"""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      _A	(						unittest.TestCase					):




    """simple docstring"""




    @property
    def  lowercase					(		self							:     Any    )    ->       Tuple:
         torch.manual_seed(0    )
         __snake_case				=							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  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         __snake_case				=							self.dummy_uncond_unet
         __snake_case				=							ScoreSdeVeScheduler()

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

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

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

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

         assert image.shape == (1, 32, 32, 3)
         __snake_case				=							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      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Union[str, Any]    )    ->       Dict:
         __snake_case				=							'''google/ncsnpp-church-256'''
         __snake_case				=							UNetaDModel.from_pretrained(A_    )

         __snake_case				=							ScoreSdeVeScheduler.from_pretrained(A_    )

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

         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sde_ve(num_inference_steps=10	,      output_type='''numpy'''	,      generator=A_    ).images

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

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

         __snake_case				=							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 re





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''')
     if match := re.search(snake_case,      snake_case):
          return match.string == phone
     return False


if __name__ == "__main__":
       print(indian_phone_validator("+918827897895"))