






"""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       snake_case  (A_				:str				):







  '''simple docstring'''

  a					:      Optional[Any]    =       torch.exp(lowercase__				)
  a					:      Union[str, Any]    =       torch.sum(lowercase__     ,      dim=1				)  # sum of exp(x_i)
  a					:      Any    =       torch.sum(x * exp_x     ,      dim=1				)  # sum of x_i * exp(x_i)
  return torch.log(lowercase__				) - B / A






class 						snake_case  (  nn.Module	):





    def __init__(							self     : str    ,		A     : Union[str, Any]      ):


      '''simple docstring'''




      super().__init__()
      a					:      Union[str, Any]    =       config.output_attentions
      a					:      Dict    =       config.output_hidden_states
      a					:      Tuple    =       nn.ModuleList([BertLayer(A      ) for _ in range(config.num_hidden_layers      )]      )
      a					:      List[Any]    =       nn.ModuleList([BertHighway(A      ) for _ in range(config.num_hidden_layers      )]      )

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





    def     lowerCamelCase__				(							self     : Any    ,		A     : str      ):


      '''simple docstring'''




      if (type(A      ) is float) or (type(A      ) is int):
        for i in range(len(self.early_exit_entropy      )      ):
          a					:      Tuple    =       x
      else:
        a					:      List[str]    =       x





    def     lowerCamelCase__				(							self     : Any    ,		A     : Dict      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : Optional[int]    ,		A     : Union[str, Any]=None    ,		A     : Tuple=None    ,		A     : str=None    ,		A     : Optional[int]=None    ,		):


      '''simple docstring'''




      a					:      Optional[Any]    =       ()
      a					:      Dict    =       ()
      a					:      List[Any]    =       ()
      for i, layer_module in enumerate(self.layer      ):
        if self.output_hidden_states:
          a					:      str    =       all_hidden_states + (hidden_states,)

        a					:      Optional[int]    =       layer_module(
            A    ,		A    ,		head_mask[i]    ,		A    ,		A      )
        a					:      Optional[Any]    =       layer_outputs[0]

        if self.output_attentions:
          a					:      List[Any]    =       all_attentions + (layer_outputs[1],)

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

        a					:      Any    =       self.highway[i](A      )
        # logits, pooled_output

        if not self.training:
          a					:      str    =       highway_exit[0]
          a					:      Tuple    =       entropy(A      )
          a					:      List[Any]    =       highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
          a					:      Union[str, Any]    =       all_highway_exits + (highway_exit,)

          if highway_entropy < self.early_exit_entropy[i]:
            a					:      List[str]    =       (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
            raise HighwayException(A    ,		i + 1      )
        else:
          a					:      str    =       all_highway_exits + (highway_exit,)

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

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

      a					:      Optional[Any]    =       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 						snake_case  (  __UpperCamelCase	):





    def __init__(							self     : List[str]    ,		A     : Dict      ):


      '''simple docstring'''




      super().__init__(A      )
      a					:      Dict    =       config

      a					:      Dict    =       BertEmbeddings(A      )
      a					:      List[str]    =       DeeBertEncoder(A      )
      a					:      Tuple    =       BertPooler(A      )

      self.init_weights()





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      self.encoder.init_highway_pooler(self.pooler      )





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      return self.embeddings.word_embeddings





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       value





    def     lowerCamelCase__				(							self     : int    ,		A     : str      ):


      '''simple docstring'''




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





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

      a					:      List[str]    =       input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
        a					:      List[Any]    =       torch.ones(A    ,		device=A      )
      if encoder_attention_mask is None:
        a					:      Any    =       torch.ones(A    ,		device=A      )
      if token_type_ids is None:
        a					:      List[str]    =       torch.zeros(A    ,		dtype=torch.long    ,		device=A      )

      # 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.
      a					:      torch.Tensor    =       self.get_extended_attention_mask(A    ,		A    ,		A      )

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

      a					:      List[Any]    =       encoder_extended_attention_mask.to(
          dtype=next(self.parameters()      ).dtype      )  # fp16 compatibility
      a					:      str    =       (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

      # 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]
      a					:      Optional[Any]    =       self.get_head_mask(A    ,		self.config.num_hidden_layers      )

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

      a					:      List[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 						snake_case  (  __UpperCamelCase	):





    def __init__(							self     : Optional[int]    ,		A     : List[Any]    ,		A     : Any      ):


      '''simple docstring'''




      a					:      str    =       message
      a					:      int    =       exit_layer  # start from 1!






class 						snake_case  (  nn.Module	):





    def __init__(							self     : Any    ,		A     : Optional[Any]      ):


      '''simple docstring'''




      super().__init__()
      a					:      Optional[int]    =       BertPooler(A      )
      a					:      Optional[Any]    =       nn.Dropout(config.hidden_dropout_prob      )
      a					:      Optional[int]    =       nn.Linear(config.hidden_size    ,		config.num_labels      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : List[Any]      ):


      '''simple docstring'''




      a					:      List[str]    =       encoder_outputs[0]
      a					:      Dict    =       self.pooler(A      )
      # "return" pooler_output

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

      # Dropout and classification
      a					:      Tuple    =       bmodel_output[1]

      a					:      List[str]    =       self.dropout(A      )
      a					:      List[Any]    =       self.classifier(A      )

      return logits, pooled_output






@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''       ,			__UpperCamelCase       ,			)
class 						snake_case  (  __UpperCamelCase	):





    def __init__(							self     : int    ,		A     : List[str]      ):


      '''simple docstring'''




      super().__init__(A      )
      a					:      Tuple    =       config.num_labels
      a					:      int    =       config.num_hidden_layers

      a					:      Union[str, Any]    =       DeeBertModel(A      )
      a					:      Any    =       nn.Dropout(config.hidden_dropout_prob      )
      a					:      str    =       nn.Linear(config.hidden_size    ,		self.config.num_labels      )

      self.init_weights()





    @add_start_docstrings_to_model_forward(A      )
    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : Optional[int]=None    ,		A     : Any=None    ,		A     : Dict=None    ,		A     : Tuple=None    ,		A     : str=None    ,		A     : Union[str, Any]=None    ,		A     : Optional[Any]=None    ,		A     : Tuple=-1    ,		A     : str=False    ,		):


      '''simple docstring'''




      a					:      List[str]    =       self.num_layers
      try:
        a					:      Any    =       self.bert(
            A    ,		attention_mask=A    ,		token_type_ids=A    ,		position_ids=A    ,		head_mask=A    ,		inputs_embeds=A    ,		)
        # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

        a					:      Optional[Any]    =       outputs[1]

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

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

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

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












"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  return math.sqrt(sum(pow(a - b     ,      2				) for a, b in zip(A_     ,      A_				)				)				)




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  if dataset.ndim != value_array.ndim:
    a					:      Optional[Any]    =       (
        'Wrong input data\'s dimensions... '
        f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
    )
    raise ValueError(A_				)

  try:
    if dataset.shape[1] != value_array.shape[1]:
      a					:      Optional[int]    =       (
          'Wrong input data\'s shape... '
          f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
      )
      raise ValueError(A_				)
  except IndexError:
    if dataset.ndim != value_array.ndim:
      raise TypeError('Wrong shape'				)

  if dataset.dtype != value_array.dtype:
    a					:      Optional[Any]    =       (
        'Input data have different datatype... '
        f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
    )
    raise TypeError(A_				)

  a					:      Tuple    =       []

  for value in value_array:
    a					:      List[Any]    =       euclidean(A_     ,      dataset[0]				)
    a					:      int    =       dataset[0].tolist()

    for dataset_value in dataset[1:]:
      a					:      Optional[int]    =       euclidean(A_     ,      A_				)

      if dist > temp_dist:
        a					:      List[str]    =       temp_dist
        a					:      int    =       dataset_value.tolist()

    answer.append([vector, dist]				)

  return answer




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  return np.dot(A_     ,      A_				) / (norm(A_				) * norm(A_				))


if __name__ == "__main__":
   import doctest

   doctest.testmod()




