'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				Dict   				=  UNetaDModel(
             block_out_channels=(32, 64)					,layers_per_block=2					,sample_size=32					,in_channels=3					,out_channels=3					,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,)
         return model




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.dummy_uncond_unet
         UpperCAmelCase__							:				Optional[Any]   				=  ScoreSdeVeScheduler()

         UpperCAmelCase__							:				str   				=  ScoreSdeVePipeline(unet=lowerCamelCase_					,scheduler=lowerCamelCase_							)
         sde_ve.to(lowerCamelCase_							)
         sde_ve.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  torch.manual_seed(0							)
         UpperCAmelCase__							:				Optional[int]   				=  sde_ve(num_inference_steps=2					,output_type='''numpy'''					,generator=lowerCamelCase_							).images

         UpperCAmelCase__							:				Optional[Any]   				=  torch.manual_seed(0							)
         UpperCAmelCase__							:				Optional[Any]   				=  sde_ve(num_inference_steps=2					,output_type='''numpy'''					,generator=lowerCamelCase_					,return_dict=lowerCamelCase_							)[
             0
         ]

         UpperCAmelCase__							:				Optional[Any]   				=  image[0, -3:, -3:, -1]
         UpperCAmelCase__							:				List[Any]   				=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase__							:				List[Any]   				=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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




@slow
@require_torch
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  '''google/ncsnpp-church-256'''
         UpperCAmelCase__							:				List[str]   				=  UNetaDModel.from_pretrained(lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  ScoreSdeVeScheduler.from_pretrained(lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[Any]   				=  ScoreSdeVePipeline(unet=lowerCamelCase_					,scheduler=lowerCamelCase_							)
         sde_ve.to(lowerCamelCase_							)
         sde_ve.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Dict   				=  torch.manual_seed(0							)
         UpperCAmelCase__							:				Tuple   				=  sde_ve(num_inference_steps=10					,output_type='''numpy'''					,generator=lowerCamelCase_							).images

         UpperCAmelCase__							:				Union[str, Any]   				=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)

         UpperCAmelCase__							:				List[Any]   				=  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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





'''simple docstring'''

import math







def __UpperCamelCase(   _A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   return math.pow(_A  ,			2    ) - a







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   return 2 * x







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  2.0

   while start <= a:
      UpperCAmelCase__							:				int   				=  math.pow(_A  ,			2    )

   return start







def __UpperCamelCase(   _A					:				float  ,			_A					:				int = 99_99  ,			_A					:				float = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1    ):
   '''simple docstring'''

   if a < 0:
      raise ValueError('''math domain error'''    )

   UpperCAmelCase__							:				Any   				=  get_initial_point(_A    )

   for _ in range(_A    ):
      UpperCAmelCase__							:				Optional[Any]   				=  value
      UpperCAmelCase__							:				Tuple   				=  value - fx(_A  ,			_A    ) / fx_derivative(_A    )
      if abs(prev_value - value    ) < tolerance:
         return value

   return value


if __name__ == "__main__":
  from doctest import testmod

  testmod()




