


'''simple docstring'''




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





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





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_dpmpp_2m'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe(
            [prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

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









def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					current_set.copy()
  for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					row[0]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      if magnitude == 0:
        A_      :				Union[str, Any]		     =					column
        continue
      A_      :				Dict		     =					column / magnitude
    # Subtract to cancel term
  A_      :				Union[str, Any]		     =					current_set[0]
  A_      :				Tuple		     =					[first_row]
  A_      :				int		     =					current_set[1::]
  for row in current_set:
    A_      :				Tuple		     =					[]
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(SCREAMING_SNAKE_CASE       )
      continue
    for column_index in range(len(SCREAMING_SNAKE_CASE       )       ):
      temp_row.append(first_row[column_index] - row[column_index]       )
    final_set.append(SCREAMING_SNAKE_CASE       )
  # Create next recursion iteration set
  if len(final_set[0]       ) != 3:
    A_      :				Optional[Any]		     =					final_set[0]
    A_      :				Any		     =					[]
    A_      :				str		     =					[]
    for row in final_set[1::]:
      current_first_column.append(row[0]       )
      next_iteration.append(row[1::]       )
    A_      :				Optional[Any]		     =					simplify(SCREAMING_SNAKE_CASE       )
    for i in range(len(SCREAMING_SNAKE_CASE       )       ):
      resultant[i].insert(0 ,			current_first_column[i]       )
    resultant.insert(0 ,			SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					resultant
  return final_set


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if len(SCREAMING_SNAKE_CASE       ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  A_      :				str		     =					len(SCREAMING_SNAKE_CASE       ) + 1
  if any(len(SCREAMING_SNAKE_CASE       ) != _length for item in equations       ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  for row in equations:
    if any(not isinstance(SCREAMING_SNAKE_CASE ,			(int, float)       ) for column in row       ):
      raise ValueError('''solve_simultaneous() requires lists of integers'''       )
  if len(SCREAMING_SNAKE_CASE       ) == 1:
    return [equations[0][-1] / equations[0][0]]
  A_      :				Dict		     =					equations.copy()
  if any(0 in row for row in data_set       ):
    A_      :				Tuple		     =					data_set.copy()
    A_      :				Optional[Any]		     =					[]
    for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
      if 0 not in row:
        A_      :				str		     =					data_set.pop(SCREAMING_SNAKE_CASE       )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation'''       )
    data_set.insert(0 ,			SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					data_set.copy()
  A_      :				Dict		     =					simplify(SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					simplified[::-1]
  A_      :				list		     =					[]
  for row in simplified:
    A_      :				Union[str, Any]		     =					row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0       )
        continue
      solutions.append(current_solution / row[-2]       )
      continue
    A_      :				Optional[Any]		     =					row.copy()[: len(SCREAMING_SNAKE_CASE       ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0       )
    if len(SCREAMING_SNAKE_CASE       ) == 0:
      solutions.append(0       )
      continue
    A_      :				int		     =					temp_row[1::]
    A_      :				int		     =					temp_row[::-1]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      current_solution -= column * solutions[column_index]
    solutions.append(SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					[]
  for item in solutions:
    final.append(float(round(SCREAMING_SNAKE_CASE ,			5       )       )       )
  return final[::-1]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     UpperCamelCase								=	[
         [2, 1, 1, 1, 1, 4],
         [1, 2, 1, 1, 1, 5],
         [1, 1, 2, 1, 1, 6],
         [1, 1, 1, 2, 1, 7],
         [1, 1, 1, 1, 2, 8],
     ]
     print(solve_simultaneous(eq))
     print(solve_simultaneous([[4, 2]]))




