"""simple docstring"""






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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   lowercase__:   str					=      torch.exp(UpperCAmelCase_ )
   lowercase__:   List[str]					=      torch.sum(UpperCAmelCase_       ,    dim=1 )  # sum of exp(x_i)
   lowercase__:   Any					=      torch.sum(x * exp_x       ,    dim=1 )  # sum of x_i * exp(x_i)
   return torch.log(UpperCAmelCase_ ) - B / A


class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   int					=      config.output_attentions
       lowercase__:   List[Any]					=      config.output_hidden_states
       lowercase__:   List[Any]					=      nn.ModuleList([BertLayer(_UpperCAmelCase							) for _ in range(config.num_hidden_layers							)]							)
       lowercase__:   Tuple					=      nn.ModuleList([BertHighway(_UpperCAmelCase							) for _ in range(config.num_hidden_layers							)]							)

       lowercase__:   Tuple					=      [-1 for _ in range(config.num_hidden_layers							)]






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if (type(_UpperCAmelCase							) is float) or (type(_UpperCAmelCase							) is int):
          for i in range(len(self.early_exit_entropy							)							):
             lowercase__:   Dict					=      x
       else:
          lowercase__:   Tuple					=      x






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      pooler.state_dict()
       for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
             param.copy_(loaded_model[name]							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			):
       lowercase__:   Union[str, Any]					=      ()
       lowercase__:   Optional[Any]					=      ()
       lowercase__:   Optional[int]					=      ()
       for i, layer_module in enumerate(self.layer							):
          if self.output_hidden_states:
             lowercase__:   List[str]					=      all_hidden_states + (hidden_states,)

          lowercase__:   Dict					=      layer_module(
              _UpperCAmelCase    ,			_UpperCAmelCase    ,			head_mask[i]    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
          lowercase__:   Tuple					=      layer_outputs[0]

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

          lowercase__:   Optional[int]					=      (hidden_states,)
          if self.output_hidden_states:
             lowercase__:   Dict					=      current_outputs + (all_hidden_states,)
          if self.output_attentions:
             lowercase__:   str					=      current_outputs + (all_attentions,)

          lowercase__:   List[str]					=      self.highway[i](_UpperCAmelCase							)
          # logits, pooled_output

          if not self.training:
             lowercase__:   Dict					=      highway_exit[0]
             lowercase__:   List[str]					=      entropy(_UpperCAmelCase							)
             lowercase__:   Dict					=      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
             lowercase__:   Any					=      all_highway_exits + (highway_exit,)

             if highway_entropy < self.early_exit_entropy[i]:
                lowercase__:   Optional[int]					=      (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                raise HighwayException(_UpperCAmelCase    ,			i + 1							)
          else:
             lowercase__:   Dict					=      all_highway_exits + (highway_exit,)

                # Add last layer
       if self.output_hidden_states:
          lowercase__:   Optional[int]					=      all_hidden_states + (hidden_states,)

       lowercase__:   Any					=      (hidden_states,)
       if self.output_hidden_states:
          lowercase__:   Optional[int]					=      outputs + (all_hidden_states,)
       if self.output_attentions:
          lowercase__:   Optional[int]					=      outputs + (all_attentions,)

       lowercase__:   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). "    ,_UpperCAmelCase    ,)
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__(_UpperCAmelCase							)
       lowercase__:   List[Any]					=      config

       lowercase__:   Optional[Any]					=      BertEmbeddings(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      DeeBertEncoder(_UpperCAmelCase							)
       lowercase__:   Tuple					=      BertPooler(_UpperCAmelCase							)

       self.init_weights()






    def _snake_case			(   self							):
       self.encoder.init_highway_pooler(self.pooler							)






    def _snake_case			(   self							):
       return self.embeddings.word_embeddings






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Tuple					=      value






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






    @add_start_docstrings_to_model_forward(_UpperCAmelCase							)
    def _snake_case			(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			):
       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:
          lowercase__:   Union[str, Any]					=      input_ids.size()
       elif inputs_embeds is not None:
          lowercase__:   List[str]					=      inputs_embeds.size()[:-1]
       else:
          raise ValueError('''You have to specify either input_ids or inputs_embeds'''							)

       lowercase__:   Optional[Any]					=      input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
          lowercase__:   int					=      torch.ones(_UpperCAmelCase    ,			device=_UpperCAmelCase							)
       if encoder_attention_mask is None:
          lowercase__:   Optional[int]					=      torch.ones(_UpperCAmelCase    ,			device=_UpperCAmelCase							)
       if token_type_ids is None:
          lowercase__:   Tuple					=      torch.zeros(_UpperCAmelCase    ,			dtype=torch.long    ,			device=_UpperCAmelCase							)

       # 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.
       lowercase__:   Tuple					=      self.get_extended_attention_mask(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

       # 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:
          lowercase__:   Optional[int]					=      encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
          lowercase__:   Optional[int]					=      encoder_attention_mask[:, None, None, :]

       lowercase__:   Optional[Any]					=      encoder_extended_attention_mask.to(
           dtype=next(self.parameters()							).dtype							)  # fp16 compatibility
       lowercase__:   List[Any]					=      (1.0 - encoder_extended_attention_mask) * -10000.0

       # 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]
       lowercase__:   Any					=      self.get_head_mask(_UpperCAmelCase    ,			self.config.num_hidden_layers							)

       lowercase__:   Dict					=      self.embeddings(
           input_ids=_UpperCAmelCase    ,			position_ids=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase    ,			inputs_embeds=_UpperCAmelCase							)
       lowercase__:   int					=      self.encoder(
           _UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			encoder_hidden_states=_UpperCAmelCase    ,			encoder_attention_mask=_UpperCAmelCase    ,			)
       lowercase__:   Optional[int]					=      encoder_outputs[0]
       lowercase__:   Any					=      self.pooler(_UpperCAmelCase							)

       lowercase__:   Tuple					=      (
           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     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      message
       lowercase__:   str					=      exit_layer  # start from 1!



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   Any					=      BertPooler(_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      nn.Dropout(config.hidden_dropout_prob							)
       lowercase__:   List[str]					=      nn.Linear(config.hidden_size    ,			config.num_labels							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      encoder_outputs[0]
       lowercase__:   str					=      self.pooler(_UpperCAmelCase							)
       # "return" pooler_output

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

       # Dropout and classification
       lowercase__:   Union[str, Any]					=      bmodel_output[1]

       lowercase__:   str					=      self.dropout(_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      self.classifier(_UpperCAmelCase							)

       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. "    ,_UpperCAmelCase    ,)
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__(_UpperCAmelCase							)
       lowercase__:   List[Any]					=      config.num_labels
       lowercase__:   Tuple					=      config.num_hidden_layers

       lowercase__:   int					=      DeeBertModel(_UpperCAmelCase							)
       lowercase__:   List[str]					=      nn.Dropout(config.hidden_dropout_prob							)
       lowercase__:   str					=      nn.Linear(config.hidden_size    ,			self.config.num_labels							)

       self.init_weights()






    @add_start_docstrings_to_model_forward(_UpperCAmelCase							)
    def _snake_case			(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=-1    ,			_UpperCAmelCase=False    ,			):
       lowercase__:   Any					=      self.num_layers
       try:
          lowercase__:   int					=      self.bert(
              _UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase    ,			position_ids=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			inputs_embeds=_UpperCAmelCase    ,			)
          # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

          lowercase__:   Union[str, Any]					=      outputs[1]

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

       if not self.training:
          lowercase__:   Optional[Any]					=      entropy(_UpperCAmelCase							)
          lowercase__:   Dict					=      []
          lowercase__:   Optional[Any]					=      []
       if labels is not None:
          if self.num_labels == 1:
             #  We are doing regression
             lowercase__:   Any					=      MSELoss()
             lowercase__:   Optional[int]					=      loss_fct(logits.view(-1							)    ,			labels.view(-1							)							)
          else:
             lowercase__:   str					=      CrossEntropyLoss()
             lowercase__:   Optional[Any]					=      loss_fct(logits.view(-1    ,			self.num_labels							)    ,			labels.view(-1							)							)

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

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




'''simple docstring'''

a_  :				Any						= """0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich



