
'''simple docstring'''
import gc
import tempfile
import unittest

import numpy as np
import torch

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


a_											=						False






class    UpperCAmelCase_				(	unittest.TestCase					):
 pass






@nightly
@require_torch_gpu
class    UpperCAmelCase_				(	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):
     snake_case_ :    str     			=						VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" ,       torch_dtype=torch.floataa)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    str     			=						load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")

     snake_case_ :    Dict     			=						torch.manual_seed(0)
     snake_case_ :    int     			=						pipe.dual_guided(
         prompt="first prompt" ,       image=lowercase_ ,       text_to_image_strength=0.75 ,       generator=lowercase_ ,       guidance_scale=7.5 ,       num_inference_steps=2 ,       output_type="numpy" ,       ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(lowercase_)
         snake_case_ :    Dict     			=						VersatileDiffusionPipeline.from_pretrained(lowercase_ ,       torch_dtype=torch.floataa)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						generator.manual_seed(0)
     snake_case_ :    Any     			=						pipe.dual_guided(
         prompt="first prompt" ,       image=lowercase_ ,       text_to_image_strength=0.75 ,       generator=lowercase_ ,       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):
     snake_case_ :    Dict     			=						VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" ,       torch_dtype=torch.floataa)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						"cyberpunk 2077"
     snake_case_ :    Optional[Any]     			=						load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")
     snake_case_ :    Any     			=						torch.manual_seed(0)
     snake_case_ :    Tuple     			=						pipe.dual_guided(
         prompt=lowercase_ ,       image=lowercase_ ,       text_to_image_strength=0.75 ,       generator=lowercase_ ,       guidance_scale=7.5 ,       num_inference_steps=50 ,       output_type="numpy" ,       ).images

     snake_case_ :    List[Any]     			=						image[0, 2_53:2_56, 2_53:2_56, -1]

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    int     			=						np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001])

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

     snake_case_ :    Tuple     			=						"A painting of a squirrel eating a burger "
     snake_case_ :    Dict     			=						torch.manual_seed(0)
     snake_case_ :    str     			=						pipe.text_to_image(
         prompt=lowercase_ ,       generator=lowercase_ ,       guidance_scale=7.5 ,       num_inference_steps=50 ,       output_type="numpy").images

     snake_case_ :    Optional[Any]     			=						image[0, 2_53:2_56, 2_53:2_56, -1]

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    Dict     			=						np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])

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

     snake_case_ :    Tuple     			=						pipe.image_variation(lowercase_ ,       generator=lowercase_ ,       output_type="numpy").images

     snake_case_ :    List[Any]     			=						image[0, 2_53:2_56, 2_53:2_56, -1]

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    Optional[Any]     			=						np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456])

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






'''simple docstring'''
from __future__ import annotations

import math

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if depth < 0:
        raise ValueError("Depth cannot be less than 0"       )

    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        raise ValueError("Scores cannot be empty"       )

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

    return min(
        minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    int     			=						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    snake_case_ :    Union[str, Any]     			=						math.log(len(__SCREAMING_SNAKE_CASE       ),		2       )
    print("Optimal value : ",		end=""       )
    print(minimax(0,		0,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




