





'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case_     =	False


class   _lowercase						(							unittest.TestCase				):
   pass







@nightly
@require_torch_gpu
class   _lowercase						(							unittest.TestCase				):


   def 					snake_case				( self					):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def 					snake_case				( self					):
       A       :       Tuple							=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       List[Any]							=						'''A painting of a squirrel eating a burger '''
       A       :       int							=						torch.manual_seed(0					)
       A       :       List[str]							=						pipe(
           prompt=_UpperCAmelCase    ,   generator=_UpperCAmelCase    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='''numpy'''					).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(_UpperCAmelCase					)
           A       :       Dict							=						VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase					)
       pipe.to(_UpperCAmelCase					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       Union[str, Any]							=						generator.manual_seed(0					)
       A       :       List[Any]							=						pipe(
           prompt=_UpperCAmelCase    ,   generator=_UpperCAmelCase    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='''numpy'''					).images

       assert np.abs(image - new_image					).sum() < 1E-5, "Models don't have the same forward pass"


   def 					snake_case				( self					):
       A       :       int							=						VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,   torch_dtype=torch.floataa					)
       pipe.to(_UpperCAmelCase					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       Optional[int]							=						'''A painting of a squirrel eating a burger '''
       A       :       List[str]							=						torch.manual_seed(0					)
       A       :       List[Any]							=						pipe(
           prompt=_UpperCAmelCase    ,   generator=_UpperCAmelCase    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type='''numpy'''					).images

       A       :       List[Any]							=						image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       A       :       str							=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 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()
