from __future__ import annotations

from queue import Queue


def SCREAMING_SNAKE_CASE			(       lowercase_:		int						,  lowercase_:		int			):
       a ^= b
       b ^= a
       a ^= b
       return a, b


def SCREAMING_SNAKE_CASE			(       lowercase_:		int						,  lowercase_:		list[list[int]]			):
       _UpperCamelCase       :    List[Any]					  =					1
       while (1 << j) < max_node:
              for i in range(1						,  max_node + 1			):
                     _UpperCamelCase       :    Any					  =					parent[j - 1][parent[j - 1][i]]
              j += 1
       return parent


def SCREAMING_SNAKE_CASE			(       lowercase_:		int						,  lowercase_:		int						,  lowercase_:		list[int]						,  lowercase_:		list[list[int]]			):
       # u must be deeper in the tree than v
       if level[u] < level[v]:
              _UpperCamelCase  , _UpperCamelCase       :    Optional[Any]					  =					swap(lowercase_						,  lowercase_			)
       # making depth of u same as depth of v
       for i in range(18						,  -1						,  -1			):
              if level[u] - (1 << i) >= level[v]:
                     _UpperCamelCase       :    Optional[int]					  =					parent[i][u]
    # at the same depth if u==v that mean lca is found
       if u == v:
              return u
       # moving both nodes upwards till lca in found
       for i in range(18						,  -1						,  -1			):
              if parent[i][u] not in [0, parent[i][v]]:
                     _UpperCamelCase  , _UpperCamelCase       :    int					  =					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
       return parent[0][u]


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]						,  lowercase_:		list[list[int]]						,  lowercase_:		int						,  lowercase_:		dict[int, list[int]]						,  lowercase_:		int = 1						,  ):
       _UpperCamelCase       :    List[Any]					  =					0
       _UpperCamelCase       :    Queue[int]					  =					Queue(maxsize=lowercase_			)
       q.put(lowercase_			)
       while q.qsize() != 0:
              _UpperCamelCase       :    Union[str, Any]					  =					q.get()
              for v in graph[u]:
                     if level[v] == -1:
                            _UpperCamelCase       :    Optional[int]					  =					level[u] + 1
                            q.put(lowercase_			)
                            _UpperCamelCase       :    List[str]					  =					u
       return level, parent


def SCREAMING_SNAKE_CASE			(       ):
       _UpperCamelCase       :    Dict					  =					13
       # initializing with 0
       _UpperCamelCase       :    List[str]					  =					[[0 for _ in range(max_node + 10			)] for _ in range(20			)]
       # initializing with -1 which means every node is unvisited
       _UpperCamelCase       :    int					  =					[-1 for _ in range(max_node + 10			)]
       _UpperCamelCase       :    dict[int, list[int]]					  =					{
           1: [2, 3, 4],
           2: [5],
           3: [6, 7],
           4: [8],
           5: [9, 10],
           6: [11],
           7: [],
           8: [12, 13],
           9: [],
           10: [],
           11: [],
           12: [],
           13: [],
       }
       _UpperCamelCase  , _UpperCamelCase       :    int					  =					breadth_first_search(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  1			)
       _UpperCamelCase       :    Dict					  =					create_sparse(lowercase_						,  lowercase_			)
       print('LCA of node 1 and 3 is: '						,  lowest_common_ancestor(1						,  3						,  lowercase_						,  lowercase_			)			)
       print('LCA of node 5 and 6 is: '						,  lowest_common_ancestor(5						,  6						,  lowercase_						,  lowercase_			)			)
       print('LCA of node 7 and 11 is: '						,  lowest_common_ancestor(7						,  11						,  lowercase_						,  lowercase_			)			)
       print('LCA of node 6 and 7 is: '						,  lowest_common_ancestor(6						,  7						,  lowercase_						,  lowercase_			)			)
       print('LCA of node 4 and 12 is: '						,  lowest_common_ancestor(4						,  12						,  lowercase_						,  lowercase_			)			)
       print('LCA of node 8 and 8 is: '						,  lowest_common_ancestor(8						,  8						,  lowercase_						,  lowercase_			)			)


if __name__ == "__main__":
 main()




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase     						=			False




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



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     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    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



