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       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

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




class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_ = 0.9999				,				A_ = 0.0				,				A_ = 0				,				A_ = False				,				A_ = 1.0				,				A_ = 2 / 3				,				A_ = None				,				A_ = None				,				**A_				,				) ->				Optional[Any]:






           """simple docstring"""

           if isinstance(A_				,				torch.nn.Module						):
                 _lowerCamelCase			    =				(
                     '''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_				,				)
                 _lowerCamelCase			    =				parameters.parameters()

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

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

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

           _lowerCamelCase			    =				list(A_						)
           _lowerCamelCase			    =				[p.clone().detach() for p in parameters]

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

           _lowerCamelCase			    =				None

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

           _lowerCamelCase			    =				model_cls
           _lowerCamelCase			    =				model_config



     @classmethod
     def 		UpperCamelCase_						(					cls				,				A_				,				A_						) ->				"EMAModel":






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				model_cls.load_config(A_				,				return_unused_kwargs=A_						)
           _lowerCamelCase			    =				model_cls.from_pretrained(A_						)

           _lowerCamelCase			    =				cls(model.parameters()				,				model_cls=A_				,				model_config=model.config						)

           ema_model.load_state_dict(A_						)
           return ema_model



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[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__.'''						)

           _lowerCamelCase			    =				self.model_cls.from_config(self.model_config						)
           _lowerCamelCase			    =				self.state_dict()
           state_dict.pop('''shadow_params'''				,				A_						)

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



     def 		UpperCamelCase_						(					self				,				A_						) ->				float:






           """simple docstring"""
           _lowerCamelCase			    =				max(0				,				optimization_step - self.update_after_step - 1						)

           if step <= 0:
                 return 0.0

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

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



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           if isinstance(A_				,				torch.nn.Module						):
                 _lowerCamelCase			    =				(
                     '''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_				,				)
                 _lowerCamelCase			    =				parameters.parameters()

           _lowerCamelCase			    =				list(A_						)

           self.optimization_step += 1

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

           _lowerCamelCase			    =				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():
                       _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				list(A_						)
           for s_param, param in zip(self.shadow_params				,				A_						):
                 param.data.copy_(s_param.to(param.device						).data						)



     def 		UpperCamelCase_						(					self				,				A_=None				,				A_=None						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				[
               p.to(device=A_				,				dtype=A_						) if p.is_floating_point() else p.to(device=A_						)
               for p in self.shadow_params
           ]



     def 		UpperCamelCase_						(					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 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				[param.detach().cpu().clone() for param in parameters]



     def 		UpperCamelCase_						(					self				,				A_						) ->				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				,				A_						):
                 param.data.copy_(c_param.data						)

           # Better memory-wise.
           _lowerCamelCase			    =				None





     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				copy.deepcopy(A_						)

           _lowerCamelCase			    =				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'''						)

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

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

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

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

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

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

           _lowerCamelCase			    =				state_dict.get('''shadow_params'''				,				A_						)
           if shadow_params is not None:
                 _lowerCamelCase			    =				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'''						)
from typing import List

import numpy as np
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				{key: len(__UpperCAmelCase		) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      if len(set(lists_lengths.values()		)		) > 1:
            raise RuntimeError(
                (
                    '''Sharding is ambiguous for this dataset: '''
                    + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                    + '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()		)
                    + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                    + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
                )		)
      _lowerCamelCase			    =				max(lists_lengths.values() ,						default=0		)
      return max(1 ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[range]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for group_idx in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            _lowerCamelCase			    =				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            _lowerCamelCase			    =				range(__UpperCAmelCase ,						start + num_shards_to_add		)
            shards_indices_per_group.append(__UpperCAmelCase		)
      return shards_indices_per_group
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[dict]:



      '''simple docstring'''

      _lowerCamelCase			    =				_number_of_shards_in_gen_kwargs(__UpperCAmelCase		)
      if num_shards == 1:
            return [dict(__UpperCAmelCase		)]
      else:
            _lowerCamelCase			    =				_distribute_shards(num_shards=__UpperCAmelCase ,						max_num_jobs=__UpperCAmelCase		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(__UpperCAmelCase		)		)
            ]
def       __magic_name__(  __UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key] ,						__UpperCAmelCase		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      _lowerCamelCase			    =				{len(__UpperCAmelCase		) for value in gen_kwargs.values() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      _lowerCamelCase			    =				{}
      for size in list_sizes:
            _lowerCamelCase			    =				list(range(__UpperCAmelCase		)		)
            rng.shuffle(indices_per_size[size]		)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      _lowerCamelCase			    =				dict(__UpperCAmelCase		)
      for key, value in shuffled_kwargs.items():
            if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		):
                  _lowerCamelCase			    =				[value[i] for i in indices_per_size[len(__UpperCAmelCase		)]]
      return shuffled_kwargs