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







class     lowerCAmelCase_     :







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

												if isinstance(_A ,			torch.nn.Module		):
																	_UpperCamelCase			     =     (
																	    '''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''' ,			_A ,			standard_warn=_A ,			)
																	_UpperCamelCase			     =     parameters.parameters()

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

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

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

												_UpperCamelCase			     =     list(_A		)
												_UpperCamelCase			     =     [p.clone().detach() for p in parameters]

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

												_UpperCamelCase			     =     None

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

												_UpperCamelCase			     =     model_cls
												_UpperCamelCase			     =     model_config







							@classmethod
							def  UpperCamelCase_						(   cls      :     Optional[Any] ,			_A      :     Any ,			_A      :     str		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     model_cls.load_config(_A ,			return_unused_kwargs=_A		)
												_UpperCamelCase			     =     model_cls.from_pretrained(_A		)

												_UpperCamelCase			     =     cls(model.parameters() ,			model_cls=_A ,			model_config=model.config		)

												ema_model.load_state_dict(_A		)
												return ema_model







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												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__.'''		)

												_UpperCamelCase			     =     self.model_cls.from_config(self.model_config		)
												_UpperCamelCase			     =     self.state_dict()
												state_dict.pop('''shadow_params''' ,			_A		)

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







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     int		):
												_UpperCamelCase			     =     max(0 ,			optimization_step - self.update_after_step - 1		)

												if step <= 0:
																	return 0.0

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

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







							@torch.no_grad()
							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Iterable[torch.nn.Parameter]		):
												if isinstance(_A ,			torch.nn.Module		):
																	_UpperCamelCase			     =     (
																	    '''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''' ,			_A ,			standard_warn=_A ,			)
																	_UpperCamelCase			     =     parameters.parameters()

												_UpperCamelCase			     =     list(_A		)

												self.optimization_step += 1

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

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

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

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







							def  UpperCamelCase_						(   self      :     str ,			_A      :     Iterable[torch.nn.Parameter]		):
												_UpperCamelCase			     =     list(_A		)
												for s_param, param in zip(self.shadow_params ,			_A		):
																	param.data.copy_(s_param.to(param.device		).data		)







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Optional[Any]=None ,			_A      :     Dict=None		):
												_UpperCamelCase			     =     [
												    p.to(device=_A ,			dtype=_A		) if p.is_floating_point() else p.to(device=_A		)
												    for p in self.shadow_params
												]







							def  UpperCamelCase_						(   self      :     Any		):
												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  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     Iterable[torch.nn.Parameter]		):
												_UpperCamelCase			     =     [param.detach().cpu().clone() for param in parameters]







							def  UpperCamelCase_						(   self      :     str ,			_A      :     Iterable[torch.nn.Parameter]		):
												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 ,			_A		):
																	param.data.copy_(c_param.data		)

												# Better memory-wise.
												_UpperCamelCase			     =     None







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     dict		):
												_UpperCamelCase			     =     copy.deepcopy(_A		)

												_UpperCamelCase			     =     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'''		)

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

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

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

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

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

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

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








"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






						def __init__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = (0, 0)
										lowercase    :      str       = None
										lowercase    :      Any       = 0
										lowercase    :      List[Any]       = 0
										lowercase    :      Union[str, Any]       = 0






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										print(self.position				)


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase    :      List[str]       = cell.position[0]
										lowercase    :      List[str]       = cell.position[1]
										lowercase    :      Optional[int]       = []
										for n in neughbour_cord:
														lowercase    :      List[Any]       = current_x + n[0]
														lowercase    :      List[Any]       = current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase    :      Tuple       = Cell()
																		lowercase    :      Union[str, Any]       = (x, y)
																		lowercase    :      Dict       = cell
																		neighbours.append(__magic_name__				)
										return neighbours


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

				while _open:
								lowercase    :      List[str]       = np.argmin([n.f for n in _open] )
								lowercase    :      int       = _open[min_f]
								_closed.append(_open.pop(_A ) )
								if current == goal:
												break
								for n in world.get_neigbours(_A ):
												for c in _closed:
																if c == n:
																				continue
												lowercase    :      List[Any]       = current.g + 1
												lowercase      ,      lowercase    :      Any       = n.position
												lowercase      ,      lowercase    :      List[str]       = goal.position
												lowercase    :      Any       = (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase    :      Union[str, Any]       = n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_A )
				lowercase    :      List[str]       = []
				while current.parent is not None:
								path.append(current.position )
								lowercase    :      int       = current.parent
				path.append(current.position )
				return path[::-1]


if __name__ == "__main__":
				_lowerCAmelCase            =	Gridworld()
				# Start position and goal
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(0, 0)
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(4, 4)
				print(F'path from {start.position} to {goal.position}')
				_lowerCAmelCase            =	astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								_lowerCAmelCase            =	1
				print(world.w)
