING_SNAKE_CASE							:     int	=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE							:     List[str]	=   PipelineTesterMixin.required_optional_params - {"latents"}


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     return self._get_superresolution_dummy_components()


 def 	snake_case__			(		self							,					_A							,					_A=0)	->						str:

     """simple docstring"""



     if str(_A).startswith('''mps'''):
         _UpperCAmelCase			: int     					=			torch.manual_seed(_A)
     else:
         _UpperCAmelCase			: Tuple     					=			torch.Generator(device=_A).manual_seed(_A)

     _UpperCAmelCase			: Optional[int]     					=			floats_tensor((1, 3, 32, 32)							,					rng=random.Random(_A)).to(_A)

     _UpperCAmelCase			: int     					=			{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': 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 	snake_case__			(		self)	->						str:

     """simple docstring"""



     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     self._test_save_load_optional_components()


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

     """simple docstring"""



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


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

     """simple docstring"""



     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     self._test_save_load_local()


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



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












def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int	) ->   str:
    if not root or root not in binary_tree_mirror_dictionary:
        return
    _UpperCAmelCase					,	_UpperCAmelCase			: int     					=			binary_tree_mirror_dictionary[root][:2]
    _UpperCAmelCase			: int     					=			[right_child, left_child]
    binary_tree_mirror_dict(__A						,						__A	)
    binary_tree_mirror_dict(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int = 1	) ->   dict:
    if not binary_tree:
        raise ValueError('''binary tree cannot be empty'''	)
    if root not in binary_tree:
        _UpperCAmelCase			: Dict     					=			f'''root {root} is not present in the binary_tree'''
        raise ValueError(__A	)
    _UpperCAmelCase			: int     					=			dict(__A	)
    binary_tree_mirror_dict(__A						,						__A	)
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE								=  {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'Binary tree: {binary_tree}')
  SCREAMING_SNAKE_CASE								=  binary_tree_mirror(binary_tree, 5)
  print(F'Binary tree mirror: {binary_tree_mirror_dictionary}')




