






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__      (    __lowerCamelCase			:     int		):
  __UpperCAmelCase							:	Dict           =  torch.exp(__lowerCamelCase		)
  __UpperCAmelCase							:	List[Any]           =  torch.sum(__lowerCamelCase  ,		dim=1		)  # sum of exp(x_i)
  __UpperCAmelCase							:	Optional[int]           =  torch.sum(x * exp_x  ,		dim=1		)  # sum of x_i * exp(x_i)
  return torch.log(__lowerCamelCase		) - B / A
class     a    ( nn.Module   ):



      """simple docstring"""


      def __init__(      self					:			List[str]		,      __lowercase					:			List[Any]					)							->      Tuple:
        super().__init__()
        __UpperCAmelCase							:	Optional[Any]           =  config.output_attentions
        __UpperCAmelCase							:	Optional[Any]           =  config.output_hidden_states
        __UpperCAmelCase							:	Dict           =  nn.ModuleList([BertLayer(__lowercase					) for _ in range(config.num_hidden_layers					)]					)
        __UpperCAmelCase							:	str           =  nn.ModuleList([BertHighway(__lowercase					) for _ in range(config.num_hidden_layers					)]					)

        __UpperCAmelCase							:	Optional[int]           =  [-1 for _ in range(config.num_hidden_layers					)]
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			Union[str, Any]					)							->      Optional[int]:
        if (type(__lowercase					) is float) or (type(__lowercase					) is int):
          for i in range(len(self.early_exit_entropy					)					):
            __UpperCAmelCase							:	Tuple           =  x
        else:
          __UpperCAmelCase							:	Union[str, Any]           =  x
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			Dict					)							->      Optional[Any]:
        __UpperCAmelCase							:	int           =  pooler.state_dict()
        for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
            param.copy_(loaded_model[name]					)




      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			Optional[int]		,      __lowercase					:			str=None		,      __lowercase					:			Any=None		,      __lowercase					:			str=None		,      __lowercase					:			List[Any]=None		,      )							->      Dict:
        __UpperCAmelCase							:	Dict           =  ()
        __UpperCAmelCase							:	str           =  ()
        __UpperCAmelCase							:	Any           =  ()
        for i, layer_module in enumerate(self.layer					):
          if self.output_hidden_states:
            __UpperCAmelCase							:	List[str]           =  all_hidden_states + (hidden_states,)

          __UpperCAmelCase							:	Optional[Any]           =  layer_module(
              __lowercase		,      __lowercase		,      head_mask[i]		,      __lowercase		,      __lowercase					)
          __UpperCAmelCase							:	List[str]           =  layer_outputs[0]

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

          __UpperCAmelCase							:	int           =  (hidden_states,)
          if self.output_hidden_states:
            __UpperCAmelCase							:	List[str]           =  current_outputs + (all_hidden_states,)
          if self.output_attentions:
            __UpperCAmelCase							:	Union[str, Any]           =  current_outputs + (all_attentions,)

          __UpperCAmelCase							:	List[Any]           =  self.highway[i](__lowercase					)
          # logits, pooled_output

          if not self.training:
            __UpperCAmelCase							:	str           =  highway_exit[0]
            __UpperCAmelCase							:	str           =  entropy(__lowercase					)
            __UpperCAmelCase							:	Optional[Any]           =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
            __UpperCAmelCase							:	Tuple           =  all_highway_exits + (highway_exit,)

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

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

        __UpperCAmelCase							:	List[Any]           =  (hidden_states,)
        if self.output_hidden_states:
          __UpperCAmelCase							:	str           =  outputs + (all_hidden_states,)
        if self.output_attentions:
          __UpperCAmelCase							:	Optional[Any]           =  outputs + (all_attentions,)

        __UpperCAmelCase							:	str           =  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). '							,       lowercase__							,       )
class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			List[Any]					)							->      str:
        super().__init__(__lowercase					)
        __UpperCAmelCase							:	Dict           =  config

        __UpperCAmelCase							:	Optional[int]           =  BertEmbeddings(__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  DeeBertEncoder(__lowercase					)
        __UpperCAmelCase							:	int           =  BertPooler(__lowercase					)

        self.init_weights()
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Union[str, Any]:
        self.encoder.init_highway_pooler(self.pooler					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Tuple:
        return self.embeddings.word_embeddings
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			str					)							->      str:
        __UpperCAmelCase							:	List[Any]           =  value
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Any					)							->      Optional[Any]:
        for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(__lowercase					)




      @add_start_docstrings_to_model_forward(__lowercase					)
      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Tuple=None		,      __lowercase					:			Dict=None		,      __lowercase					:			Union[str, Any]=None		,      __lowercase					:			Optional[Any]=None		,      __lowercase					:			str=None		,      __lowercase					:			Union[str, Any]=None		,      __lowercase					:			Dict=None		,      __lowercase					:			Optional[int]=None		,      )							->      List[Any]:
        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							:	Union[str, Any]           =  input_ids.size()
        elif inputs_embeds is not None:
          __UpperCAmelCase							:	List[str]           =  inputs_embeds.size()[:-1]
        else:
          raise ValueError("""You have to specify either input_ids or inputs_embeds"""					)

        __UpperCAmelCase							:	Union[str, Any]           =  input_ids.device if input_ids is not None else inputs_embeds.device

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

        # 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							:	torch.Tensor           =  self.get_extended_attention_mask(__lowercase		,      __lowercase		,      __lowercase					)

        # 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							:	Any           =  encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
          __UpperCAmelCase							:	Optional[Any]           =  encoder_attention_mask[:, None, None, :]

        __UpperCAmelCase							:	List[str]           =  encoder_extended_attention_mask.to(
            dtype=next(self.parameters()					).dtype					)  # fp16 compatibility
        __UpperCAmelCase							:	int           =  (1.0 - encoder_extended_attention_mask) * -10_000.0

        # 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							:	str           =  self.get_head_mask(__lowercase		,      self.config.num_hidden_layers					)

        __UpperCAmelCase							:	Tuple           =  self.embeddings(
            input_ids=__lowercase		,      position_ids=__lowercase		,      token_type_ids=__lowercase		,      inputs_embeds=__lowercase					)
        __UpperCAmelCase							:	Tuple           =  self.encoder(
            __lowercase		,      attention_mask=__lowercase		,      head_mask=__lowercase		,      encoder_hidden_states=__lowercase		,      encoder_attention_mask=__lowercase		,      )
        __UpperCAmelCase							:	List[Any]           =  encoder_outputs[0]
        __UpperCAmelCase							:	Optional[int]           =  self.pooler(__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  (
            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     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			str		,      __lowercase					:			int		,      __lowercase					:			List[str]					)							->      List[str]:
        __UpperCAmelCase							:	Any           =  message
        __UpperCAmelCase							:	Dict           =  exit_layer  # start from 1!


class     a    ( nn.Module   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			int					)							->      Union[str, Any]:
        super().__init__()
        __UpperCAmelCase							:	Optional[Any]           =  BertPooler(__lowercase					)
        __UpperCAmelCase							:	Union[str, Any]           =  nn.Dropout(config.hidden_dropout_prob					)
        __UpperCAmelCase							:	Optional[Any]           =  nn.Linear(config.hidden_size		,      config.num_labels					)




      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Optional[int]					)							->      Any:
        # Pooler
        __UpperCAmelCase							:	Union[str, Any]           =  encoder_outputs[0]
        __UpperCAmelCase							:	Optional[int]           =  self.pooler(__lowercase					)
        # "return" pooler_output

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

        # Dropout and classification
        __UpperCAmelCase							:	List[Any]           =  bmodel_output[1]

        __UpperCAmelCase							:	Tuple           =  self.dropout(__lowercase					)
        __UpperCAmelCase							:	Dict           =  self.classifier(__lowercase					)

        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. '							,       lowercase__							,       )
class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			Union[str, Any]		,      __lowercase					:			Optional[Any]					)							->      int:
        super().__init__(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  config.num_labels
        __UpperCAmelCase							:	Optional[Any]           =  config.num_hidden_layers

        __UpperCAmelCase							:	Optional[int]           =  DeeBertModel(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  nn.Dropout(config.hidden_dropout_prob					)
        __UpperCAmelCase							:	Union[str, Any]           =  nn.Linear(config.hidden_size		,      self.config.num_labels					)

        self.init_weights()




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

          __UpperCAmelCase							:	Optional[int]           =  outputs[1]

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

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

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

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

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


