

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			):

   @property
   def     UpperCamelCase					(				self	:    Dict       ) ->			str:
         """simple docstring"""





         torch.manual_seed(0       )
         A_							       =		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





   def     UpperCamelCase					(				self	:    str       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.dummy_uncond_unet
         A_							       =		ScoreSdeVeScheduler()

         A_							       =		ScoreSdeVePipeline(unet=lowerCamelCase__			,						scheduler=lowerCamelCase__       )
         sde_ve.to(lowerCamelCase__       )
         sde_ve.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		torch.manual_seed(0       )
         A_							       =		sde_ve(num_inference_steps=2			,						output_type='''numpy'''			,						generator=lowerCamelCase__       ).images

         A_							       =		torch.manual_seed(0       )
         A_							       =		sde_ve(num_inference_steps=2			,						output_type='''numpy'''			,						generator=lowerCamelCase__			,						return_dict=lowerCamelCase__       )[
             0
         ]

         A_							       =		image[0, -3:, -3:, -1]
         A_							       =		image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         A_							       =		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			):

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		'''google/ncsnpp-church-256'''
         A_							       =		UNetaDModel.from_pretrained(lowerCamelCase__       )

         A_							       =		ScoreSdeVeScheduler.from_pretrained(lowerCamelCase__       )

         A_							       =		ScoreSdeVePipeline(unet=lowerCamelCase__			,						scheduler=lowerCamelCase__       )
         sde_ve.to(lowerCamelCase__       )
         sde_ve.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		torch.manual_seed(0       )
         A_							       =		sde_ve(num_inference_steps=1_0			,						output_type='''numpy'''			,						generator=lowerCamelCase__       ).images

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

         assert image.shape == (1, 2_5_6, 2_5_6, 3)

         A_							       =		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








__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

      A_							       =		Stack()
      A_							       =		Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(SCREAMING_SNAKE_CASE   )   )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(SCREAMING_SNAKE_CASE   )
            elif i == ")":
                  # RULE 4
                  A_							       =		operator_stack.peek()
                  operator_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()

                  A_							       =		operators[opr](SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                  operand_stack.push(SCREAMING_SNAKE_CASE   )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
      __lowercase				      =						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





