
"""simple docstring"""

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 (		a_      ):





		'''simple docstring'''


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






class 	_lowercase       :




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = 0.9999		,			UpperCAmelCase_ = 0.0		,			UpperCAmelCase_ = 0		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = 1.0		,			UpperCAmelCase_ = 2 / 3		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							List[str]:

			if isinstance(__lowerCAmelCase		,			torch.nn.Module ):
					lowerCamelCase					:      Tuple          =   (
					    '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		,			)
					lowerCamelCase					:      Any          =   parameters.parameters()

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

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

			if kwargs.get('min_value'		,			__lowerCAmelCase ) is not None:
					lowerCamelCase					:      str          =   'The `min_value` argument is deprecated. Please use `min_decay` instead.'
					deprecate('min_value'		,			'1.0.0'		,			__lowerCAmelCase		,			standard_warn=__lowerCAmelCase )
					lowerCamelCase					:      Optional[int]          =   kwargs['min_value']

			lowerCamelCase					:      Optional[int]          =   list(__lowerCAmelCase )
			lowerCamelCase					:      int          =   [p.clone().detach() for p in parameters]

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

			lowerCamelCase					:      List[str]          =   None

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

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




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   model_cls.load_config(__lowerCAmelCase		,			return_unused_kwargs=__lowerCAmelCase )
			lowerCamelCase					:      Optional[Any]          =   model_cls.from_pretrained(__lowerCAmelCase )

			lowerCamelCase					:      Union[str, Any]          =   cls(model.parameters()		,			model_cls=__lowerCAmelCase		,			model_config=model.config )

			ema_model.load_state_dict(__lowerCAmelCase )
			return ema_model




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							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__.' )

			lowerCamelCase					:      List[str]          =   self.model_cls.from_config(self.model_config )
			lowerCamelCase					:      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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      Dict          =   max(0		,			optimization_step - self.update_after_step - 1 )

			if step <= 0:
					return 0.0

			if self.use_ema_warmup:
					lowerCamelCase					:      List[Any]          =   1 - (1 + step / self.inv_gamma) ** -self.power
			else:
					lowerCamelCase					:      List[Any]          =   (1 + step) / (10 + step)

			lowerCamelCase					:      Optional[int]          =   min(__lowerCAmelCase		,			self.decay )
			# make sure decay is not smaller than min_decay
			lowerCamelCase					:      Optional[Any]          =   max(__lowerCAmelCase		,			self.min_decay )
			return cur_decay_value




	@torch.no_grad()
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Union[str, Any]:
			if isinstance(__lowerCAmelCase		,			torch.nn.Module ):
					lowerCamelCase					:      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		,			)
					lowerCamelCase					:      str          =   parameters.parameters()

			lowerCamelCase					:      List[str]          =   list(__lowerCAmelCase )

			self.optimization_step += 1

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

			lowerCamelCase					:      int          =   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():
							lowerCamelCase					:      int          =   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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
			lowerCamelCase					:      Tuple          =   list(__lowerCAmelCase )
			for s_param, param in zip(self.shadow_params		,			__lowerCAmelCase ):
					param.data.copy_(s_param.to(param.device ).data )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None )		->							Dict:
			lowerCamelCase					:      Dict          =   [
			    p.to(device=__lowerCAmelCase		,			dtype=__lowerCAmelCase ) if p.is_floating_point() else p.to(device=__lowerCAmelCase )
			    for p in self.shadow_params
			]




	def       _UpperCamelCase   (			self )		->							List[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		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      Dict          =   [param.detach().cpu().clone() for param in parameters]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			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.
			lowerCamelCase					:      List[str]          =   None




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
			lowerCamelCase					:      Optional[Any]          =   copy.deepcopy(__lowerCAmelCase )

			lowerCamelCase					:      Tuple          =   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' )

			lowerCamelCase					:      Tuple          =   state_dict.get('min_decay'		,			self.min_decay )
			if not isinstance(self.min_decay		,			__lowerCAmelCase ):
					raise ValueError('Invalid min_decay' )

			lowerCamelCase					:      Optional[int]          =   state_dict.get('optimization_step'		,			self.optimization_step )
			if not isinstance(self.optimization_step		,			__lowerCAmelCase ):
					raise ValueError('Invalid optimization_step' )

			lowerCamelCase					:      Optional[int]          =   state_dict.get('update_after_step'		,			self.update_after_step )
			if not isinstance(self.update_after_step		,			__lowerCAmelCase ):
					raise ValueError('Invalid update_after_step' )

			lowerCamelCase					:      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' )

			lowerCamelCase					:      List[Any]          =   state_dict.get('inv_gamma'		,			self.inv_gamma )
			if not isinstance(self.inv_gamma		,			(float, int) ):
					raise ValueError('Invalid inv_gamma' )

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

			lowerCamelCase					:      Dict          =   state_dict.get('shadow_params'		,			__lowerCAmelCase )
			if shadow_params is not None:
					lowerCamelCase					:      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' )








"""simple docstring"""

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_A						=			re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   _str_to_version_tuple(self.version_str )




	def __repr__(			self )		->							int:
			return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return self.major, self.minor, self.patch




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return Version(UpperCAmelCase_ )
			elif isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return other
			raise TypeError(F"""{other} (type {type(UpperCAmelCase_ )}) cannot be compared to version.""" )




	def __eq__(			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			try:
					lowerCamelCase					:      List[str]          =   self._validate_operand(UpperCAmelCase_ )
			except (TypeError, ValueError):
					return False
			else:
					return self.tuple == other.tuple




	def __lt__(			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   self._validate_operand(UpperCAmelCase_ )
			return self.tuple < other.tuple




	def __hash__(			self )		->							Optional[Any]:
			return hash(_version_tuple_to_str(self.tuple ) )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_ )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   {f.name for f in dataclasses.fields(cls )}
			return cls(**{k: v for k, v in dic.items() if k in field_names} )




	def       _UpperCamelCase   (			self )		->							str:
			return self.version_str


def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   _VERSION_REG.match(a_      )
		if not res:
				raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""      )
		return tuple(int(a_      ) for v in [res.group('major'      ), res.group('minor'      ), res.group('patch'      )]      )





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return ".".join(str(a_      ) for v in version_tuple      )






