
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      _A  :						List[Any]      		=       (ScoreSdeVeScheduler,)
      _A  :						Union[str, Any]      		=       ()
      @property
      def A_     (     self	):
          snake_case__  =			4
          snake_case__  =			3
          snake_case__  =			8
          snake_case__  =			8

          snake_case__  =			torch.rand((batch_size, num_channels, height, width)	)

          return sample
      @property
      def A_     (     self	):
          snake_case__  =			4
          snake_case__  =			3
          snake_case__  =			8
          snake_case__  =			8

          snake_case__  =			batch_size * num_channels * height * width
          snake_case__  =			torch.arange(snake_case__	)
          snake_case__  =			sample.reshape(snake_case__						,		snake_case__						,		snake_case__						,		snake_case__	)
          snake_case__  =			sample / num_elems
          snake_case__  =			sample.permute(3						,		0						,		1						,		2	)

          return sample
      def A_     (     self	):


          def model(lowerCamelCase						,		lowerCamelCase						,		*lowerCamelCase	):
              return sample * t / (t + 1)

          return model
      def A_     (     self						,		**lowerCamelCase	):
          snake_case__  =			{
              "num_train_timesteps": 20_00,
              "snr": 0.1_5,
              "sigma_min": 0.0_1,
              "sigma_max": 13_48,
              "sampling_eps": 1e-5,
          }

          config.update(**snake_case__	)
          return config
      def A_     (     self						,		lowerCamelCase=0						,		**lowerCamelCase	):
          snake_case__  =			dict(self.forward_default_kwargs	)

          for scheduler_class in self.scheduler_classes:
              snake_case__  =			self.dummy_sample
              snake_case__  =			0.1 * sample

              snake_case__  =			self.get_scheduler_config(**snake_case__	)
              snake_case__  =			scheduler_class(**snake_case__	)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(snake_case__	)
                  snake_case__  =			scheduler_class.from_pretrained(snake_case__	)

              snake_case__  =			scheduler.step_pred(
                  snake_case__						,		snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample
              snake_case__  =			new_scheduler.step_pred(
                  snake_case__						,		snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample

              assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

              snake_case__  =			scheduler.step_correct(snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample
              snake_case__  =			new_scheduler.step_correct(
                  snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample

              assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler correction are not identical"
      def A_     (     self						,		lowerCamelCase=0						,		**lowerCamelCase	):
          snake_case__  =			dict(self.forward_default_kwargs	)
          kwargs.update(snake_case__	)

          for scheduler_class in self.scheduler_classes:
              snake_case__  =			self.dummy_sample
              snake_case__  =			0.1 * sample

              snake_case__  =			self.get_scheduler_config()
              snake_case__  =			scheduler_class(**snake_case__	)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(snake_case__	)
                  snake_case__  =			scheduler_class.from_pretrained(snake_case__	)

              snake_case__  =			scheduler.step_pred(
                  snake_case__						,		snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample
              snake_case__  =			new_scheduler.step_pred(
                  snake_case__						,		snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample

              assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

              snake_case__  =			scheduler.step_correct(snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample
              snake_case__  =			new_scheduler.step_correct(
                  snake_case__						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample

              assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler correction are not identical"
      def A_     (     self	):


          for timesteps in [10, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=snake_case__	)
      def A_     (     self	):


          for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]						,		[1, 1_00, 10_00]	):
              self.check_over_configs(sigma_min=snake_case__						,		sigma_max=snake_case__	)
      def A_     (     self	):


          for t in [0.1, 0.5, 0.7_5]:
              self.check_over_forward(time_step=snake_case__	)
      def A_     (     self	):
          snake_case__  =			dict(self.forward_default_kwargs	)

          snake_case__  =			self.scheduler_classes[0]
          snake_case__  =			self.get_scheduler_config()
          snake_case__  =			scheduler_class(**snake_case__	)

          snake_case__  =			3

          snake_case__  =			self.dummy_model()
          snake_case__  =			self.dummy_sample_deter

          scheduler.set_sigmas(snake_case__	)
          scheduler.set_timesteps(snake_case__	)
          snake_case__  =			torch.manual_seed(0	)

          for i, t in enumerate(scheduler.timesteps	):
              snake_case__  =			scheduler.sigmas[i]

              for _ in range(scheduler.config.correct_steps	):
                  with torch.no_grad():
                      snake_case__  =			model(snake_case__						,		snake_case__	)
                  snake_case__  =			scheduler.step_correct(snake_case__						,		snake_case__						,		generator=snake_case__						,		**snake_case__	).prev_sample

              with torch.no_grad():
                  snake_case__  =			model(snake_case__						,		snake_case__	)

              snake_case__  =			scheduler.step_pred(snake_case__						,		snake_case__						,		snake_case__						,		generator=snake_case__						,		**snake_case__	)
              snake_case__  ,   snake_case__  =			output.prev_sample, output.prev_sample_mean

          snake_case__  =			torch.sum(torch.abs(snake_case__	)	)
          snake_case__  =			torch.mean(torch.abs(snake_case__	)	)

          assert np.isclose(result_sum.item()						,		1_43_72_75_85_28.0	)
          assert np.isclose(result_mean.item()						,		18_71_45_30.0	)



      def A_     (     self	):
          snake_case__  =			dict(self.forward_default_kwargs	)

          snake_case__  =			kwargs.pop("num_inference_steps"						,		snake_case__	)

          for scheduler_class in self.scheduler_classes:
              snake_case__  =			self.get_scheduler_config()
              snake_case__  =			scheduler_class(**snake_case__	)

              snake_case__  =			self.dummy_sample
              snake_case__  =			0.1 * sample

              if num_inference_steps is not None and hasattr(snake_case__						,		"set_timesteps"	):
                  scheduler.set_timesteps(snake_case__	)
              elif num_inference_steps is not None and not hasattr(snake_case__						,		"set_timesteps"	):
                  snake_case__  =			num_inference_steps

              snake_case__  =			scheduler.step_pred(snake_case__						,		0						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample
              snake_case__  =			scheduler.step_pred(snake_case__						,		1						,		snake_case__						,		generator=torch.manual_seed(0	)						,		**snake_case__	).prev_sample

              self.assertEqual(output_a.shape						,		sample.shape	)
              self.assertEqual(output_a.shape						,		output_a.shape	)


from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__magic_name__           =					[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			len(cells[0]							)  # 31
    snake_case__  =			[]
    for i in range(__lowerCAmelCase							):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        snake_case__  =			0 if i == 0 else cells[time][i - 1]
        snake_case__  =			0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        snake_case__  =			7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""      ,						2							)
        next_generation.append(rule[situation]							)
    return next_generation





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


if __name__ == "__main__":
     __magic_name__           =					bin(int(input('''Rule:\n''').strip()))[2:]
     __magic_name__           =					format_ruleset(int(rule_num))
     for time in range(16):
          CELLS.append(new_generation(CELLS, rule, time))
     __magic_name__           =					generate_image(CELLS)
     # Uncomment to save the image
     # img.save(f"rule_{rule_num}.png")
     img.show()



