



"""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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int    )  ->       List[str]:





     '''simple docstring'''

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






class      UpperCAmelCase_  :



      def __init__(					self					:     Optional[Any]					,     A					:     Iterable[torch.nn.Parameter]					,     A					:     float = 0.9_999					,     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					:     Tuple					,     ):

           if isinstance(_a					,     torch.nn.Module						):
                _UpperCAmelCase      :     int        =   (
                    "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      :     Tuple        =   parameters.parameters()

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

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

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

           _UpperCAmelCase      :     int        =   list(_a						)
           _UpperCAmelCase      :     List[Any]        =   [p.clone().detach() for p in parameters]

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

           _UpperCAmelCase      :     Any        =   None

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

           _UpperCAmelCase      :     Union[str, Any]        =   model_cls
           _UpperCAmelCase      :     List[str]        =   model_config



      @classmethod
      def 						snake_case_ (					cls					:     str					,     A					:     Optional[Any]					,     A					:     List[Any]						):
           _UpperCAmelCase      :     int        =   model_cls.load_config(_a					,     return_unused_kwargs=_a						)
           _UpperCAmelCase      :     Union[str, Any]        =   model_cls.from_pretrained(_a						)

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

           ema_model.load_state_dict(_a						)
           return ema_model



      def 						snake_case_ (					self					:     List[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      :     List[str]        =   self.model_cls.from_config(self.model_config						)
           _UpperCAmelCase      :     List[Any]        =   self.state_dict()
           state_dict.pop("shadow_params"					,     _a						)

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



      def 						snake_case_ (					self					:     str					,     A					:     int						):
           _UpperCAmelCase      :     List[str]        =   max(0					,     optimization_step - self.update_after_step - 1						)

           if step <= 0:
                return 0.0

           if self.use_ema_warmup:
                _UpperCAmelCase      :     Any        =   1 - (1 + step / self.inv_gamma) ** -self.power
           else:
                _UpperCAmelCase      :     Any        =   (1 + step) / (1_0 + step)

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



      @torch.no_grad()
      def 						snake_case_ (					self					:     List[str]					,     A					:     Iterable[torch.nn.Parameter]						):
           if isinstance(_a					,     torch.nn.Module						):
                _UpperCAmelCase      :     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					,     )
                _UpperCAmelCase      :     Dict        =   parameters.parameters()

           _UpperCAmelCase      :     Tuple        =   list(_a						)

           self.optimization_step += 1

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

           _UpperCAmelCase      :     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					,     _a						):
                if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                     _UpperCAmelCase      :     int        =   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 						snake_case_ (					self					:     List[str]					,     A					:     Iterable[torch.nn.Parameter]						):
           _UpperCAmelCase      :     Union[str, Any]        =   list(_a						)
           for s_param, param in zip(self.shadow_params					,     _a						):
                param.data.copy_(s_param.to(param.device						).data						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     List[Any]=None					,     A					:     List[str]=None						):
           _UpperCAmelCase      :     int        =   [
               p.to(device=_a					,     dtype=_a						) if p.is_floating_point() else p.to(device=_a						)
               for p in self.shadow_params
           ]



      def 						snake_case_ (					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 						snake_case_ (					self					:     Optional[int]					,     A					:     Iterable[torch.nn.Parameter]						):
           _UpperCAmelCase      :     int        =   [param.detach().cpu().clone() for param in parameters]



      def 						snake_case_ (					self					:     List[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      :     List[str]        =   None



      def 						snake_case_ (					self					:     int					,     A					:     dict						):
           _UpperCAmelCase      :     Optional[Any]        =   copy.deepcopy(_a						)

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

           _UpperCAmelCase      :     Optional[Any]        =   state_dict.get("optimization_step"					,     self.optimization_step						)
           if not isinstance(self.optimization_step					,     _a						):
                raise ValueError("Invalid optimization_step"						)

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

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

           _UpperCAmelCase      :     Optional[int]        =   state_dict.get("shadow_params"					,     _a						)
           if shadow_params is not None:
                _UpperCAmelCase      :     List[str]        =   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"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_lowerCAmelCase    : Union[str, Any]							=							{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[Any]							=							["GPTSw3Tokenizer"]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 _lowerCAmelCase    : List[Any]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
