



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 	UpperCAmelCase   (						_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 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			Dict	,	__lowerCamelCase      :			Iterable[torch.nn.Parameter]	,	__lowerCamelCase      :			float = 0.9999	,	__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      :			List[Any]	,	)      ->				List[Any]:

									if isinstance(__lowerCamelCase	,	torch.nn.Module ):
												A							:					List[str]              =						(
												    "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	,	)
												A							:					Tuple              =						parameters.parameters()

												# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
												A							:					List[str]              =						True

									if kwargs.get("max_value"	,	__lowerCamelCase ) is not None:
												A							:					int              =						"The `max_value` argument is deprecated. Please use `decay` instead."
												deprecate("max_value"	,	"1.0.0"	,	__lowerCamelCase	,	standard_warn=__lowerCamelCase )
												A							:					int              =						kwargs["max_value"]

									if kwargs.get("min_value"	,	__lowerCamelCase ) is not None:
												A							:					int              =						"The `min_value` argument is deprecated. Please use `min_decay` instead."
												deprecate("min_value"	,	"1.0.0"	,	__lowerCamelCase	,	standard_warn=__lowerCamelCase )
												A							:					List[Any]              =						kwargs["min_value"]

									A							:					List[Any]              =						list(__lowerCamelCase )
									A							:					List[str]              =						[p.clone().detach() for p in parameters]

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

									A							:					Optional[Any]              =						None

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

									A							:					Any              =						model_cls
									A							:					int              =						model_config







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Any	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict )      ->				"EMAModel":
									A       , A							:					List[Any]              =						model_cls.load_config(__lowerCamelCase	,	return_unused_kwargs=__lowerCamelCase )
									A							:					Optional[int]              =						model_cls.from_pretrained(__lowerCamelCase )

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

									ema_model.load_state_dict(__lowerCamelCase )
									return ema_model







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Tuple )      ->				Optional[int]:
									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							:					Union[str, Any]              =						self.model_cls.from_config(self.model_config )
									A							:					str              =						self.state_dict()
									state_dict.pop("shadow_params"	,	__lowerCamelCase )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			int )      ->				float:
									A							:					Any              =						max(0	,	optimization_step - self.update_after_step - 1 )

									if step <= 0:
												return 0.0

									if self.use_ema_warmup:
												A							:					Any              =						1 - (1 + step / self.inv_gamma) ** -self.power
									else:
												A							:					Union[str, Any]              =						(1 + step) / (10 + step)

									A							:					Dict              =						min(__lowerCamelCase	,	self.decay )
									# make sure decay is not smaller than min_decay
									A							:					Any              =						max(__lowerCamelCase	,	self.min_decay )
									return cur_decay_value







						@torch.no_grad()
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Iterable[torch.nn.Parameter] )      ->				Optional[int]:
									if isinstance(__lowerCamelCase	,	torch.nn.Module ):
												A							:					Dict              =						(
												    "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	,	)
												A							:					Optional[Any]              =						parameters.parameters()

									A							:					Optional[Any]              =						list(__lowerCamelCase )

									self.optimization_step += 1

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

									A							:					List[str]              =						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():
															A							:					Any              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Iterable[torch.nn.Parameter] )      ->				None:
									A							:					Tuple              =						list(__lowerCamelCase )
									for s_param, param in zip(self.shadow_params	,	__lowerCamelCase ):
												param.data.copy_(s_param.to(param.device ).data )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			int=None )      ->				None:
									A							:					int              =						[
									    p.to(device=__lowerCamelCase	,	dtype=__lowerCamelCase ) if p.is_floating_point() else p.to(device=__lowerCamelCase )
									    for p in self.shadow_params
									]







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__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.
									A							:					Tuple              =						None







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			dict )      ->				None:
									A							:					str              =						copy.deepcopy(__lowerCamelCase )

									A							:					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							:					Optional[int]              =						state_dict.get("min_decay"	,	self.min_decay )
									if not isinstance(self.min_decay	,	__lowerCamelCase ):
												raise ValueError("Invalid min_decay" )

									A							:					Optional[int]              =						state_dict.get("optimization_step"	,	self.optimization_step )
									if not isinstance(self.optimization_step	,	__lowerCamelCase ):
												raise ValueError("Invalid optimization_step" )

									A							:					Union[str, 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" )

									A							:					Optional[Any]              =						state_dict.get("use_ema_warmup"	,	self.use_ema_warmup )
									if not isinstance(self.use_ema_warmup	,	__lowerCamelCase ):
												raise ValueError("Invalid use_ema_warmup" )

									A							:					Optional[Any]              =						state_dict.get("inv_gamma"	,	self.inv_gamma )
									if not isinstance(self.inv_gamma	,	(float, int) ):
												raise ValueError("Invalid inv_gamma" )

									A							:					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"	,	__lowerCamelCase )
									if shadow_params is not None:
												A							:					List[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" )




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE           =  """."""


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
							__SCREAMING_SNAKE_CASE           =  []
							__SCREAMING_SNAKE_CASE           =  []
							with open(doctest_file_path) as fp:
														for line in fp:
																					__SCREAMING_SNAKE_CASE           =  line.strip()
																					__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														__SCREAMING_SNAKE_CASE           =  """\n""".join(non_existent_paths)
														raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
							if all_paths != sorted(all_paths):
														raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")