





'''simple docstring'''






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   _lowercase						(							a		,					a		,					unittest.TestCase				):
   _UpperCamelCase       		=   IFInpaintingPipeline
   _UpperCamelCase       		=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
   _UpperCamelCase       		=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCamelCase       		=   PipelineTesterMixin.required_optional_params - {"""latents"""}


   def 					snake_case				( self					):
       return self._get_dummy_components()


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0					):
       if str(_UpperCAmelCase					).startswith('''mps'''					):
           A       :       Any							=						torch.manual_seed(_UpperCAmelCase					)
       else:
           A       :       Optional[Any]							=						torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)

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

       A       :       str							=						{
           '''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 					snake_case				( self					):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)


   def 					snake_case				( self					):
       self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''					)
   def 					snake_case				( self					):
       # 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 					snake_case				( self					):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)


   def 					snake_case				( self					):
       self._test_save_load_local()


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







'''simple docstring'''






from __future__ import annotations


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase					):
       A       :       str							=						data
       A       :       Node | None							=						None
       A       :       Node | None							=						None







def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  None:  # In Order traversal of the tree
    if tree:
        display(tree.left		)
        print(tree.data		)
        display(tree.right		)
def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  int:
    return 1 + max(depth_of_tree(tree.left		)    ,       depth_of_tree(tree.right		)		) if tree else 0
def 							_lowerCamelCase(  UpperCamelCase__    : Node		)		->  bool:
    if not tree:
        return True
    if tree.left and tree.right:
        return is_full_binary_tree(tree.left		) and is_full_binary_tree(tree.right		)
    else:
        return not tree.left and not tree.right
def 							_lowerCamelCase(  )		->  None:  # Main function for testing.
    A       :       Optional[int]							=						Node(1		)
    A       :       Tuple							=						Node(2		)
    A       :       Dict							=						Node(3		)
    A       :       List[str]							=						Node(4		)
    A       :       Union[str, Any]							=						Node(5		)
    A       :       str							=						Node(6		)
    A       :       Any							=						Node(7		)
    A       :       str							=						Node(8		)
    A       :       Optional[int]							=						Node(9		)

    print(is_full_binary_tree(UpperCamelCase__		)		)
    print(depth_of_tree(UpperCamelCase__		)		)
    print('''Tree is: '''		)
    display(UpperCamelCase__		)


if __name__ == "__main__":
     main()
