

'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class      SCREAMING_SNAKE_CASE__      :
    _A         =							42

    # setable values
    _A         =							42
    _A         =							42
    _A         =							42
    _A         =							None

    # running values
    _A         =							None




    @classmethod
    def   __lowerCamelCase				( cls    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          return cls(common=lowercase__    ,					init_noise_sigma=lowercase__    ,					timesteps=lowercase__    ,					sigmas=lowercase__      )






@dataclass
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							42






class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase,_UpperCAmelCase				):
    _A         =							[e.name for e in FlaxKarrasDiffusionSchedulers]

    _A         =							42




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return True




    @register_to_config
    def __init__( self    ,					lowercase__ = 1000    ,					lowercase__ = 0.0001    ,					lowercase__ = 0.02    ,					lowercase__ = "linear"    ,					lowercase__ = None    ,					lowercase__ = "epsilon"    ,					lowercase__ = jnp.floataa    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	dtype




    def   __lowerCamelCase				( self    ,					lowercase__ = None      ):






          """simple docstring"""






          if common is None:
                SCREAMING_SNAKE_CASE_				:      Any					=	CommonSchedulerState.create(self      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	jnp.arange(0    ,					self.config.num_train_timesteps      ).round()[::-1]
          SCREAMING_SNAKE_CASE_				:      Tuple					=	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

          # standard deviation of the initial noise distribution
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	sigmas.max()

          return LMSDiscreteSchedulerState.create(
              common=lowercase__    ,					init_noise_sigma=lowercase__    ,					timesteps=lowercase__    ,					sigmas=lowercase__    ,					)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          ((SCREAMING_SNAKE_CASE_),						)				:      List[Any]					=	jnp.where(state.timesteps == timestep    ,					size=1      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	step_index[0]

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	state.sigmas[step_index]
          SCREAMING_SNAKE_CASE_				:      Tuple					=	sample / ((sigma**2 + 1) ** 0.5)
          return sample




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          def lms_derivative(lowercase__      ):
                SCREAMING_SNAKE_CASE_				:      Any					=	1.0
                for k in range(lowercase__      ):
                      if current_order == k:
                            continue
                      prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                return prod

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	integrate.quad(lowercase__    ,					state.sigmas[t]    ,					state.sigmas[t + 1]    ,					epsrel=1e-4      )[0]

          return integrated_coeff




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__ = ()      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	jnp.linspace(self.config.num_train_timesteps - 1    ,					0    ,					lowercase__    ,					dtype=self.dtype      )

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	jnp.floor(lowercase__      ).astype(jnp.intaa      )
          SCREAMING_SNAKE_CASE_				:      str					=	jnp.ceil(lowercase__      ).astype(jnp.intaa      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	jnp.mod(lowercase__    ,					1.0      )

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
          SCREAMING_SNAKE_CASE_				:      Tuple					=	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
          SCREAMING_SNAKE_CASE_				:      Dict					=	jnp.concatenate([sigmas, jnp.array([0.0]    ,					dtype=self.dtype      )]      )

          SCREAMING_SNAKE_CASE_				:      Tuple					=	timesteps.astype(jnp.intaa      )

          # initial running values
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	jnp.zeros((0,) + shape    ,					dtype=self.dtype      )

          return state.replace(
              timesteps=lowercase__    ,					sigmas=lowercase__    ,					num_inference_steps=lowercase__    ,					derivatives=lowercase__    ,					)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__ = 4    ,					lowercase__ = True    ,					):






          """simple docstring"""






          if state.num_inference_steps is None:
                raise ValueError(
                    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	state.sigmas[timestep]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                SCREAMING_SNAKE_CASE_				:      int					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          else:
                raise ValueError(
                    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

          # 2. Convert to an ODE derivative
          SCREAMING_SNAKE_CASE_				:      Any					=	(sample - pred_original_sample) / sigma
          SCREAMING_SNAKE_CASE_				:      Any					=	state.replace(derivatives=jnp.append(state.derivatives    ,					lowercase__      )      )
          if len(state.derivatives      ) > order:
                SCREAMING_SNAKE_CASE_				:      Tuple					=	state.replace(derivatives=jnp.delete(state.derivatives    ,					0      )      )

          # 3. Compute linear multistep coefficients
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	min(timestep + 1    ,					lowercase__      )
          SCREAMING_SNAKE_CASE_				:      str					=	[self.get_lms_coefficient(lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      ) for curr_order in range(lowercase__      )]

          # 4. Compute previous sample based on the derivatives path
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	sample + sum(
              coeff * derivative for coeff, derivative in zip(lowercase__    ,					reversed(state.derivatives      )      )      )

          if not return_dict:
                return (prev_sample, state)

          return FlaxLMSSchedulerOutput(prev_sample=lowercase__    ,					state=lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	state.sigmas[timesteps].flatten()
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	broadcast_to_shape_from_left(lowercase__    ,					noise.shape      )

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	original_samples + noise * sigma

          return noisy_samples







    def __len__( self      ):






          """simple docstring"""






          return self.config.num_train_timesteps




'''simple docstring'''






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
snake_case_					  =	[[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  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int   )							->       list[int]:
      """simple docstring"""

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


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     list[list[int]]  ,  SCREAMING_SNAKE_CASE_					:     list[int]  ,  SCREAMING_SNAKE_CASE_					:     int   )							->       list[int]:
      """simple docstring"""

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


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     list[list[int]]   )							->       Image.Image:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Tuple					=	Image.new("RGB"  ,  (len(cells[0]   ), len(SCREAMING_SNAKE_CASE_   ))   )
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	img.load()
      # Generates image
      for w in range(img.width   ):
            for h in range(img.height   ):
                  SCREAMING_SNAKE_CASE_				:      List[Any]					=	2_5_5 - int(2_5_5 * cells[h][w]   )
                  SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(color, color, color)
      return img


if __name__ == "__main__":
      snake_case_					  =	bin(int(input('Rule:\n').strip()))[2:]
      snake_case_					  =	format_ruleset(int(rule_num))
      for time in range(1_6):
            CELLS.append(new_generation(CELLS, rule, time))
      snake_case_					  =	generate_image(CELLS)
      # Uncomment to save the image
      # img.save(f"rule_{rule_num}.png")
      img.show()

