




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 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    @property
    def   a (			self	:	Any  ) ->			List[Any]:
         torch.manual_seed(0  )
         lowerCAmelCase__            =					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   a (			self	:	Dict  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.dummy_uncond_unet
         lowerCAmelCase__            =					ScoreSdeVeScheduler()

         lowerCAmelCase__            =					ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE__  ,      scheduler=SCREAMING_SNAKE_CASE__  )
         sde_ve.to(SCREAMING_SNAKE_CASE__  )
         sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.manual_seed(0  )
         lowerCAmelCase__            =					sde_ve(num_inference_steps=2  ,      output_type="numpy"  ,      generator=SCREAMING_SNAKE_CASE__  ).images

         lowerCAmelCase__            =					torch.manual_seed(0  )
         lowerCAmelCase__            =					sde_ve(num_inference_steps=2  ,      output_type="numpy"  ,      generator=SCREAMING_SNAKE_CASE__  ,      return_dict=SCREAMING_SNAKE_CASE__  )[
             0
         ]

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

         assert image.shape == (1, 32, 32, 3)
         lowerCAmelCase__            =					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 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def   a (			self	:	int  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					"google/ncsnpp-church-256"
         lowerCAmelCase__            =					UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE__  ,      scheduler=SCREAMING_SNAKE_CASE__  )
         sde_ve.to(SCREAMING_SNAKE_CASE__  )
         sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.manual_seed(0  )
         lowerCAmelCase__            =					sde_ve(num_inference_steps=10  ,      output_type="numpy"  ,      generator=SCREAMING_SNAKE_CASE__  ).images

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

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

         lowerCAmelCase__            =					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

def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 _UpperCamelCase:  Dict    						=			set({'''(''', '''[''', '''{'''}		)
 _UpperCamelCase:  int    						=			set({''')''', ''']''', '''}'''}		)
 _UpperCamelCase:  Dict    						=			{'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

 for i in range(len(lowercase		)		):
  if s[i] in open_brackets:
   stack.append(s[i]		)

  elif s[i] in closed_brackets and (
      len(lowercase		) == 0 or (len(lowercase		) > 0 and open_to_closed[stack.pop()] != s[i])
  ):
   return False

 return len(lowercase		) == 0




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			input('''Enter sequence of brackets: '''		)
 if is_balanced(lowercase		):
  print(lowercase	,       '''is balanced'''		)
 else:
  print(lowercase	,       '''is not balanced'''		)


if __name__ == "__main__":
  main()