



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   @property
   def      UpperCamelCase_  (					self							:			Any)				->				str:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  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      UpperCamelCase_  (					self							:			Tuple)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  str					=					self.dummy_uncond_unet
    _snake_case    :  Union[str, Any]					=					PNDMScheduler()

    _snake_case    :  Any					=					PNDMPipeline(unet=lowerCAmelCase				,		scheduler=lowerCAmelCase)
    pndm.to(lowerCAmelCase)
    pndm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  List[str]					=					torch.manual_seed(0)
    _snake_case    :  Optional[Any]					=					pndm(generator=lowerCAmelCase				,		num_inference_steps=20				,		output_type="""numpy""").images

    _snake_case    :  List[str]					=					torch.manual_seed(0)
    _snake_case    :  Dict					=					pndm(generator=lowerCAmelCase				,		num_inference_steps=20				,		output_type="""numpy"""				,		return_dict=lowerCAmelCase)[0]

    _snake_case    :  List[str]					=					image[0, -3:, -3:, -1]
    _snake_case    :  Any					=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case    :  List[str]					=					np.array([1.0, 1.0, 0.0, 1.0, 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 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Dict)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					"""google/ddpm-cifar10-32"""

    _snake_case    :  List[Any]					=					UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case    :  List[Any]					=					PNDMScheduler()

    _snake_case    :  Optional[Any]					=					PNDMPipeline(unet=lowerCAmelCase				,		scheduler=lowerCAmelCase)
    pndm.to(lowerCAmelCase)
    pndm.set_progress_bar_config(disable=lowerCAmelCase)
    _snake_case    :  List[Any]					=					torch.manual_seed(0)
    _snake_case    :  int					=					pndm(generator=lowerCAmelCase				,		output_type="""numpy""").images

    _snake_case    :  int					=					image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case    :  str					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125])

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










from math import ceil, sqrt



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000_000				)  ->     int:
 _snake_case    :  Tuple					=					0

 for outer_width in range(3				,       (limit // 4) + 2				):
  if outer_width**2 > limit:
   _snake_case    :  int					=					max(ceil(sqrt(outer_width**2 - limit				)				)				,       1				)
  else:
   _snake_case    :  Any					=					1
  if (outer_width - hole_width_lower_bound) % 2:
   hole_width_lower_bound += 1

  answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

 return answer


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