
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


lowercase__		:Dict   =    False







class 				lowercase		(					unittest.TestCase		):
							pass







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




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




							def  A__      (				self):
												lowercase							=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,torch_dtype=torch.floataa)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

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

												lowercase							=						torch.manual_seed(0)
												lowercase							=						pipe.dual_guided(
												    prompt='''first prompt'''   ,image=A__   ,text_to_image_strength=0.75   ,generator=A__   ,guidance_scale=7.5   ,num_inference_steps=2   ,output_type='''numpy'''   ,).images

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

												lowercase							=						generator.manual_seed(0)
												lowercase							=						pipe.dual_guided(
												    prompt='''first prompt'''   ,image=A__   ,text_to_image_strength=0.75   ,generator=A__   ,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):
												lowercase							=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,torch_dtype=torch.floataa)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						'''cyberpunk 2077'''
												lowercase							=						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''')
												lowercase							=						torch.manual_seed(0)
												lowercase							=						pipe.dual_guided(
												    prompt=A__   ,image=A__   ,text_to_image_strength=0.75   ,generator=A__   ,guidance_scale=7.5   ,num_inference_steps=5_0   ,output_type='''numpy'''   ,).images

												lowercase							=						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)
												lowercase							=						np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])

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

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

												lowercase							=						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)
												lowercase							=						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-1

												lowercase							=						pipe.image_variation(A__   ,generator=A__   ,output_type='''numpy''').images

												lowercase							=						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)
												lowercase							=						np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])

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





"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase    :       str					     =list[list[float | int]]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Matrix					,		lowerCAmelCase__     :Matrix		) ->    Matrix:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(size + 1		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for row in range(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	matrix[row][col]

            lowercase														=	vector[row][0]

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

            for rowa in range(row + 1					,		lowerCAmelCase__		):
                  lowercase														=	augmented[rowa][col] / augmented[row][col]
                  lowercase														=	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__		):
                  lowercase														=	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]					,		1_0		)] for row in range(lowerCAmelCase__		)
      ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    Callable[[int], int]:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	[[0] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

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

      lowercase														=	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 			UpperCAmelCase__       (				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**1_0
      )


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable[[int], int] = question_function					,		lowerCAmelCase__     :int = 1_0		) ->    int:
      '''simple docstring'''


      lowercase														=	[func(lowerCAmelCase__		) for x_val in range(1					,		order + 1		)]

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

      lowercase														=	0
      lowercase														=	42
      lowercase														=	42

      for poly in polynomials:
            lowercase														=	1
            while func(lowerCAmelCase__		) == poly(lowerCAmelCase__		):
                  x_val += 1

            ret += poly(lowerCAmelCase__		)

      return ret


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



