






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		@property
		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							torch.manual_seed(0       )
							UpperCAmelCase_   :				Union[str, Any]						=						UNetaDModel(
							    block_out_channels=(3_2, 6_4)    ,		layers_per_block=2    ,		sample_size=3_2    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=('DownBlock2D', 'AttnDownBlock2D')    ,		up_block_types=('AttnUpBlock2D', 'UpBlock2D')    ,		)
							return model



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							torch.manual_seed(0       )
							UpperCAmelCase_   :				str						=						VQModel(
							    block_out_channels=[3_2, 6_4]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,		latent_channels=3    ,		)
							return model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							torch.manual_seed(0       )
							UpperCAmelCase_   :				Tuple						=						CLIPTextConfig(
							    bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=3_2    ,		intermediate_size=3_7    ,		layer_norm_eps=1E-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=1_0_0_0    ,		)
							return CLIPTextModel(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Any						=						self.dummy_uncond_unet
							UpperCAmelCase_   :				str						=						DDIMScheduler()
							UpperCAmelCase_   :				int						=						self.dummy_vq_model

							UpperCAmelCase_   :				Tuple						=						LDMPipeline(unet=_UpperCamelCase    ,		vqvae=_UpperCamelCase    ,		scheduler=_UpperCamelCase       )
							ldm.to(_UpperCamelCase       )
							ldm.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						torch.manual_seed(0       )
							UpperCAmelCase_   :				List[Any]						=						ldm(generator=_UpperCamelCase    ,		num_inference_steps=2    ,		output_type='numpy'       ).images

							UpperCAmelCase_   :				List[str]						=						torch.manual_seed(0       )
							UpperCAmelCase_   :				Optional[Any]						=						ldm(generator=_UpperCamelCase    ,		num_inference_steps=2    ,		output_type='numpy'    ,		return_dict=_UpperCamelCase       )[0]

							UpperCAmelCase_   :				Tuple						=						image[0, -3:, -3:, -1]
							UpperCAmelCase_   :				str						=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							UpperCAmelCase_   :				Optional[int]						=						np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
							UpperCAmelCase_   :				Dict						=						1E-2 if torch_device != 'mps' else 3E-2

							assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance





@slow
@require_torch
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				str						=						LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
							ldm.to(_UpperCamelCase       )
							ldm.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						torch.manual_seed(0       )
							UpperCAmelCase_   :				Optional[Any]						=						ldm(generator=_UpperCamelCase    ,		num_inference_steps=5    ,		output_type='numpy'       ).images

							UpperCAmelCase_   :				Any						=						image[0, -3:, -3:, -1]

							assert image.shape == (1, 2_5_6, 2_5_6, 3)
							UpperCAmelCase_   :				Optional[int]						=						np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
							UpperCAmelCase_   :				str						=						1E-2 if torch_device != 'mps' else 3E-2

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














from __future__ import annotations

from collections.abc import Callable

__UpperCAmelCase     							=						list[list[float | int]]




def 					lowercase__				(					__snake_case   :							Matrix					,	__snake_case   :							Matrix  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						len(__snake_case  )
					UpperCAmelCase_   :				Matrix						=						[[0 for _ in range(size + 1  )] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				float

					for row in range(__snake_case  ):
										for col in range(__snake_case  ):
															UpperCAmelCase_   :				Dict						=						matrix[row][col]

										UpperCAmelCase_   :				Union[str, Any]						=						vector[row][0]

					UpperCAmelCase_   :				Union[str, Any]						=						0
					UpperCAmelCase_   :				Union[str, Any]						=						0
					while row < size and col < size:
										# pivoting
										UpperCAmelCase_   :				Optional[Any]						=						max((abs(augmented[rowa][col]  ), rowa) for rowa in range(__snake_case					,	__snake_case  )  )[
										    1
										]
										if augmented[pivot_row][col] == 0:
															col += 1
															continue
										else:
															UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						augmented[pivot_row], augmented[row]

										for rowa in range(row + 1					,	__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						augmented[rowa][col] / augmented[row][col]
															UpperCAmelCase_   :				List[str]						=						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					,	__snake_case  ):
										for row in range(__snake_case  ):
															UpperCAmelCase_   :				Union[str, Any]						=						augmented[row][col] / augmented[col][col]
															for cola in range(__snake_case					,	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(__snake_case  )
					]




def 					lowercase__				(					__snake_case   :							list[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						len(__snake_case  )
					UpperCAmelCase_   :				Matrix						=						[[0 for _ in range(__snake_case  )] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				Matrix						=						[[0] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				Matrix
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int

					for x_val, y_val in enumerate(__snake_case  ):
										for col in range(__snake_case  ):
															UpperCAmelCase_   :				int						=						(x_val + 1) ** (size - col - 1)
										UpperCAmelCase_   :				int						=						y_val

					UpperCAmelCase_   :				List[str]						=						solve(__snake_case					,	__snake_case  )

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

					return interpolated_func




def 					lowercase__				(					__snake_case   :							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__				(					__snake_case   :							Callable[[int], int] = question_function					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				list[int]						=						[func(__snake_case  ) for x_val in range(1					,	order + 1  )]

					UpperCAmelCase_   :				list[Callable[[int], int]]						=						[
					    interpolate(data_points[:max_coeff]  ) for max_coeff in range(1					,	order + 1  )
					]

					UpperCAmelCase_   :				int						=						0
					UpperCAmelCase_   :				Callable[[int], int]
					UpperCAmelCase_   :				int

					for poly in polynomials:
										UpperCAmelCase_   :				Optional[int]						=						1
										while func(__snake_case  ) == poly(__snake_case  ):
															x_val += 1

										ret += poly(__snake_case  )

					return ret


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






