'''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  _UpperCamelCase (     _a	:	List[Any]			):




    """simple docstring"""



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






class 			__lowercase		(						nn.Module						):







     """simple docstring"""






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

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







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







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

             __UpperCamelCase	:	List[Any]		    =  layer_module(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			head_mask[i]     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             __UpperCamelCase	:	Dict		    =  layer_outputs[0]

             if self.output_attentions:
                 __UpperCamelCase	:	List[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	:	Tuple		    =  current_outputs + (all_attentions,)

             __UpperCamelCase	:	str		    =  self.highway[i](_lowerCamelCase    )
             # logits, pooled_output

             if not self.training:
                 __UpperCamelCase	:	Any		    =  highway_exit[0]
                 __UpperCamelCase	:	int		    =  entropy(_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                 __UpperCamelCase	:	List[Any]		    =  all_highway_exits + (highway_exit,)

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

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

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

         __UpperCamelCase	:	List[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). '''			,							_lowerCamelCase			,							)
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase    ):
         super().__init__(_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  config

         __UpperCamelCase	:	Optional[int]		    =  BertEmbeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  DeeBertEncoder(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  BertPooler(_lowerCamelCase    )

         self.init_weights()







     def lowerCAmelCase     (		self    ):
         self.encoder.init_highway_pooler(self.pooler    )







     def lowerCAmelCase     (		self    ):
         return self.embeddings.word_embeddings







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Tuple		    =  value







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







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

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

         if attention_mask is None:
             __UpperCamelCase	:	Dict		    =  torch.ones(_lowerCamelCase     ,			device=_lowerCamelCase    )
         if encoder_attention_mask is None:
             __UpperCamelCase	:	Optional[Any]		    =  torch.ones(_lowerCamelCase     ,			device=_lowerCamelCase    )
         if token_type_ids is None:
             __UpperCamelCase	:	Tuple		    =  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.
         __UpperCamelCase	:	torch.Tensor		    =  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:
             __UpperCamelCase	:	List[str]		    =  encoder_attention_mask[:, None, :, :]
         if encoder_attention_mask.dim() == 2:
             __UpperCamelCase	:	Union[str, Any]		    =  encoder_attention_mask[:, None, None, :]

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

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

         __UpperCamelCase	:	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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  message
         __UpperCamelCase	:	List[Any]		    =  exit_layer  # start from 1!






class 			__lowercase		(						nn.Module						):







     """simple docstring"""






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







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

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

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

         __UpperCamelCase	:	List[str]		    =  self.dropout(_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  self.classifier(_lowerCamelCase    )

         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. '''			,							_lowerCamelCase			,							)
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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

         __UpperCamelCase	:	Optional[int]		    =  DeeBertModel(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  nn.Dropout(config.hidden_dropout_prob    )
         __UpperCamelCase	:	List[Any]		    =  nn.Linear(config.hidden_size     ,			self.config.num_labels    )

         self.init_weights()







     @add_start_docstrings_to_model_forward(_lowerCamelCase    )
     def lowerCAmelCase     (		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=-1     ,			_lowerCamelCase=False     ,			):
         __UpperCamelCase	:	List[str]		    =  self.num_layers
         try:
             __UpperCamelCase	:	Tuple		    =  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

             __UpperCamelCase	:	Dict		    =  outputs[1]

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

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

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

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

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
  import bsa
  from bsa import BeautifulSoup


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			**_lowerCamelCase    ):
         requires_backends(self     ,			['bs4']    )
         super().__init__(**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  []
         __UpperCamelCase	:	Dict		    =  []
         __UpperCamelCase	:	Union[str, Any]		    =  element if element.name else element.parent
         for parent in child.parents:  # type: bs4.element.Tag
             __UpperCamelCase	:	str		    =  parent.find_all(child.name     ,			recursive=_lowerCamelCase    )
             xpath_tags.append(child.name    )
             xpath_subscripts.append(
                 0 if 1 == len(_lowerCamelCase    ) else next(i for i, s in enumerate(_lowerCamelCase     ,			1    ) if s is child    )    )
             __UpperCamelCase	:	Union[str, Any]		    =  parent
         xpath_tags.reverse()
         xpath_subscripts.reverse()
         return xpath_tags, xpath_subscripts







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  BeautifulSoup(_lowerCamelCase     ,			'html.parser'    )

         __UpperCamelCase	:	int		    =  []
         __UpperCamelCase	:	List[Any]		    =  []
         __UpperCamelCase	:	int		    =  []

         for element in html_code.descendants:
             if type(_lowerCamelCase    ) == bsa.element.NavigableString:
                 if type(element.parent    ) != bsa.element.Tag:
                     continue

                 __UpperCamelCase	:	Tuple		    =  html.unescape(_lowerCamelCase    ).strip()
                 if not text_in_this_tag:
                     continue

                 all_doc_strings.append(_lowerCamelCase    )

                 __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.xpath_soup(_lowerCamelCase    )
                 stringaxtag_seq.append(_lowerCamelCase    )
                 stringaxsubs_seq.append(_lowerCamelCase    )

         if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
             raise ValueError('Number of doc strings and xtags does not correspond'    )
         if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
             raise ValueError('Number of doc strings and xsubs does not correspond'    )

         return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  ''
         for tagname, subs in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
             xpath += f"""/{tagname}"""
             if subs != 0:
                 xpath += f"""[{subs}]"""
         return xpath







     def __call__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  False

         # Check that strings has a valid type
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	List[str]		    =  True
         elif isinstance(_lowerCamelCase     ,			(list, tuple)    ):
             if len(_lowerCamelCase    ) == 0 or isinstance(html_strings[0]     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[int]		    =  True

         if not valid_strings:
             raise ValueError(
                 'HTML strings must of type `str`, `List[str]` (batch of examples), '
                 f"""but is of type {type(_lowerCamelCase    )}."""    )

         __UpperCamelCase	:	Dict		    =  bool(isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(html_strings[0]     ,			_lowerCamelCase    ))    )

         if not is_batched:
             __UpperCamelCase	:	Tuple		    =  [html_strings]

         # Get nodes + xpaths
         __UpperCamelCase	:	Tuple		    =  []
         __UpperCamelCase	:	Tuple		    =  []
         for html_string in html_strings:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.get_three_from_single(_lowerCamelCase    )
             nodes.append(_lowerCamelCase    )
             __UpperCamelCase	:	Union[str, Any]		    =  []
             for node, tag_list, sub_list in zip(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[Any]		    =  self.construct_xpath(_lowerCamelCase     ,			_lowerCamelCase    )
                 xpath_strings.append(_lowerCamelCase    )
             xpaths.append(_lowerCamelCase    )

         # return as Dict
         __UpperCamelCase	:	Optional[int]		    =  {'nodes': nodes, 'xpaths': xpaths}
         __UpperCamelCase	:	Union[str, Any]		    =  BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )

         return encoded_inputs



