"""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    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple					)					->				List[Any]:





      """simple docstring"""






      snake_case								=			torch.exp(_UpperCamelCase					)
      snake_case								=			torch.sum(_UpperCamelCase ,			dim=1					)  # sum of exp(x_i)
      snake_case								=			torch.sum(x * exp_x ,			dim=1					)  # sum of x_i * exp(x_i)
      return torch.log(_UpperCamelCase					) - B / A



class   lowerCAmelCase_    (			nn.Module					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase    ):






          """simple docstring"""

          super().__init__()
          snake_case								=			config.output_attentions
          snake_case								=			config.output_hidden_states
          snake_case								=			nn.ModuleList([BertLayer(lowerCAmelCase    ) for _ in range(config.num_hidden_layers    )]    )
          snake_case								=			nn.ModuleList([BertHighway(lowerCAmelCase    ) for _ in range(config.num_hidden_layers    )]    )

          snake_case								=			[-1 for _ in range(config.num_hidden_layers    )]

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

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

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			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		,    lowerCAmelCase		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    ):






          """simple docstring"""

          snake_case								=			()
          snake_case								=			()
          snake_case								=			()
          for i, layer_module in enumerate(self.layer    ):
                if self.output_hidden_states:
                      snake_case								=			all_hidden_states + (hidden_states,)

                snake_case								=			layer_module(
                    lowerCAmelCase		,    lowerCAmelCase		,    head_mask[i]		,    lowerCAmelCase		,    lowerCAmelCase    )
                snake_case								=			layer_outputs[0]

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

                snake_case								=			(hidden_states,)
                if self.output_hidden_states:
                      snake_case								=			current_outputs + (all_hidden_states,)
                if self.output_attentions:
                      snake_case								=			current_outputs + (all_attentions,)

                snake_case								=			self.highway[i](lowerCAmelCase    )
                # logits, pooled_output

                if not self.training:
                      snake_case								=			highway_exit[0]
                      snake_case								=			entropy(lowerCAmelCase    )
                      snake_case								=			highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                      snake_case								=			all_highway_exits + (highway_exit,)

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

        # Add last layer
          if self.output_hidden_states:
                snake_case								=			all_hidden_states + (hidden_states,)

          snake_case								=			(hidden_states,)
          if self.output_hidden_states:
                snake_case								=			outputs + (all_hidden_states,)
          if self.output_attentions:
                snake_case								=			outputs + (all_attentions,)

          snake_case								=			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   lowerCAmelCase_    (			__UpperCAmelCase					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase    ):






          """simple docstring"""

          super().__init__(lowerCAmelCase    )
          snake_case								=			config

          snake_case								=			BertEmbeddings(lowerCAmelCase    )
          snake_case								=			DeeBertEncoder(lowerCAmelCase    )
          snake_case								=			BertPooler(lowerCAmelCase    )

          self.init_weights()

    def 		snake_case (				self    ):






          """simple docstring"""

          self.encoder.init_highway_pooler(self.pooler    )

    def 		snake_case (				self    ):






          """simple docstring"""

          return self.embeddings.word_embeddings

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			value

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """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 		snake_case (				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    ):






          """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:
                snake_case								=			input_ids.size()
          elif inputs_embeds is not None:
                snake_case								=			inputs_embeds.size()[:-1]
          else:
                raise ValueError('You have to specify either input_ids or inputs_embeds'    )

          snake_case								=			input_ids.device if input_ids is not None else inputs_embeds.device

          if attention_mask is None:
                snake_case								=			torch.ones(lowerCAmelCase		,    device=lowerCAmelCase    )
          if encoder_attention_mask is None:
                snake_case								=			torch.ones(lowerCAmelCase		,    device=lowerCAmelCase    )
          if token_type_ids is None:
                snake_case								=			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.
          snake_case								=			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:
                snake_case								=			encoder_attention_mask[:, None, :, :]
          if encoder_attention_mask.dim() == 2:
                snake_case								=			encoder_attention_mask[:, None, None, :]

          snake_case								=			encoder_extended_attention_mask.to(
              dtype=next(self.parameters()    ).dtype    )  # fp16 compatibility
          snake_case								=			(1.0 - encoder_extended_attention_mask) * -1_00_00.0

          # 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]
          snake_case								=			self.get_head_mask(lowerCAmelCase		,    self.config.num_hidden_layers    )

          snake_case								=			self.embeddings(
              input_ids=lowerCAmelCase		,    position_ids=lowerCAmelCase		,    token_type_ids=lowerCAmelCase		,    inputs_embeds=lowerCAmelCase    )
          snake_case								=			self.encoder(
              lowerCAmelCase		,    attention_mask=lowerCAmelCase		,    head_mask=lowerCAmelCase		,    encoder_hidden_states=lowerCAmelCase		,    encoder_attention_mask=lowerCAmelCase		,    )
          snake_case								=			encoder_outputs[0]
          snake_case								=			self.pooler(lowerCAmelCase    )

          snake_case								=			(
              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   lowerCAmelCase_    (			__UpperCAmelCase					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			message
          snake_case								=			exit_layer  # start from 1!





class   lowerCAmelCase_    (			nn.Module					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase    ):






          """simple docstring"""

          super().__init__()
          snake_case								=			BertPooler(lowerCAmelCase    )
          snake_case								=			nn.Dropout(config.hidden_dropout_prob    )
          snake_case								=			nn.Linear(config.hidden_size		,    config.num_labels    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			encoder_outputs[0]
          snake_case								=			self.pooler(lowerCAmelCase    )
          # "return" pooler_output

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

          # Dropout and classification
          snake_case								=			bmodel_output[1]

          snake_case								=			self.dropout(lowerCAmelCase    )
          snake_case								=			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. """     ,    __UpperCAmelCase     ,    )
class   lowerCAmelCase_    (			__UpperCAmelCase					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase    ):






          """simple docstring"""

          super().__init__(lowerCAmelCase    )
          snake_case								=			config.num_labels
          snake_case								=			config.num_hidden_layers

          snake_case								=			DeeBertModel(lowerCAmelCase    )
          snake_case								=			nn.Dropout(config.hidden_dropout_prob    )
          snake_case								=			nn.Linear(config.hidden_size		,    self.config.num_labels    )

          self.init_weights()

    @add_start_docstrings_to_model_forward(lowerCAmelCase    )
    def 		snake_case (				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=-1		,    lowerCAmelCase=False		,    ):






          """simple docstring"""

          snake_case								=			self.num_layers
          try:
                snake_case								=			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

                snake_case								=			outputs[1]

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

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

                # work with highway exits
                snake_case								=			[]
                for highway_exit in outputs[-1]:
                      snake_case								=			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
                            snake_case								=			MSELoss()
                            snake_case								=			loss_fct(highway_logits.view(-1    )		,    labels.view(-1    )    )
                      else:
                            snake_case								=			CrossEntropyLoss()
                            snake_case								=			loss_fct(highway_logits.view(-1		,    self.num_labels    )		,    labels.view(-1    )    )
                      highway_losses.append(lowerCAmelCase    )

                if train_highway:
                      snake_case								=			(sum(highway_losses[:-1]    ),) + outputs
                      # exclude the final highway, of course
                else:
                      snake_case								=			(loss,) + outputs
          if not self.training:
                snake_case								=			outputs + ((original_entropy, highway_entropy), exit_layer)
                if output_layer >= 0:
                      snake_case								=			(
                          (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"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""roc_bert"""

				def __init__(				self		,    lowerCAmelCase=3_05_22		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-12		,    lowerCAmelCase=True		,    lowerCAmelCase=0		,    lowerCAmelCase="absolute"		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=7_68		,    lowerCAmelCase=9_10		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2_48_58		,    lowerCAmelCase=True		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			vocab_size
										snake_case								=			max_position_embeddings
										snake_case								=			hidden_size
										snake_case								=			num_hidden_layers
										snake_case								=			num_attention_heads
										snake_case								=			intermediate_size
										snake_case								=			hidden_act
										snake_case								=			hidden_dropout_prob
										snake_case								=			attention_probs_dropout_prob
										snake_case								=			initializer_range
										snake_case								=			type_vocab_size
										snake_case								=			layer_norm_eps
										snake_case								=			use_cache
										snake_case								=			enable_pronunciation
										snake_case								=			enable_shape
										snake_case								=			pronunciation_embed_dim
										snake_case								=			pronunciation_vocab_size
										snake_case								=			shape_embed_dim
										snake_case								=			shape_vocab_size
										snake_case								=			concat_input
										snake_case								=			position_embedding_type
										snake_case								=			classifier_dropout
										super().__init__(pad_token_id=lowerCAmelCase		,    **lowerCAmelCase    )


