





'''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 	_lowerCAmelCase      (  __snake_case  :			int      )					->					List[Any]:
     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 			SCREAMING_SNAKE_CASE   :


     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						,     ):

          '''simple docstring'''






          if isinstance(_UpperCAmelCase						,     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'						,     _UpperCAmelCase						,     standard_warn=_UpperCAmelCase						,     )
               __A						:			Optional[int]								=	parameters.parameters()

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

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

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

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

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

          __A						:			Optional[Any]								=	None

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

          __A						:			Tuple								=	model_cls
          __A						:			Tuple								=	model_config


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A			,__A						:			Dict								=	model_cls.load_config(_UpperCAmelCase						,     return_unused_kwargs=_UpperCAmelCase)
          __A						:			int								=	model_cls.from_pretrained(_UpperCAmelCase)

          __A						:			str								=	cls(model.parameters()						,     model_cls=_UpperCAmelCase						,     model_config=model.config)

          ema_model.load_state_dict(_UpperCAmelCase)
          return ema_model


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          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						:			Optional[int]								=	self.model_cls.from_config(self.model_config)
          __A						:			List[str]								=	self.state_dict()
          state_dict.pop('shadow_params'						,     _UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	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						:			str								=	(1 + step) / (10 + step)

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


     @torch.no_grad()
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if isinstance(_UpperCAmelCase						,     torch.nn.Module):
               __A						:			List[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'						,     _UpperCAmelCase						,     standard_warn=_UpperCAmelCase						,     )
               __A						:			List[Any]								=	parameters.parameters()

          __A						:			List[Any]								=	list(_UpperCAmelCase)

          self.optimization_step += 1

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

          __A						:			List[Any]								=	contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
               import deepspeed

          for s_param, param in zip(self.shadow_params						,     _UpperCAmelCase):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                    __A						:			str								=	deepspeed.zero.GatheredParameters(_UpperCAmelCase						,     modifier_rank=_UpperCAmelCase)

               with context_manager():
                    if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param))
                    else:
                         s_param.copy_(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Tuple								=	list(_UpperCAmelCase)
          for s_param, param in zip(self.shadow_params						,     _UpperCAmelCase):
               param.data.copy_(s_param.to(param.device).data)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	[
              p.to(device=_UpperCAmelCase						,     dtype=_UpperCAmelCase) if p.is_floating_point() else p.to(device=_UpperCAmelCase)
              for p in self.shadow_params
          ]


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          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						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			int								=	[param.detach().cpu().clone() for param in parameters]


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          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						,     _UpperCAmelCase):
               param.data.copy_(c_param.data)

          # Better memory-wise.
          __A						:			Dict								=	None


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	copy.deepcopy(_UpperCAmelCase)

          __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						:			List[str]								=	state_dict.get('min_decay'						,     self.min_decay)
          if not isinstance(self.min_decay						,     _UpperCAmelCase):
               raise ValueError('Invalid min_decay')

          __A						:			str								=	state_dict.get('optimization_step'						,     self.optimization_step)
          if not isinstance(self.optimization_step						,     _UpperCAmelCase):
               raise ValueError('Invalid optimization_step')

          __A						:			Tuple								=	state_dict.get('update_after_step'						,     self.update_after_step)
          if not isinstance(self.update_after_step						,     _UpperCAmelCase):
               raise ValueError('Invalid update_after_step')

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

          __A						:			List[str]								=	state_dict.get('shadow_params'						,     _UpperCAmelCase)
          if shadow_params is not None:
               __A						:			Optional[int]								=	shadow_params
               if not isinstance(self.shadow_params						,     _UpperCAmelCase):
                    raise ValueError('shadow_params must be a list')
               if not all(isinstance(_UpperCAmelCase						,     torch.Tensor) for p in self.shadow_params):
                    raise ValueError('shadow_params must all be Tensors')






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))