

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      a      (			A__	:   int     )    ->      str:





      """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  __lowerCAmelCase     :




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase = 0.9999							,   lowerCAmelCase = 0.0							,   lowerCAmelCase = 0							,   lowerCAmelCase = False							,   lowerCAmelCase = 1.0							,   lowerCAmelCase = 2 / 3							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> str:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   torch.nn.Module					):
              _lowercase	       =(
                  '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							,   )
              _lowercase	       =parameters.parameters()

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

        if kwargs.get('max_value'							,   lowerCAmelCase					) is not None:
              _lowercase	       ='The `max_value` argument is deprecated. Please use `decay` instead.'
              deprecate('max_value'							,   '1.0.0'							,   lowerCAmelCase							,   standard_warn=lowerCAmelCase					)
              _lowercase	       =kwargs['max_value']

        if kwargs.get('min_value'							,   lowerCAmelCase					) is not None:
              _lowercase	       ='The `min_value` argument is deprecated. Please use `min_decay` instead.'
              deprecate('min_value'							,   '1.0.0'							,   lowerCAmelCase							,   standard_warn=lowerCAmelCase					)
              _lowercase	       =kwargs['min_value']

        _lowercase	       =list(lowerCAmelCase					)
        _lowercase	       =[p.clone().detach() for p in parameters]

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

        _lowercase	       =None

        _lowercase	       =decay
        _lowercase	       =min_decay
        _lowercase	       =update_after_step
        _lowercase	       =use_ema_warmup
        _lowercase	       =inv_gamma
        _lowercase	       =power
        _lowercase	       =0
        _lowercase	       =None  # set in `step()`

        _lowercase	       =model_cls
        _lowercase	       =model_config




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   lowerCAmelCase					)  -> "EMAModel":
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =model_cls.load_config(lowerCAmelCase							,   return_unused_kwargs=lowerCAmelCase					)
        _lowercase	       =model_cls.from_pretrained(lowerCAmelCase					)

        _lowercase	       =cls(model.parameters()							,   model_cls=lowerCAmelCase							,   model_config=model.config					)

        ema_model.load_state_dict(lowerCAmelCase					)
        return ema_model




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''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__.'					)

        _lowercase	       =self.model_cls.from_config(self.model_config					)
        _lowercase	       =self.state_dict()
        state_dict.pop('shadow_params'							,   lowerCAmelCase					)

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




  def   A__     (				self							,   lowerCAmelCase					)  -> float:
        '''simple docstring'''






        _lowercase	       =max(0							,   optimization_step - self.update_after_step - 1					)

        if step <= 0:
              return 0.0

        if self.use_ema_warmup:
              _lowercase	       =1 - (1 + step / self.inv_gamma) ** -self.power
        else:
              _lowercase	       =(1 + step) / (10 + step)

        _lowercase	       =min(lowerCAmelCase							,   self.decay					)
        # make sure decay is not smaller than min_decay
        _lowercase	       =max(lowerCAmelCase							,   self.min_decay					)
        return cur_decay_value




  @torch.no_grad()
  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   torch.nn.Module					):
              _lowercase	       =(
                  '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							,   )
              _lowercase	       =parameters.parameters()

        _lowercase	       =list(lowerCAmelCase					)

        self.optimization_step += 1

        # Compute the decay factor for the exponential moving average.
        _lowercase	       =self.get_decay(self.optimization_step					)
        _lowercase	       =decay
        _lowercase	       =1 - decay

        _lowercase	       =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():
                    _lowercase	       =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   A__     (				self							,   lowerCAmelCase					)  -> None:
        '''simple docstring'''






        _lowercase	       =list(lowerCAmelCase					)
        for s_param, param in zip(self.shadow_params							,   lowerCAmelCase					):
              param.data.copy_(s_param.to(param.device					).data					)




  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None					)  -> None:
        '''simple docstring'''






        _lowercase	       =[
            p.to(device=lowerCAmelCase							,   dtype=lowerCAmelCase					) if p.is_floating_point() else p.to(device=lowerCAmelCase					)
            for p in self.shadow_params
        ]




  def   A__     (				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   A__     (				self							,   lowerCAmelCase					)  -> None:
        '''simple docstring'''






        _lowercase	       =[param.detach().cpu().clone() for param in parameters]




  def   A__     (				self							,   lowerCAmelCase					)  -> 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							,   lowerCAmelCase					):
              param.data.copy_(c_param.data					)

        # Better memory-wise.
        _lowercase	       =None




  def   A__     (				self							,   lowerCAmelCase					)  -> None:
        '''simple docstring'''






        _lowercase	       =copy.deepcopy(lowerCAmelCase					)

        _lowercase	       =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'					)

        _lowercase	       =state_dict.get('min_decay'							,   self.min_decay					)
        if not isinstance(self.min_decay							,   lowerCAmelCase					):
              raise ValueError('Invalid min_decay'					)

        _lowercase	       =state_dict.get('optimization_step'							,   self.optimization_step					)
        if not isinstance(self.optimization_step							,   lowerCAmelCase					):
              raise ValueError('Invalid optimization_step'					)

        _lowercase	       =state_dict.get('update_after_step'							,   self.update_after_step					)
        if not isinstance(self.update_after_step							,   lowerCAmelCase					):
              raise ValueError('Invalid update_after_step'					)

        _lowercase	       =state_dict.get('use_ema_warmup'							,   self.use_ema_warmup					)
        if not isinstance(self.use_ema_warmup							,   lowerCAmelCase					):
              raise ValueError('Invalid use_ema_warmup'					)

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

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

        _lowercase	       =state_dict.get('shadow_params'							,   lowerCAmelCase					)
        if shadow_params is not None:
              _lowercase	       =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'					)





def      a      (			A__	:   str      ,   A__	:   bool = False     )    ->      str:





      """simple docstring"""



      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected string as input, found {type(A__     )}'''
            raise ValueError(A__     )
      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected boolean as use_pascal parameter, found {type(A__     )}'''
            raise ValueError(A__     )

      _lowercase	       =input_str.split('_'     )

      _lowercase	       =0 if use_pascal else 1

      _lowercase	       =words[start_index:]

      _lowercase	       =[word[0].upper() + word[1:] for word in words_to_capitalize]

      _lowercase	       ='' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
  from doctest import testmod

  testmod()


