


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

class        a	:




							"""simple docstring"""


							def __init__(							self :						List[Any]					,			lowerCamelCase :						Iterable[torch.nn.Parameter]					,			lowerCamelCase :						float = 0.99_99					,			lowerCamelCase :						float = 0.0					,			lowerCamelCase :						int = 0					,			lowerCamelCase :						bool = False					,			lowerCamelCase :						Union[float, int] = 1.0					,			lowerCamelCase :						Union[float, int] = 2 / 3					,			lowerCamelCase :						Optional[Any] = None					,			lowerCamelCase :						Dict[str, Any] = None					,			**lowerCamelCase :						Optional[int]					,			)			->					Optional[Any]:







														if isinstance(lowerCamelCase__					,			torch.nn.Module       ):
																					__snake_case	:    List[Any]											=     (
																					    "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"					,			lowerCamelCase__					,			standard_warn=lowerCamelCase__					,			)
																					__snake_case	:    List[str]											=     parameters.parameters()

																					# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																					__snake_case	:    Optional[int]											=     True

														if kwargs.get("max_value"					,			lowerCamelCase__       ) is not None:
																					__snake_case	:    Tuple											=     "The `max_value` argument is deprecated. Please use `decay` instead."
																					deprecate("max_value"					,			"1.0.0"					,			lowerCamelCase__					,			standard_warn=lowerCamelCase__       )
																					__snake_case	:    str											=     kwargs["max_value"]

														if kwargs.get("min_value"					,			lowerCamelCase__       ) is not None:
																					__snake_case	:    Optional[int]											=     "The `min_value` argument is deprecated. Please use `min_decay` instead."
																					deprecate("min_value"					,			"1.0.0"					,			lowerCamelCase__					,			standard_warn=lowerCamelCase__       )
																					__snake_case	:    Tuple											=     kwargs["min_value"]

														__snake_case	:    Optional[Any]											=     list(lowerCamelCase__       )
														__snake_case	:    Dict											=     [p.clone().detach() for p in parameters]

														if kwargs.get("device"					,			lowerCamelCase__       ) is not None:
																					__snake_case	:    Any											=     "The `device` argument is deprecated. Please use `to` instead."
																					deprecate("device"					,			"1.0.0"					,			lowerCamelCase__					,			standard_warn=lowerCamelCase__       )
																					self.to(device=kwargs["device"]       )

														__snake_case	:    Union[str, Any]											=     None

														__snake_case	:    int											=     decay
														__snake_case	:    Any											=     min_decay
														__snake_case	:    Optional[int]											=     update_after_step
														__snake_case	:    str											=     use_ema_warmup
														__snake_case	:    Union[str, Any]											=     inv_gamma
														__snake_case	:    Union[str, Any]											=     power
														__snake_case	:    List[str]											=     0
														__snake_case	:    List[str]											=     None  # set in `step()`

														__snake_case	:    Optional[int]											=     model_cls
														__snake_case	:    Union[str, Any]											=     model_config

							@classmethod
							def 					__snake_case      (							cls :						int					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						int       )			->					"EMAModel":
														__snake_case	:    Optional[int]											=     model_cls.load_config(lowerCamelCase__					,			return_unused_kwargs=lowerCamelCase__       )
														__snake_case	:    Union[str, Any]											=     model_cls.from_pretrained(lowerCamelCase__       )

														__snake_case	:    List[str]											=     cls(model.parameters()					,			model_cls=lowerCamelCase__					,			model_config=model.config       )

														ema_model.load_state_dict(lowerCamelCase__       )
														return ema_model

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						int       )			->					Dict:







														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__."       )

														__snake_case	:    int											=     self.model_cls.from_config(self.model_config       )
														__snake_case	:    Union[str, Any]											=     self.state_dict()
														state_dict.pop("shadow_params"					,			lowerCamelCase__       )

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

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						int       )			->					float:
														__snake_case	:    int											=     max(0					,			optimization_step - self.update_after_step - 1       )

														if step <= 0:
																					return 0.0

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

														__snake_case	:    int											=     min(lowerCamelCase__					,			self.decay       )
														# make sure decay is not smaller than min_decay
														__snake_case	:    Union[str, Any]											=     max(lowerCamelCase__					,			self.min_decay       )
														return cur_decay_value

							@torch.no_grad()
							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Iterable[torch.nn.Parameter]       )			->					Dict:







														if isinstance(lowerCamelCase__					,			torch.nn.Module       ):
																					__snake_case	:    Union[str, Any]											=     (
																					    "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"					,			lowerCamelCase__					,			standard_warn=lowerCamelCase__					,			)
																					__snake_case	:    Any											=     parameters.parameters()

														__snake_case	:    Dict											=     list(lowerCamelCase__       )

														self.optimization_step += 1

														# Compute the decay factor for the exponential moving average.
														__snake_case	:    Tuple											=     self.get_decay(self.optimization_step       )
														__snake_case	:    Any											=     decay
														__snake_case	:    Optional[Any]											=     1 - decay

														__snake_case	:    Union[str, Any]											=     contextlib.nullcontext
														if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																					import deepspeed

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

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

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						Iterable[torch.nn.Parameter]       )			->					None:
														__snake_case	:    List[str]											=     list(lowerCamelCase__       )
														for s_param, param in zip(self.shadow_params					,			lowerCamelCase__       ):
																					param.data.copy_(s_param.to(param.device       ).data       )

							def 					__snake_case      (							self :						int					,			lowerCamelCase :						Dict=None					,			lowerCamelCase :						Optional[int]=None       )			->					None:
														__snake_case	:    str											=     [
														    p.to(device=lowerCamelCase__					,			dtype=lowerCamelCase__       ) if p.is_floating_point() else p.to(device=lowerCamelCase__       )
														    for p in self.shadow_params
														]

							def 					__snake_case      (							self :						List[Any]       )			->					dict:
														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 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						Iterable[torch.nn.Parameter]       )			->					None:
														__snake_case	:    Tuple											=     [param.detach().cpu().clone() for param in parameters]

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Iterable[torch.nn.Parameter]       )			->					None:







														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					,			lowerCamelCase__       ):
																					param.data.copy_(c_param.data       )

														# Better memory-wise.
														__snake_case	:    int											=     None






							def 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						dict       )			->					None:
														__snake_case	:    Optional[Any]											=     copy.deepcopy(lowerCamelCase__       )

														__snake_case	:    List[str]											=     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"       )

														__snake_case	:    Union[str, Any]											=     state_dict.get("min_decay"					,			self.min_decay       )
														if not isinstance(self.min_decay					,			lowerCamelCase__       ):
																					raise ValueError("Invalid min_decay"       )

														__snake_case	:    List[str]											=     state_dict.get("optimization_step"					,			self.optimization_step       )
														if not isinstance(self.optimization_step					,			lowerCamelCase__       ):
																					raise ValueError("Invalid optimization_step"       )

														__snake_case	:    List[Any]											=     state_dict.get("update_after_step"					,			self.update_after_step       )
														if not isinstance(self.update_after_step					,			lowerCamelCase__       ):
																					raise ValueError("Invalid update_after_step"       )

														__snake_case	:    str											=     state_dict.get("use_ema_warmup"					,			self.use_ema_warmup       )
														if not isinstance(self.use_ema_warmup					,			lowerCamelCase__       ):
																					raise ValueError("Invalid use_ema_warmup"       )

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

														__snake_case	:    Any											=     state_dict.get("power"					,			self.power       )
														if not isinstance(self.power					,			(float, int)       ):
																					raise ValueError("Invalid power"       )

														__snake_case	:    List[str]											=     state_dict.get("shadow_params"					,			lowerCamelCase__       )
														if shadow_params is not None:
																					__snake_case	:    Optional[Any]											=     shadow_params
																					if not isinstance(self.shadow_params					,			lowerCamelCase__       ):
																												raise ValueError("shadow_params must be a list"       )
																					if not all(isinstance(lowerCamelCase__					,			torch.Tensor       ) for p in self.shadow_params       ):
																												raise ValueError("shadow_params must all be Tensors"       )





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

