


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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  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   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = 0.9_9_9_9				,       _SCREAMING_SNAKE_CASE = 0.0				,       _SCREAMING_SNAKE_CASE = 0				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = 1.0				,       _SCREAMING_SNAKE_CASE = 2 / 3				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->Union[str, Any]:




         '''simple docstring'''





         if isinstance(_SCREAMING_SNAKE_CASE				,       torch.nn.Module		):
           A_      :				List[Any]		     =					(
               '''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'''				,       _SCREAMING_SNAKE_CASE				,       standard_warn=_SCREAMING_SNAKE_CASE				,       )
           A_      :				Optional[int]		     =					parameters.parameters()

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

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

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

         A_      :				int		     =					list(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					[p.clone().detach() for p in parameters]

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

         A_      :				Optional[int]		     =					None

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

         A_      :				int		     =					model_cls
         A_      :				Dict		     =					model_config



       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->"EMAModel":




         '''simple docstring'''





         A_     ,       A_      :				Union[str, Any]		     =					model_cls.load_config(_SCREAMING_SNAKE_CASE				,       return_unused_kwargs=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					model_cls.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					cls(model.parameters()				,       model_cls=_SCREAMING_SNAKE_CASE				,       model_config=model.config		)

         ema_model.load_state_dict(_SCREAMING_SNAKE_CASE		)
         return ema_model



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Any:




         '''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_      :				List[str]		     =					self.model_cls.from_config(self.model_config		)
         A_      :				Dict		     =					self.state_dict()
         state_dict.pop('''shadow_params'''				,       _SCREAMING_SNAKE_CASE		)

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->float:




         '''simple docstring'''





         A_      :				List[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_      :				Optional[int]		     =					min(_SCREAMING_SNAKE_CASE				,       self.decay		)
         # make sure decay is not smaller than min_decay
         A_      :				List[str]		     =					max(_SCREAMING_SNAKE_CASE				,       self.min_decay		)
         return cur_decay_value



       @torch.no_grad()
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if isinstance(_SCREAMING_SNAKE_CASE				,       torch.nn.Module		):
           A_      :				Union[str, Any]		     =					(
               '''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'''				,       _SCREAMING_SNAKE_CASE				,       standard_warn=_SCREAMING_SNAKE_CASE				,       )
           A_      :				int		     =					parameters.parameters()

         A_      :				Optional[Any]		     =					list(_SCREAMING_SNAKE_CASE		)

         self.optimization_step += 1

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

         A_      :				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				,       _SCREAMING_SNAKE_CASE		):
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             A_      :				Optional[Any]		     =					deepspeed.zero.GatheredParameters(_SCREAMING_SNAKE_CASE				,       modifier_rank=_SCREAMING_SNAKE_CASE		)

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				List[str]		     =					list(_SCREAMING_SNAKE_CASE		)
         for s_param, param in zip(self.shadow_params				,       _SCREAMING_SNAKE_CASE		):
           param.data.copy_(s_param.to(param.device		).data		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None		)->None:




         '''simple docstring'''





         A_      :				Any		     =					[
             p.to(device=_SCREAMING_SNAKE_CASE				,       dtype=_SCREAMING_SNAKE_CASE		) if p.is_floating_point() else p.to(device=_SCREAMING_SNAKE_CASE		)
             for p in self.shadow_params
         ]



       def        _snake_case       (      self		)->dict:




         '''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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				Dict		     =					[param.detach().cpu().clone() for param in parameters]



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''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				,       _SCREAMING_SNAKE_CASE		):
           param.data.copy_(c_param.data		)

         # Better memory-wise.
         A_      :				Tuple		     =					None







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				int		     =					copy.deepcopy(_SCREAMING_SNAKE_CASE		)

         A_      :				List[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				,       _SCREAMING_SNAKE_CASE		):
           raise ValueError('''Invalid min_decay'''		)

         A_      :				Union[str, Any]		     =					state_dict.get('''optimization_step'''				,       self.optimization_step		)
         if not isinstance(self.optimization_step				,       _SCREAMING_SNAKE_CASE		):
           raise ValueError('''Invalid optimization_step'''		)

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

         A_      :				Optional[int]		     =					state_dict.get('''use_ema_warmup'''				,       self.use_ema_warmup		)
         if not isinstance(self.use_ema_warmup				,       _SCREAMING_SNAKE_CASE		):
           raise ValueError('''Invalid use_ema_warmup'''		)

         A_      :				Tuple		     =					state_dict.get('''inv_gamma'''				,       self.inv_gamma		)
         if not isinstance(self.inv_gamma				,       (float, int)		):
           raise ValueError('''Invalid inv_gamma'''		)

         A_      :				int		     =					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'''				,       _SCREAMING_SNAKE_CASE		)
         if shadow_params is not None:
           A_      :				Any		     =					shadow_params
           if not isinstance(self.shadow_params				,       _SCREAMING_SNAKE_CASE		):
             raise ValueError('''shadow_params must be a list'''		)
           if not all(isinstance(_SCREAMING_SNAKE_CASE				,       torch.Tensor		) for p in self.shadow_params		):
             raise ValueError('''shadow_params must all be Tensors'''		)








from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




