

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)





def      snake_case_				(__A					:						Optional[Any]					)      ->     Union[str, Any]:
   __lowerCAmelCase  :      Tuple    =						torch.exp(__A					)
   __lowerCAmelCase  :      Union[str, Any]    =						torch.sum(__A ,       dim=1					)  # sum of exp(x_i)
   __lowerCAmelCase  :      Union[str, Any]    =						torch.sum(x * exp_x ,       dim=1					)  # sum of x_i * exp(x_i)
   return torch.log(__A					) - B / A



class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Any   )							->     int:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Optional[Any]    =						config.output_attentions
     __lowerCAmelCase  :      int    =						config.output_hidden_states
     __lowerCAmelCase  :      Tuple    =						nn.ModuleList([BertLayer(lowerCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )
     __lowerCAmelCase  :      Optional[Any]    =						nn.ModuleList([BertHighway(lowerCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )

     __lowerCAmelCase  :      List[Any]    =						[-1 for _ in range(config.num_hidden_layers   )]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Any   )							->     Dict:






     """simple docstring"""







     if (type(lowerCAmelCase   ) is float) or (type(lowerCAmelCase   ) is int):
        for i in range(len(self.early_exit_entropy   )   ):
           __lowerCAmelCase  :      int    =						x
     else:
        __lowerCAmelCase  :      Dict    =						x







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   int   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						pooler.state_dict()
     for highway in self.highway:
        for name, param in highway.pooler.state_dict().items():
           param.copy_(loaded_model[name]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[Any]=None	,							lowerCAmelCase			:   str=None	,							lowerCAmelCase			:   Tuple=None	,							)							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						()
     __lowerCAmelCase  :      Union[str, Any]    =						()
     __lowerCAmelCase  :      Dict    =						()
     for i, layer_module in enumerate(self.layer   ):
        if self.output_hidden_states:
           __lowerCAmelCase  :      Union[str, Any]    =						all_hidden_states + (hidden_states,)

        __lowerCAmelCase  :      List[Any]    =						layer_module(
            lowerCAmelCase	,							lowerCAmelCase	,							head_mask[i]	,							lowerCAmelCase	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						layer_outputs[0]

        if self.output_attentions:
           __lowerCAmelCase  :      str    =						all_attentions + (layer_outputs[1],)

        __lowerCAmelCase  :      Dict    =						(hidden_states,)
        if self.output_hidden_states:
           __lowerCAmelCase  :      str    =						current_outputs + (all_hidden_states,)
        if self.output_attentions:
           __lowerCAmelCase  :      Dict    =						current_outputs + (all_attentions,)

        __lowerCAmelCase  :      Dict    =						self.highway[i](lowerCAmelCase   )
        # logits, pooled_output

        if not self.training:
           __lowerCAmelCase  :      int    =						highway_exit[0]
           __lowerCAmelCase  :      Dict    =						entropy(lowerCAmelCase   )
           __lowerCAmelCase  :      List[Any]    =						highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
           __lowerCAmelCase  :      Tuple    =						all_highway_exits + (highway_exit,)

           if highway_entropy < self.early_exit_entropy[i]:
              __lowerCAmelCase  :      Tuple    =						(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
              raise HighwayException(lowerCAmelCase	,							i + 1   )
        else:
           __lowerCAmelCase  :      Tuple    =						all_highway_exits + (highway_exit,)

        # Add last layer
     if self.output_hidden_states:
        __lowerCAmelCase  :      List[str]    =						all_hidden_states + (hidden_states,)

     __lowerCAmelCase  :      Any    =						(hidden_states,)
     if self.output_hidden_states:
        __lowerCAmelCase  :      int    =						outputs + (all_hidden_states,)
     if self.output_attentions:
        __lowerCAmelCase  :      Any    =						outputs + (all_attentions,)

     __lowerCAmelCase  :      int    =						outputs + (all_highway_exits,)
     return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "       ,							a_       ,							)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   str	,							lowerCAmelCase			:   Dict   )							->     Dict:






     """simple docstring"""







     super().__init__(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						config

     __lowerCAmelCase  :      str    =						BertEmbeddings(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						DeeBertEncoder(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						BertPooler(lowerCAmelCase   )

     self.init_weights()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     str:






     """simple docstring"""







     self.encoder.init_highway_pooler(self.pooler   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Any:






     """simple docstring"""







     return self.embeddings.word_embeddings







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						value







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   Any   )							->     Optional[Any]:






     """simple docstring"""







     for layer, heads in heads_to_prune.items():
        self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase   )







  @add_start_docstrings_to_model_forward(lowerCAmelCase   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   Optional[Any]=None	,							lowerCAmelCase			:   int=None	,							lowerCAmelCase			:   str=None	,							lowerCAmelCase			:   List[Any]=None	,							lowerCAmelCase			:   int=None	,							lowerCAmelCase			:   int=None	,							)							->     List[str]:






     """simple docstring"""







     if input_ids is not None and inputs_embeds is not None:
        raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""   )
     elif input_ids is not None:
        __lowerCAmelCase  :      int    =						input_ids.size()
     elif inputs_embeds is not None:
        __lowerCAmelCase  :      List[Any]    =						inputs_embeds.size()[:-1]
     else:
        raise ValueError("""You have to specify either input_ids or inputs_embeds"""   )

     __lowerCAmelCase  :      Dict    =						input_ids.device if input_ids is not None else inputs_embeds.device

     if attention_mask is None:
        __lowerCAmelCase  :      str    =						torch.ones(lowerCAmelCase	,							device=lowerCAmelCase   )
     if encoder_attention_mask is None:
        __lowerCAmelCase  :      Optional[int]    =						torch.ones(lowerCAmelCase	,							device=lowerCAmelCase   )
     if token_type_ids is None:
        __lowerCAmelCase  :      Dict    =						torch.zeros(lowerCAmelCase	,							dtype=torch.long	,							device=lowerCAmelCase   )

     # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
     # ourselves in which case we just need to make it broadcastable to all heads.
     __lowerCAmelCase  :      torch.Tensor    =						self.get_extended_attention_mask(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

     # If a 2D ou 3D attention mask is provided for the cross-attention
     # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
     if encoder_attention_mask.dim() == 3:
        __lowerCAmelCase  :      List[Any]    =						encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.dim() == 2:
        __lowerCAmelCase  :      Optional[Any]    =						encoder_attention_mask[:, None, None, :]

     __lowerCAmelCase  :      Optional[Any]    =						encoder_extended_attention_mask.to(
         dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
     __lowerCAmelCase  :      int    =						(1.0 - encoder_extended_attention_mask) * -1_0000.0

     # Prepare head mask if needed
     # 1.0 in head_mask indicate we keep the head
     # attention_probs has shape bsz x n_heads x N x N
     # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
     # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
     __lowerCAmelCase  :      Optional[int]    =						self.get_head_mask(lowerCAmelCase	,							self.config.num_hidden_layers   )

     __lowerCAmelCase  :      Optional[Any]    =						self.embeddings(
         input_ids=lowerCAmelCase	,							position_ids=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							inputs_embeds=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						self.encoder(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							head_mask=lowerCAmelCase	,							encoder_hidden_states=lowerCAmelCase	,							encoder_attention_mask=lowerCAmelCase	,							)
     __lowerCAmelCase  :      List[Any]    =						encoder_outputs[0]
     __lowerCAmelCase  :      Union[str, Any]    =						self.pooler(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						(
         sequence_output,
         pooled_output,
     ) + encoder_outputs[
         1:
     ]  # add hidden_states and attentions if they are here
     return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[int]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						message
     __lowerCAmelCase  :      Optional[int]    =						exit_layer  # start from 1!






class SCREAMING_SNAKE_CASE     (  nn.Module			):
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     super().__init__()
     __lowerCAmelCase  :      Optional[int]    =						BertPooler(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						nn.Dropout(config.hidden_dropout_prob   )
     __lowerCAmelCase  :      Union[str, Any]    =						nn.Linear(config.hidden_size	,							config.num_labels   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Any   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						encoder_outputs[0]
     __lowerCAmelCase  :      List[Any]    =						self.pooler(lowerCAmelCase   )
     # "return" pooler_output

     # BertModel
     __lowerCAmelCase  :      List[str]    =						(pooler_input, pooler_output) + encoder_outputs[1:]
     # "return" bmodel_output

     # Dropout and classification
     __lowerCAmelCase  :      Dict    =						bmodel_output[1]

     __lowerCAmelCase  :      List[str]    =						self.dropout(lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						self.classifier(lowerCAmelCase   )

     return logits, pooled_output






@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "       ,							a_       ,							)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Tuple   )							->     Any:






     """simple docstring"""







     super().__init__(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						config.num_labels
     __lowerCAmelCase  :      str    =						config.num_hidden_layers

     __lowerCAmelCase  :      Tuple    =						DeeBertModel(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						nn.Dropout(config.hidden_dropout_prob   )
     __lowerCAmelCase  :      List[str]    =						nn.Linear(config.hidden_size	,							self.config.num_labels   )

     self.init_weights()







  @add_start_docstrings_to_model_forward(lowerCAmelCase   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   str=None	,							lowerCAmelCase			:   Optional[Any]=None	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   str=-1	,							lowerCAmelCase			:   List[Any]=False	,							)							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.num_layers
     try:
        __lowerCAmelCase  :      str    =						self.bert(
            lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							position_ids=lowerCAmelCase	,							head_mask=lowerCAmelCase	,							inputs_embeds=lowerCAmelCase	,							)
        # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

        __lowerCAmelCase  :      Optional[int]    =						outputs[1]

        __lowerCAmelCase  :      Any    =						self.dropout(lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						self.classifier(lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						(logits,) + outputs[2:]  # add hidden states and attention if they are here
     except HighwayException as e:
        __lowerCAmelCase  :      int    =						e.message
        __lowerCAmelCase  :      Tuple    =						e.exit_layer
        __lowerCAmelCase  :      Union[str, Any]    =						outputs[0]

     if not self.training:
        __lowerCAmelCase  :      str    =						entropy(lowerCAmelCase   )
        __lowerCAmelCase  :      List[str]    =						[]
        __lowerCAmelCase  :      Tuple    =						[]
     if labels is not None:
        if self.num_labels == 1:
           #  We are doing regression
           __lowerCAmelCase  :      str    =						MSELoss()
           __lowerCAmelCase  :      str    =						loss_fct(logits.view(-1   )	,							labels.view(-1   )   )
        else:
           __lowerCAmelCase  :      Tuple    =						CrossEntropyLoss()
           __lowerCAmelCase  :      Any    =						loss_fct(logits.view(-1	,							self.num_labels   )	,							labels.view(-1   )   )

        # work with highway exits
        __lowerCAmelCase  :      str    =						[]
        for highway_exit in outputs[-1]:
           __lowerCAmelCase  :      Optional[Any]    =						highway_exit[0]
           if not self.training:
              highway_logits_all.append(lowerCAmelCase   )
              highway_entropy.append(highway_exit[2]   )
           if self.num_labels == 1:
              #  We are doing regression
              __lowerCAmelCase  :      str    =						MSELoss()
              __lowerCAmelCase  :      int    =						loss_fct(highway_logits.view(-1   )	,							labels.view(-1   )   )
           else:
              __lowerCAmelCase  :      Optional[int]    =						CrossEntropyLoss()
              __lowerCAmelCase  :      Dict    =						loss_fct(highway_logits.view(-1	,							self.num_labels   )	,							labels.view(-1   )   )
           highway_losses.append(lowerCAmelCase   )

        if train_highway:
           __lowerCAmelCase  :      Optional[int]    =						(sum(highway_losses[:-1]   ),) + outputs
           # exclude the final highway, of course
        else:
           __lowerCAmelCase  :      Optional[int]    =						(loss,) + outputs
     if not self.training:
        __lowerCAmelCase  :      List[str]    =						outputs + ((original_entropy, highway_entropy), exit_layer)
        if output_layer >= 0:
           __lowerCAmelCase  :      Any    =						(
               (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
           )  # use the highway of the last layer

     return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)





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					)      ->     List[Any]:
   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 SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]	,							lowerCAmelCase			:   float = 0.9999	,							lowerCAmelCase			:   float = 0.0	,							lowerCAmelCase			:   int = 0	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Union[float, int] = 1.0	,							lowerCAmelCase			:   Union[float, int] = 2 / 3	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   Dict[str, Any] = None	,							**lowerCAmelCase			:   Any	,							)							->     List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      Optional[int]    =						(
            """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	,							)
        __lowerCAmelCase  :      int    =						parameters.parameters()

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

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

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

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

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

     __lowerCAmelCase  :      List[Any]    =						None

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

     __lowerCAmelCase  :      int    =						model_cls
     __lowerCAmelCase  :      List[Any]    =						model_config







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     "EMAModel":






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						model_cls.load_config(lowerCAmelCase	,							return_unused_kwargs=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						model_cls.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls(model.parameters()	,							model_cls=lowerCAmelCase	,							model_config=model.config   )

     ema_model.load_state_dict(lowerCAmelCase   )
     return ema_model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]   )							->     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__."""   )

     __lowerCAmelCase  :      Dict    =						self.model_cls.from_config(self.model_config   )
     __lowerCAmelCase  :      Optional[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 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     float:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						max(0	,							optimization_step - self.update_after_step - 1   )

     if step <= 0:
        return 0.0

     if self.use_ema_warmup:
        __lowerCAmelCase  :      str    =						1 - (1 + step / self.inv_gamma) ** -self.power
     else:
        __lowerCAmelCase  :      Optional[int]    =						(1 + step) / (10 + step)

     __lowerCAmelCase  :      Any    =						min(lowerCAmelCase	,							self.decay   )
     # make sure decay is not smaller than min_decay
     __lowerCAmelCase  :      Union[str, Any]    =						max(lowerCAmelCase	,							self.min_decay   )
     return cur_decay_value







  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     Optional[int]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      int    =						(
            """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	,							)
        __lowerCAmelCase  :      Any    =						parameters.parameters()

     __lowerCAmelCase  :      Any    =						list(lowerCAmelCase   )

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     __lowerCAmelCase  :      Optional[Any]    =						self.get_decay(self.optimization_step   )
     __lowerCAmelCase  :      Dict    =						decay
     __lowerCAmelCase  :      Tuple    =						1 - decay

     __lowerCAmelCase  :      str    =						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():
           __lowerCAmelCase  :      List[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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Optional[Any]=None   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[
         p.to(device=lowerCAmelCase	,							dtype=lowerCAmelCase   ) if p.is_floating_point() else p.to(device=lowerCAmelCase   )
         for p in self.shadow_params
     ]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     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.
     __lowerCAmelCase  :      Union[str, Any]    =						None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   dict   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						copy.deepcopy(lowerCAmelCase   )

     __lowerCAmelCase  :      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"""   )

     __lowerCAmelCase  :      Tuple    =						state_dict.get("""min_decay"""	,							self.min_decay   )
     if not isinstance(self.min_decay	,							lowerCAmelCase   ):
        raise ValueError("""Invalid min_decay"""   )

     __lowerCAmelCase  :      List[Any]    =						state_dict.get("""optimization_step"""	,							self.optimization_step   )
     if not isinstance(self.optimization_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid optimization_step"""   )

     __lowerCAmelCase  :      Union[str, Any]    =						state_dict.get("""update_after_step"""	,							self.update_after_step   )
     if not isinstance(self.update_after_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid update_after_step"""   )

     __lowerCAmelCase  :      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"""   )

     __lowerCAmelCase  :      Optional[int]    =						state_dict.get("""inv_gamma"""	,							self.inv_gamma   )
     if not isinstance(self.inv_gamma	,							(float, int)   ):
        raise ValueError("""Invalid inv_gamma"""   )

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

     __lowerCAmelCase  :      Optional[Any]    =						state_dict.get("""shadow_params"""	,							lowerCAmelCase   )
     if shadow_params is not None:
        __lowerCAmelCase  :      int    =						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"""   )


