
"""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


SCREAMING_SNAKE_CASE_            =    False
class   _UpperCAmelCase				(		unittest.TestCase				):
      pass

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




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




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'				)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(lowercase_				)
          pipe.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       'A painting of a squirrel eating a burger '
          UpperCAmelCase          =       torch.manual_seed(0				)
          UpperCAmelCase          =       pipe(
              prompt=lowercase_ ,							generator=lowercase_ ,							guidance_scale=7.5 ,							num_inference_steps=2 ,							output_type='numpy'				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase_				)
              UpperCAmelCase          =       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase_				)
          pipe.to(lowercase_				)
          pipe.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       generator.manual_seed(0				)
          UpperCAmelCase          =       pipe(
              prompt=lowercase_ ,							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      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       VersatileDiffusionTextToImagePipeline.from_pretrained(
              'shi-labs/versatile-diffusion' ,							torch_dtype=torch.floataa				)
          pipe.to(lowercase_				)
          pipe.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       'A painting of a squirrel eating a burger '
          UpperCAmelCase          =       torch.manual_seed(0				)
          UpperCAmelCase          =       pipe(
              prompt=lowercase_ ,							generator=lowercase_ ,							guidance_scale=7.5 ,							num_inference_steps=5_0 ,							output_type='numpy'				).images

          UpperCAmelCase          =       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          =       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]				)

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





"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

SCREAMING_SNAKE_CASE_            =    list[list[float | int]]




def      lowercase__    (       lowerCAmelCase					:       Matrix			,					lowerCAmelCase					:       Matrix			)			->   Matrix:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(size + 1			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for row in range(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       matrix[row][col]

        UpperCAmelCase          =       vector[row][0]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    while row < size and col < size:
        # pivoting
        UpperCAmelCase          =       max((abs(augmented[rowa][col]			), rowa) for rowa in range(lowerCAmelCase			,					lowerCAmelCase			)			)[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            UpperCAmelCase      ,				UpperCAmelCase          =       augmented[pivot_row], augmented[row]

        for rowa in range(row + 1			,					lowerCAmelCase			):
            UpperCAmelCase          =       augmented[rowa][col] / augmented[row][col]
            UpperCAmelCase          =       0
            for cola in range(col + 1			,					size + 1			):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1			,					lowerCAmelCase			):
        for row in range(lowerCAmelCase			):
            UpperCAmelCase          =       augmented[row][col] / augmented[col][col]
            for cola in range(lowerCAmelCase			,					size + 1			):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row]			,					10			)] for row in range(lowerCAmelCase			)
    ]




def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   Callable[[int], int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(lowerCAmelCase			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       [[0] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for x_val, y_val in enumerate(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       (x_val + 1) ** (size - col - 1)
        UpperCAmelCase          =       y_val

    UpperCAmelCase          =       solve(lowerCAmelCase			,					lowerCAmelCase			)

    def interpolated_func(lowerCAmelCase					:       int			) -> int:
        return sum(
            round(coeffs[x_val][0]			) * (var ** (size - x_val - 1))
            for x_val in range(lowerCAmelCase			)			)

    return interpolated_func




def      lowercase__    (       lowerCAmelCase					:       int			)			->   int:






    """simple docstring"""




    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )




def      lowercase__    (       lowerCAmelCase					:       Callable[[int], int] = question_function			,					lowerCAmelCase					:       int = 10			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       [func(lowerCAmelCase			) for x_val in range(1			,					order + 1			)]

    UpperCAmelCase          =       [
        interpolate(data_points[:max_coeff]			) for max_coeff in range(1			,					order + 1			)
    ]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for poly in polynomials:
        UpperCAmelCase          =       1
        while func(lowerCAmelCase			) == poly(lowerCAmelCase			):
            x_val += 1

        ret += poly(lowerCAmelCase			)

    return ret


if __name__ == "__main__":
   print(F'{solution() = }')



