



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    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   List[Any]						 =						IFInpaintingPipeline
       UpperCamelCase__  :   List[Any]						 =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
       UpperCamelCase__  :   int						 =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase__  :   int						 =						PipelineTesterMixin.required_optional_params - {'''latents'''}



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           return self._get_dummy_components()



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dict=0):


           '''simple docstring'''

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)

           __a							=    floats_tensor((1, 3, 32, 32)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    floats_tensor((1, 3, 32, 32)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)

           __a							=    {
               '''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 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           self._test_save_load_optional_components()



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


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           self._test_save_load_local()





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

    # Validation
    def count_nodes(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

    def count_coins(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_coins(node.left    ) + count_coins(node.right    ) + node.data

    if count_nodes(_UpperCAmelCase    ) != count_coins(_UpperCAmelCase    ):
        raise ValueError('''The nodes number should be same as the number of coins'''    )

    # Main calculation
    def get_distrib(_UpperCAmelCase    ) -> CoinsDistribResult:

        if node is None:
            return CoinsDistribResult(0       ,			1    )

        __a	,       __a							=    get_distrib(node.left    )
        __a	,       __a							=    get_distrib(node.right    )

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





