



'''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   a_   (		_UpperCAmelCase    :      str				)    ->  Dict:
      __snake_case :   List[str]													=    torch.exp(_UpperCAmelCase				)
      __snake_case :   Any													=    torch.sum(_UpperCAmelCase ,dim=1				)  # sum of exp(x_i)
      __snake_case :   List[str]													=    torch.sum(x * exp_x ,dim=1				)  # sum of x_i * exp(x_i)
      return torch.log(_UpperCAmelCase				) - B / A



class  snake_case__						(    nn.Module				):
 def __init__(   self						:							Optional[int]      ,     __a						:							str  )      ->   Tuple:







       '''simple docstring'''
       super().__init__()
       __snake_case :   Any													=    config.output_attentions
       __snake_case :   int													=    config.output_hidden_states
       __snake_case :   Union[str, Any]													=    nn.ModuleList([BertLayer(__a  ) for _ in range(config.num_hidden_layers  )]  )
       __snake_case :   Dict													=    nn.ModuleList([BertHighway(__a  ) for _ in range(config.num_hidden_layers  )]  )

       __snake_case :   Optional[int]													=    [-1 for _ in range(config.num_hidden_layers  )]
 def A_   (   self						:							Optional[int]      ,     __a						:							Any  )      ->   Optional[int]:







       '''simple docstring'''
       if (type(__a  ) is float) or (type(__a  ) is int):
             for i in range(len(self.early_exit_entropy  )  ):
                   __snake_case :   Dict													=    x
       else:
             __snake_case :   List[Any]													=    x
 def A_   (   self						:							Union[str, Any]      ,     __a						:							str  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    pooler.state_dict()
       for highway in self.highway:
             for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name]  )





 def A_   (   self						:							Any      ,     __a						:							List[str]      ,     __a						:							Optional[int]=None      ,     __a						:							List[Any]=None      ,     __a						:							Any=None      ,     __a						:							List[Any]=None      ,     )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    ()
       __snake_case :   Any													=    ()
       __snake_case :   Union[str, Any]													=    ()
       for i, layer_module in enumerate(self.layer  ):
             if self.output_hidden_states:
                   __snake_case :   Tuple													=    all_hidden_states + (hidden_states,)

             __snake_case :   Dict													=    layer_module(
                 __a      ,     __a      ,     head_mask[i]      ,     __a      ,     __a  )
             __snake_case :   List[str]													=    layer_outputs[0]

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

             __snake_case :   Optional[Any]													=    (hidden_states,)
             if self.output_hidden_states:
                   __snake_case :   Tuple													=    current_outputs + (all_hidden_states,)
             if self.output_attentions:
                   __snake_case :   str													=    current_outputs + (all_attentions,)

             __snake_case :   Union[str, Any]													=    self.highway[i](__a  )
             # logits, pooled_output

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

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

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

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

       __snake_case :   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). '''  ,     SCREAMING_SNAKE_CASE_  ,     )
class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							List[str]      ,     __a						:							Any  )      ->   int:







       '''simple docstring'''
       super().__init__(__a  )
       __snake_case :   Tuple													=    config

       __snake_case :   Dict													=    BertEmbeddings(__a  )
       __snake_case :   List[Any]													=    DeeBertEncoder(__a  )
       __snake_case :   Dict													=    BertPooler(__a  )

       self.init_weights()
 def A_   (   self						:							Any  )      ->   Union[str, Any]:







       '''simple docstring'''
       self.encoder.init_highway_pooler(self.pooler  )
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       return self.embeddings.word_embeddings
 def A_   (   self						:							int      ,     __a						:							Any  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Dict													=    value
 def A_   (   self						:							Any      ,     __a						:							Any  )      ->   Union[str, Any]:







       '''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 A_   (   self						:							Optional[Any]      ,     __a						:							Tuple=None      ,     __a						:							Union[str, Any]=None      ,     __a						:							Optional[Any]=None      ,     __a						:							Union[str, Any]=None      ,     __a						:							List[str]=None      ,     __a						:							Dict=None      ,     __a						:							Optional[int]=None      ,     __a						:							Any=None      ,     )      ->   Optional[int]:







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

       __snake_case :   Dict													=    input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
             __snake_case :   Dict													=    torch.ones(__a      ,     device=__a  )
       if encoder_attention_mask is None:
             __snake_case :   str													=    torch.ones(__a      ,     device=__a  )
       if token_type_ids is None:
             __snake_case :   Union[str, Any]													=    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.
       __snake_case :   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:
             __snake_case :   Optional[int]													=    encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
             __snake_case :   Tuple													=    encoder_attention_mask[:, None, None, :]

       __snake_case :   Dict													=    encoder_extended_attention_mask.to(
           dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
       __snake_case :   Dict													=    (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]
       __snake_case :   Union[str, Any]													=    self.get_head_mask(__a      ,     self.config.num_hidden_layers  )

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

       __snake_case :   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  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							List[str]      ,     __a						:							Optional[int]      ,     __a						:							Any  )      ->   int:







       '''simple docstring'''
       __snake_case :   List[Any]													=    message
       __snake_case :   int													=    exit_layer  # start from 1!



class  snake_case__						(    nn.Module				):
 def __init__(   self						:							Dict      ,     __a						:							Any  )      ->   Any:







       '''simple docstring'''
       super().__init__()
       __snake_case :   List[Any]													=    BertPooler(__a  )
       __snake_case :   Union[str, Any]													=    nn.Dropout(config.hidden_dropout_prob  )
       __snake_case :   int													=    nn.Linear(config.hidden_size      ,     config.num_labels  )





 def A_   (   self						:							str      ,     __a						:							int  )      ->   List[str]:







       '''simple docstring'''
       # Pooler
       __snake_case :   List[Any]													=    encoder_outputs[0]
       __snake_case :   Optional[int]													=    self.pooler(__a  )
       # "return" pooler_output

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

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

       __snake_case :   List[Any]													=    self.dropout(__a  )
       __snake_case :   Optional[int]													=    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. '''  ,     SCREAMING_SNAKE_CASE_  ,     )
class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							Any      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       super().__init__(__a  )
       __snake_case :   Tuple													=    config.num_labels
       __snake_case :   Tuple													=    config.num_hidden_layers

       __snake_case :   List[str]													=    DeeBertModel(__a  )
       __snake_case :   Any													=    nn.Dropout(config.hidden_dropout_prob  )
       __snake_case :   Dict													=    nn.Linear(config.hidden_size      ,     self.config.num_labels  )

       self.init_weights()





 @add_start_docstrings_to_model_forward(__a  )
 def A_   (   self						:							int      ,     __a						:							Tuple=None      ,     __a						:							int=None      ,     __a						:							Union[str, Any]=None      ,     __a						:							Tuple=None      ,     __a						:							List[str]=None      ,     __a						:							Optional[int]=None      ,     __a						:							int=None      ,     __a						:							List[Any]=-1      ,     __a						:							str=False      ,     )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Any													=    self.num_layers
       try:
             __snake_case :   Optional[int]													=    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

             __snake_case :   Any													=    outputs[1]

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

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

             # work with highway exits
             __snake_case :   Tuple													=    []
             for highway_exit in outputs[-1]:
                   __snake_case :   Optional[int]													=    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
                         __snake_case :   Any													=    MSELoss()
                         __snake_case :   List[str]													=    loss_fct(highway_logits.view(-1  )      ,     labels.view(-1  )  )
                   else:
                         __snake_case :   Optional[int]													=    CrossEntropyLoss()
                         __snake_case :   Dict													=    loss_fct(highway_logits.view(-1      ,     self.num_labels  )      ,     labels.view(-1  )  )
                   highway_losses.append(__a  )

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





import functools





def   a_   (		_UpperCAmelCase    :      list[int] ,_UpperCAmelCase    :      list[int]				)    ->  int:

      # Validation
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for day in days				):
            raise ValueError('The parameter days should be a list of integers'				)

      if len(_UpperCAmelCase				) != 3 or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for cost in costs				):
            raise ValueError('The parameter costs should be a list of three integers'				)

      if len(_UpperCAmelCase				) == 0:
            return 0

      if min(_UpperCAmelCase				) <= 0:
            raise ValueError('All days elements should be greater than 0'				)

      if max(_UpperCAmelCase				) >= 3_66:
            raise ValueError('All days elements should be less than 366'				)

      __snake_case :   str													=    set(_UpperCAmelCase				)

      @functools.cache
      def dynamic_programming(_UpperCAmelCase    :      int				) -> int:
            if index > 3_65:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1				)

            return min(
                costs[0] + dynamic_programming(index + 1				) ,costs[1] + dynamic_programming(index + 7				) ,costs[2] + dynamic_programming(index + 30				) ,)

      return dynamic_programming(1				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



