


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
			import transformers
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		random.seed(SCREAMING_SNAKE_CASE	)
		np.random.seed(SCREAMING_SNAKE_CASE	)
		torch.manual_seed(SCREAMING_SNAKE_CASE	)
		torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE	)
		# ^^ safe to call this function even if cuda is not available


class 			_a				:

							def __init__(				self:      List[str]       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]       ,		UpperCamelCase_:      float = 0.9999       ,		UpperCamelCase_:      float = 0.0       ,		UpperCamelCase_:      int = 0       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      Union[float, int] = 1.0       ,		UpperCamelCase_:      Union[float, int] = 2 / 3       ,		UpperCamelCase_:      Optional[Any] = None       ,		UpperCamelCase_:      Dict[str, Any] = None       ,		**UpperCamelCase_:      List[Any]       ,		)     ->  Tuple:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		torch.nn.Module				):
											lowercase__				= (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_       ,		)
											lowercase__				= parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											lowercase__				= True

									if kwargs.get('''max_value'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `max_value` argument is deprecated. Please use `decay` instead.'''
											deprecate('''max_value'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											lowercase__				= kwargs['''max_value''']

									if kwargs.get('''min_value'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
											deprecate('''min_value'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											lowercase__				= kwargs['''min_value''']

									lowercase__				= list(UpperCamelCase_				)
									lowercase__				= [p.clone().detach() for p in parameters]

									if kwargs.get('''device'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `device` argument is deprecated. Please use `to` instead.'''
											deprecate('''device'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											self.to(device=kwargs['''device''']				)

									lowercase__				= None

									lowercase__				= decay
									lowercase__				= min_decay
									lowercase__				= update_after_step
									lowercase__				= use_ema_warmup
									lowercase__				= inv_gamma
									lowercase__				= power
									lowercase__				= 0
									lowercase__				= None  # set in `step()`

									lowercase__				= model_cls
									lowercase__				= model_config

							@classmethod
							def  lowerCamelCase_					(				cls:      Union[str, Any]       ,		UpperCamelCase_:      List[Any]       ,		UpperCamelCase_:      int				)     ->  "EMAModel":





									"""simple docstring"""







									lowercase__	,						lowercase__				= model_cls.load_config(UpperCamelCase_       ,		return_unused_kwargs=UpperCamelCase_				)
									lowercase__				= model_cls.from_pretrained(UpperCamelCase_				)

									lowercase__				= cls(model.parameters()       ,		model_cls=UpperCamelCase_       ,		model_config=model.config				)

									ema_model.load_state_dict(UpperCamelCase_				)
									return ema_model

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      Optional[Any]				)     ->  List[str]:





									"""simple docstring"""







									if self.model_cls is None:
											raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''				)

									if self.model_config is None:
											raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''				)

									lowercase__				= self.model_cls.from_config(self.model_config				)
									lowercase__				= self.state_dict()
									state_dict.pop('''shadow_params'''       ,		UpperCamelCase_				)

									model.register_to_config(**UpperCamelCase_				)
									self.copy_to(model.parameters()				)
									model.save_pretrained(UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      int				)     ->  float:





									"""simple docstring"""







									lowercase__				= max(0       ,		optimization_step - self.update_after_step - 1				)

									if step <= 0:
											return 0.0

									if self.use_ema_warmup:
											lowercase__				= 1 - (1 + step / self.inv_gamma) ** -self.power
									else:
											lowercase__				= (1 + step) / (10 + step)

									lowercase__				= min(UpperCamelCase_       ,		self.decay				)
									# make sure decay is not smaller than min_decay
									lowercase__				= max(UpperCamelCase_       ,		self.min_decay				)
									return cur_decay_value

							@torch.no_grad()
							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  int:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		torch.nn.Module				):
											lowercase__				= (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_       ,		)
											lowercase__				= parameters.parameters()

									lowercase__				= list(UpperCamelCase_				)

									self.optimization_step += 1

									# Compute the decay factor for the exponential moving average.
									lowercase__				= self.get_decay(self.optimization_step				)
									lowercase__				= decay
									lowercase__				= 1 - decay

									lowercase__				= contextlib.nullcontext
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase_				):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													lowercase__				= deepspeed.zero.GatheredParameters(UpperCamelCase_       ,		modifier_rank=UpperCamelCase_				)

											with context_manager():
													if param.requires_grad:
															s_param.sub_(one_minus_decay * (s_param - param)				)
													else:
															s_param.copy_(UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									lowercase__				= list(UpperCamelCase_				)
									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase_				):
											param.data.copy_(s_param.to(param.device				).data				)

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Optional[int]=None				)     ->  None:





									"""simple docstring"""







									lowercase__				= [
									    p.to(device=UpperCamelCase_       ,		dtype=UpperCamelCase_				) if p.is_floating_point() else p.to(device=UpperCamelCase_				)
									    for p in self.shadow_params
									]

							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  dict:





									"""simple docstring"""







									return {
									    "decay": self.decay,
									    "min_decay": self.min_decay,
									    "optimization_step": self.optimization_step,
									    "update_after_step": self.update_after_step,
									    "use_ema_warmup": self.use_ema_warmup,
									    "inv_gamma": self.inv_gamma,
									    "power": self.power,
									    "shadow_params": self.shadow_params,
									}

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									lowercase__				= [param.detach().cpu().clone() for param in parameters]

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									if self.temp_stored_params is None:
											raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''				)
									for c_param, param in zip(self.temp_stored_params       ,		UpperCamelCase_				):
											param.data.copy_(c_param.data				)

									# Better memory-wise.
									lowercase__				= None


							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      dict				)     ->  None:





									"""simple docstring"""







									lowercase__				= copy.deepcopy(UpperCamelCase_				)

									lowercase__				= state_dict.get('''decay'''       ,		self.decay				)
									if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError('''Decay must be between 0 and 1'''				)

									lowercase__				= state_dict.get('''min_decay'''       ,		self.min_decay				)
									if not isinstance(self.min_decay       ,		UpperCamelCase_				):
											raise ValueError('''Invalid min_decay'''				)

									lowercase__				= state_dict.get('''optimization_step'''       ,		self.optimization_step				)
									if not isinstance(self.optimization_step       ,		UpperCamelCase_				):
											raise ValueError('''Invalid optimization_step'''				)

									lowercase__				= state_dict.get('''update_after_step'''       ,		self.update_after_step				)
									if not isinstance(self.update_after_step       ,		UpperCamelCase_				):
											raise ValueError('''Invalid update_after_step'''				)

									lowercase__				= state_dict.get('''use_ema_warmup'''       ,		self.use_ema_warmup				)
									if not isinstance(self.use_ema_warmup       ,		UpperCamelCase_				):
											raise ValueError('''Invalid use_ema_warmup'''				)

									lowercase__				= state_dict.get('''inv_gamma'''       ,		self.inv_gamma				)
									if not isinstance(self.inv_gamma       ,		(float, int)				):
											raise ValueError('''Invalid inv_gamma'''				)

									lowercase__				= state_dict.get('''power'''       ,		self.power				)
									if not isinstance(self.power       ,		(float, int)				):
											raise ValueError('''Invalid power'''				)

									lowercase__				= state_dict.get('''shadow_params'''       ,		UpperCamelCase_				)
									if shadow_params is not None:
											lowercase__				= shadow_params
											if not isinstance(self.shadow_params       ,		UpperCamelCase_				):
													raise ValueError('''shadow_params must be a list'''				)
											if not all(isinstance(UpperCamelCase_       ,		torch.Tensor				) for p in self.shadow_params				):
													raise ValueError('''shadow_params must all be Tensors'''				)










from __future__ import annotations

from PIL import Image

# Define glider example
__a   :							Tuple						= [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__a   :							Optional[Any]						= [[0, 1, 0], [0, 1, 0], [0, 1, 0]]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]     )    ->   list[list[int]]:





    """simple docstring"""

    __A       =						[]
    for i in range(len(__lowercase     )     ):
        __A       =						[]
        for j in range(len(cells[i]     )     ):
            # Get the number of live neighbours
            __A       =						0
            if i > 0 and j > 0:
                neighbour_count += cells[i - 1][j - 1]
            if i > 0:
                neighbour_count += cells[i - 1][j]
            if i > 0 and j < len(cells[i]     ) - 1:
                neighbour_count += cells[i - 1][j + 1]
            if j > 0:
                neighbour_count += cells[i][j - 1]
            if j < len(cells[i]     ) - 1:
                neighbour_count += cells[i][j + 1]
            if i < len(__lowercase     ) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(__lowercase     ) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(__lowercase     ) - 1 and j < len(cells[i]     ) - 1:
                neighbour_count += cells[i + 1][j + 1]

            # Rules of the game of life (excerpt from Wikipedia):
            # 1. Any live cell with two or three live neighbours survives.
            # 2. Any dead cell with three live neighbours becomes a live cell.
            # 3. All other live cells die in the next generation.
            #    Similarly, all other dead cells stay dead.
            __A       =						cells[i][j] == 1
            if (
                (alive and 2 <= neighbour_count <= 3)
                or not alive
                and neighbour_count == 3
            ):
                next_generation_row.append(1     )
            else:
                next_generation_row.append(0     )

        next_generation.append(__lowercase     )
    return next_generation



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]      ,					__lowercase :      int     )    ->   list[Image.Image]:





    """simple docstring"""

    __A       =						[]
    for _ in range(__lowercase     ):
        # Create output image
        __A       =						Image.new("""RGB"""      ,					(len(cells[0]     ), len(__lowercase     ))     )
        __A       =						img.load()

        # Save cells to image
        for x in range(len(__lowercase     )     ):
            for y in range(len(cells[0]     )     ):
                __A       =						2_5_5 - cells[y][x] * 2_5_5
                __A       =						(colour, colour, colour)

        # Save image
        images.append(__lowercase     )
        __A       =						new_generation(__lowercase     )
    return images


if __name__ == "__main__":
  __a   :							Optional[int]						= generate_images(GLIDER, 16)
  images[0].save("out.gif", save_all=True, append_images=images[1:])




