






'''simple docstring'''

from __future__ import annotations

UpperCamelCase							='''#'''




class     A    :






 """simple docstring"""





 def __init__(							self ):
       UpperCamelCase_				:   dict					    =							{}
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							self._trie
       for char in text:
             if char not in trie:
                   UpperCamelCase_				:   Tuple					    =							{}
             UpperCamelCase_				:   List[Any]					    =							trie[char]
       UpperCamelCase_				:   Dict					    =							True
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							self._trie
       for char in prefix:
             if char in trie:
                   UpperCamelCase_				:   int					    =							trie[char]
             else:
                   return []
       return self._elements(UpperCamelCase_ )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							[]
       for c, v in d.items():
             UpperCamelCase_				:   Optional[int]					    =							[' '] if c == END else [(c + s) for s in self._elements(UpperCamelCase_ )]
             result.extend(UpperCamelCase_ )
       return tuple(UpperCamelCase_ )






UpperCamelCase							=Trie()
UpperCamelCase							=('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
      trie.insert_word(word)



def 							snake_case					(     a_							: Tuple	)       ->				tuple:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							trie.find_word(_lowercase	)
      return tuple(string + word for word in suffixes	)



def 							snake_case					(     )       ->				None:





      """simple docstring"""

      print(autocomplete_using_trie("""de"""	)	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()














'''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					(     a_							: int	)       ->				Union[str, Any]:





      """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     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase = 0.99_99			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = False			,     __lowerCAmelCase = 1.0			,     __lowerCAmelCase = 2 / 3			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):

       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   Dict					    =							(
                 """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			,     )
             UpperCamelCase_				:   str					    =							parameters.parameters()

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

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

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

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

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

       UpperCamelCase_				:   List[Any]					    =							None

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

       UpperCamelCase_				:   Union[str, Any]					    =							model_cls
       UpperCamelCase_				:   Any					    =							model_config
 @classmethod
 def   _UpperCAmelCase       (							cls			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							model_cls.load_config(__lowerCAmelCase			,     return_unused_kwargs=__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							model_cls.from_pretrained(__lowerCAmelCase )

       UpperCamelCase_				:   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			,     __lowerCAmelCase ):
       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_				:   int					    =							self.model_cls.from_config(self.model_config )
       UpperCamelCase_				:   List[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   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							max(0			,     optimization_step - self.update_after_step - 1 )

       if step <= 0:
             return 0.0

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

       UpperCamelCase_				:   Optional[Any]					    =							min(__lowerCAmelCase			,     self.decay )
       # make sure decay is not smaller than min_decay
       UpperCamelCase_				:   Optional[Any]					    =							max(__lowerCAmelCase			,     self.min_decay )
       return cur_decay_value
 @torch.no_grad()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   str					    =							(
                 """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			,     )
             UpperCamelCase_				:   int					    =							parameters.parameters()

       UpperCamelCase_				:   Optional[int]					    =							list(__lowerCAmelCase )

       self.optimization_step += 1

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

       UpperCamelCase_				:   Optional[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():
                   UpperCamelCase_				:   Optional[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   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							list(__lowerCAmelCase )
       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             param.data.copy_(s_param.to(param.device ).data )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Union[str, Any]					    =							[
           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 ):
       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			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							[param.detach().cpu().clone() for param in parameters]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       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.
       UpperCamelCase_				:   List[Any]					    =							None





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							copy.deepcopy(__lowerCAmelCase )

       UpperCamelCase_				:   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_				:   Dict					    =							state_dict.get("""min_decay"""			,     self.min_decay )
       if not isinstance(self.min_decay			,     __lowerCAmelCase ):
             raise ValueError("""Invalid min_decay""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""optimization_step"""			,     self.optimization_step )
       if not isinstance(self.optimization_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid optimization_step""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""update_after_step"""			,     self.update_after_step )
       if not isinstance(self.update_after_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid update_after_step""" )

       UpperCamelCase_				:   List[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""" )

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

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

       UpperCamelCase_				:   Dict					    =							state_dict.get("""shadow_params"""			,     __lowerCAmelCase )
       if shadow_params is not None:
             UpperCamelCase_				:   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""" )






