



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__       (					__lowercase       )		->		Optional[int]:







						"""simple docstring"""

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





class 							lowerCAmelCase__  :







					def __init__(       self      :     Optional[Any]			,		_A      :     Iterable[torch.nn.Parameter]			,		_A      :     float = 0.99_99			,		_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      :     Optional[int]			,		):

											if isinstance(_A			,		torch.nn.Module):
																	A__	:							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"			,		_A			,		standard_warn=_A			,		)
																	A__	:							List[Any]						    = parameters.parameters()

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

											if kwargs.get("max_value"			,		_A) is not None:
																	A__	:							Union[str, Any]						    = "The `max_value` argument is deprecated. Please use `decay` instead."
																	deprecate("max_value"			,		"1.0.0"			,		_A			,		standard_warn=_A)
																	A__	:							Optional[Any]						    = kwargs["max_value"]

											if kwargs.get("min_value"			,		_A) is not None:
																	A__	:							Dict						    = "The `min_value` argument is deprecated. Please use `min_decay` instead."
																	deprecate("min_value"			,		"1.0.0"			,		_A			,		standard_warn=_A)
																	A__	:							Union[str, Any]						    = kwargs["min_value"]

											A__	:							List[str]						    = list(_A)
											A__	:							Tuple						    = [p.clone().detach() for p in parameters]

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

											A__	:							List[str]						    = None

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

											A__	:							Dict						    = model_cls
											A__	:							List[str]						    = model_config







					@classmethod
					def  _lowercase			(       cls      :     List[str]			,		_A      :     str			,		_A      :     Tuple):
											A__	:							int						    = model_cls.load_config(_A			,		return_unused_kwargs=_A)
											A__	:							List[Any]						    = model_cls.from_pretrained(_A)

											A__	:							Optional[int]						    = cls(model.parameters()			,		model_cls=_A			,		model_config=model.config)

											ema_model.load_state_dict(_A)
											return ema_model







					def  _lowercase			(       self      :     Optional[int]			,		_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__.")

											A__	:							str						    = self.model_cls.from_config(self.model_config)
											A__	:							Tuple						    = self.state_dict()
											state_dict.pop("shadow_params"			,		_A)

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







					def  _lowercase			(       self      :     int			,		_A      :     int):
											A__	:							Dict						    = max(0			,		optimization_step - self.update_after_step - 1)

											if step <= 0:
																	return 0.0

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

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







					@torch.no_grad()
					def  _lowercase			(       self      :     List[Any]			,		_A      :     Iterable[torch.nn.Parameter]):
											if isinstance(_A			,		torch.nn.Module):
																	A__	:							Optional[int]						    = (
																	    "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			,		)
																	A__	:							int						    = parameters.parameters()

											A__	:							Dict						    = list(_A)

											self.optimization_step += 1

											# Compute the decay factor for the exponential moving average.
											A__	:							Any						    = self.get_decay(self.optimization_step)
											A__	:							str						    = decay
											A__	:							Union[str, Any]						    = 1 - decay

											A__	:							Tuple						    = 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():
																							A__	:							Dict						    = 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  _lowercase			(       self      :     Union[str, Any]			,		_A      :     Iterable[torch.nn.Parameter]):
											A__	:							List[str]						    = list(_A)
											for s_param, param in zip(self.shadow_params			,		_A):
																	param.data.copy_(s_param.to(param.device).data)







					def  _lowercase			(       self      :     int			,		_A      :     Union[str, Any]=None			,		_A      :     Tuple=None):
											A__	:							List[Any]						    = [
											    p.to(device=_A			,		dtype=_A) if p.is_floating_point() else p.to(device=_A)
											    for p in self.shadow_params
											]







					def  _lowercase			(       self      :     Union[str, 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  _lowercase			(       self      :     str			,		_A      :     Iterable[torch.nn.Parameter]):
											A__	:							int						    = [param.detach().cpu().clone() for param in parameters]







					def  _lowercase			(       self      :     Dict			,		_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.
											A__	:							Optional[Any]						    = None







					def  _lowercase			(       self      :     int			,		_A      :     dict):
											A__	:							Dict						    = copy.deepcopy(_A)

											A__	:							Optional[Any]						    = 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")

											A__	:							str						    = state_dict.get("min_decay"			,		self.min_decay)
											if not isinstance(self.min_decay			,		_A):
																	raise ValueError("Invalid min_decay")

											A__	:							Union[str, Any]						    = state_dict.get("optimization_step"			,		self.optimization_step)
											if not isinstance(self.optimization_step			,		_A):
																	raise ValueError("Invalid optimization_step")

											A__	:							Optional[Any]						    = state_dict.get("update_after_step"			,		self.update_after_step)
											if not isinstance(self.update_after_step			,		_A):
																	raise ValueError("Invalid update_after_step")

											A__	:							Optional[int]						    = state_dict.get("use_ema_warmup"			,		self.use_ema_warmup)
											if not isinstance(self.use_ema_warmup			,		_A):
																	raise ValueError("Invalid use_ema_warmup")

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

											A__	:							List[str]						    = state_dict.get("power"			,		self.power)
											if not isinstance(self.power			,		(float, int)):
																	raise ValueError("Invalid power")

											A__	:							Tuple						    = state_dict.get("shadow_params"			,		_A)
											if shadow_params is not None:
																	A__	:							Any						    = 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")




import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
		import ctypes
		import msvcrt  # noqa




		class 							lowerCAmelCase__  (	ctypes.Structure						):
							# _fields is a specific attr expected by ctypes
							__A     :    Optional[Any]           =						[('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def 	snake_case__       (					)		->		List[Any]:







						"""simple docstring"""

						if os.name == "nt":
												A__	:							Optional[Any]						    = CursorInfo()
												A__	:							Tuple						    = ctypes.windll.kernelaa.GetStdHandle(-1_1       )
												ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
												A__	:							Any						    = False
												ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
						elif os.name == "posix":
												sys.stdout.write("\033[?25l"       )
												sys.stdout.flush()
def 	snake_case__       (					)		->		Dict:







						"""simple docstring"""

						if os.name == "nt":
												A__	:							List[str]						    = CursorInfo()
												A__	:							Optional[int]						    = ctypes.windll.kernelaa.GetStdHandle(-1_1       )
												ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
												A__	:							int						    = True
												ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowercase							,     ctypes.byref(__lowercase       )       )
						elif os.name == "posix":
												sys.stdout.write("\033[?25h"       )
												sys.stdout.flush()




@contextmanager
def 	snake_case__       (					)		->		Optional[int]:







						"""simple docstring"""

						try:
												hide_cursor()
												yield
						finally:
												show_cursor()