




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   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


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

class  _lowerCamelCase						(			nn.Module   ):

      """simple docstring"""

      def __init__(     self					: int ,       snake_case					: Union[str, Any]				):
          super().__init__()
          __UpperCamelCase  		=						config.output_attentions
          __UpperCamelCase  		=						config.output_hidden_states
          __UpperCamelCase  		=						nn.ModuleList([BertLayer(snake_case				) for _ in range(config.num_hidden_layers				)]				)
          __UpperCamelCase  		=						nn.ModuleList([BertHighway(snake_case				) for _ in range(config.num_hidden_layers				)]				)

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







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[str]				):
          if (type(snake_case				) is float) or (type(snake_case				) is int):
              for i in range(len(self.early_exit_entropy				)				):
                  __UpperCamelCase  		=						x
          else:
              __UpperCamelCase  		=						x







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

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

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

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

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

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

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

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

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

          __UpperCamelCase  		=						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). "							,			_SCREAMING_SNAKE_CASE							,			)
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: str ,       snake_case					: int				):
          super().__init__(snake_case				)
          __UpperCamelCase  		=						config

          __UpperCamelCase  		=						BertEmbeddings(snake_case				)
          __UpperCamelCase  		=						DeeBertEncoder(snake_case				)
          __UpperCamelCase  		=						BertPooler(snake_case				)

          self.init_weights()







      def       snake_case	(     self					: Any				):
          self.encoder.init_highway_pooler(self.pooler				)







      def       snake_case	(     self					: Optional[Any]				):
          return self.embeddings.word_embeddings







      def       snake_case	(     self					: Tuple ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						value







      def       snake_case	(     self					: str ,       snake_case					: Any				):
          for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(snake_case				)







      @add_start_docstrings_to_model_forward(snake_case				)
      def       snake_case	(     self					: Dict ,       snake_case					: List[Any]=None ,       snake_case					: Optional[Any]=None ,       snake_case					: List[Any]=None ,       snake_case					: str=None ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       snake_case					: Tuple=None ,       snake_case					: Dict=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:
              __UpperCamelCase  		=						input_ids.size()
          elif inputs_embeds is not None:
              __UpperCamelCase  		=						inputs_embeds.size()[:-1]
          else:
              raise ValueError('''You have to specify either input_ids or inputs_embeds'''				)

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

          if attention_mask is None:
              __UpperCamelCase  		=						torch.ones(snake_case ,       device=snake_case				)
          if encoder_attention_mask is None:
              __UpperCamelCase  		=						torch.ones(snake_case ,       device=snake_case				)
          if token_type_ids is None:
              __UpperCamelCase  		=						torch.zeros(snake_case ,       dtype=torch.long ,       device=snake_case				)

          # 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.
          __UpperCamelCase  		=						self.get_extended_attention_mask(snake_case ,       snake_case ,       snake_case				)

          # 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:
              __UpperCamelCase  		=						encoder_attention_mask[:, None, :, :]
          if encoder_attention_mask.dim() == 2:
              __UpperCamelCase  		=						encoder_attention_mask[:, None, None, :]

          __UpperCamelCase  		=						encoder_extended_attention_mask.to(
              dtype=next(self.parameters()				).dtype				)  # fp16 compatibility
          __UpperCamelCase  		=						(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]
          __UpperCamelCase  		=						self.get_head_mask(snake_case ,       self.config.num_hidden_layers				)

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

          __UpperCamelCase  		=						(
              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						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: str ,       snake_case					: Dict				):
          __UpperCamelCase  		=						message
          __UpperCamelCase  		=						exit_layer  # start from 1!







class  _lowerCamelCase						(			nn.Module   ):

      """simple docstring"""

      def __init__(     self					: Dict ,       snake_case					: List[Any]				):
          super().__init__()
          __UpperCamelCase  		=						BertPooler(snake_case				)
          __UpperCamelCase  		=						nn.Dropout(config.hidden_dropout_prob				)
          __UpperCamelCase  		=						nn.Linear(config.hidden_size ,       config.num_labels				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: List[Any]				):
          # Pooler
          __UpperCamelCase  		=						encoder_outputs[0]
          __UpperCamelCase  		=						self.pooler(snake_case				)
          # "return" pooler_output

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

          # Dropout and classification
          __UpperCamelCase  		=						bmodel_output[1]

          __UpperCamelCase  		=						self.dropout(snake_case				)
          __UpperCamelCase  		=						self.classifier(snake_case				)

          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. "							,			_SCREAMING_SNAKE_CASE							,			)
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Dict ,       snake_case					: int				):
          super().__init__(snake_case				)
          __UpperCamelCase  		=						config.num_labels
          __UpperCamelCase  		=						config.num_hidden_layers

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

          self.init_weights()







      @add_start_docstrings_to_model_forward(snake_case				)
      def       snake_case	(     self					: List[str] ,       snake_case					: Union[str, Any]=None ,       snake_case					: Optional[int]=None ,       snake_case					: str=None ,       snake_case					: Optional[Any]=None ,       snake_case					: Union[str, Any]=None ,       snake_case					: str=None ,       snake_case					: str=None ,       snake_case					: Optional[Any]=-1 ,       snake_case					: List[str]=False ,       ):
          __UpperCamelCase  		=						self.num_layers
          try:
              __UpperCamelCase  		=						self.bert(
                  snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       position_ids=snake_case ,       head_mask=snake_case ,       inputs_embeds=snake_case ,       )
              # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

              __UpperCamelCase  		=						outputs[1]

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

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

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

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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Union[str, Any]    =					"rwkv"
      lowerCAmelCase__			: Union[str, Any]    =					{"max_position_embeddings": "context_length"}







      def __init__(     self					: Optional[int] ,       snake_case					: Optional[Any]=50277 ,       snake_case					: str=1024 ,       snake_case					: str=4096 ,       snake_case					: Optional[Any]=32 ,       snake_case					: Union[str, Any]=None ,       snake_case					: Optional[Any]=None ,       snake_case					: Optional[Any]=1E-5 ,       snake_case					: List[str]=0 ,       snake_case					: Optional[Any]=0 ,       snake_case					: Union[str, Any]=6 ,       snake_case					: Tuple=False ,       snake_case					: Any=True ,       **snake_case					: List[str] ,       ):
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						context_length
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						attention_hidden_size if attention_hidden_size is not None else hidden_size
          __UpperCamelCase  		=						intermediate_size if intermediate_size is not None else 4 * hidden_size
          __UpperCamelCase  		=						layer_norm_epsilon
          __UpperCamelCase  		=						rescale_every
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id

          super().__init__(
              tie_word_embeddings=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       **snake_case				)






