



"""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 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[int]     ):


						'''simple docstring'''






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



class  _lowerCAmelCase     (					nn.Module		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase )			->    int:







								'''simple docstring'''

								super().__init__()
								snake_case_ :			Tuple            =      config.output_attentions
								snake_case_ :			str            =      config.output_hidden_states
								snake_case_ :			List[str]            =      nn.ModuleList([BertLayer(_lowercase ) for _ in range(config.num_hidden_layers )] )
								snake_case_ :			Tuple            =      nn.ModuleList([BertHighway(_lowercase ) for _ in range(config.num_hidden_layers )] )

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





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Tuple:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			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				,     _lowercase				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      ()
								snake_case_ :			str            =      ()
								snake_case_ :			List[str]            =      ()
								for i, layer_module in enumerate(self.layer ):
														if self.output_hidden_states:
																				snake_case_ :			int            =      all_hidden_states + (hidden_states,)

														snake_case_ :			Any            =      layer_module(
														    _lowercase				,     _lowercase				,     head_mask[i]				,     _lowercase				,     _lowercase )
														snake_case_ :			Dict            =      layer_outputs[0]

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

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

														snake_case_ :			Optional[Any]            =      self.highway[i](_lowercase )
														# logits, pooled_output

														if not self.training:
																				snake_case_ :			Tuple            =      highway_exit[0]
																				snake_case_ :			List[str]            =      entropy(_lowercase )
																				snake_case_ :			str            =      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																				snake_case_ :			Union[str, Any]            =      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(_lowercase				,     i + 1 )
														else:
																				snake_case_ :			Dict            =      all_highway_exits + (highway_exit,)

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

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

								snake_case_ :			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). '''	,					SCREAMING_SNAKE_CASE__	,					)
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								super().__init__(_lowercase )
								snake_case_ :			Union[str, Any]            =      config

								snake_case_ :			int            =      BertEmbeddings(_lowercase )
								snake_case_ :			Tuple            =      DeeBertEncoder(_lowercase )
								snake_case_ :			int            =      BertPooler(_lowercase )

								self.init_weights()





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								self.encoder.init_highway_pooler(self.pooler )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								return self.embeddings.word_embeddings





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Dict            =      value





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    int:







								'''simple docstring'''

								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				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     )			->    Optional[Any]:







								'''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_ :			Optional[int]            =      inputs_embeds.size()[:-1]
								else:
														raise ValueError("""You have to specify either input_ids or inputs_embeds""" )

								snake_case_ :			int            =      input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
														snake_case_ :			Dict            =      torch.ones(_lowercase				,     device=_lowercase )
								if encoder_attention_mask is None:
														snake_case_ :			Tuple            =      torch.ones(_lowercase				,     device=_lowercase )
								if token_type_ids is None:
														snake_case_ :			Any            =      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.
								snake_case_ :			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:
														snake_case_ :			List[str]            =      encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
														snake_case_ :			Any            =      encoder_attention_mask[:, None, None, :]

								snake_case_ :			List[str]            =      encoder_extended_attention_mask.to(
								    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
								snake_case_ :			List[str]            =      (1.0 - encoder_extended_attention_mask) * -1_0000.0

								# 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_ :			int            =      self.get_head_mask(_lowercase				,     self.config.num_hidden_layers )

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

								snake_case_ :			Optional[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  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      message
								snake_case_ :			str            =      exit_layer  # start from 1!




class  _lowerCAmelCase     (					nn.Module		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

								super().__init__()
								snake_case_ :			str            =      BertPooler(_lowercase )
								snake_case_ :			Dict            =      nn.Dropout(config.hidden_dropout_prob )
								snake_case_ :			Dict            =      nn.Linear(config.hidden_size				,     config.num_labels )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			List[str]            =      encoder_outputs[0]
								snake_case_ :			List[Any]            =      self.pooler(_lowercase )
								# "return" pooler_output

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

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

								snake_case_ :			Optional[int]            =      self.dropout(_lowercase )
								snake_case_ :			List[str]            =      self.classifier(_lowercase )

								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  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase )			->    List[Any]:







								'''simple docstring'''

								super().__init__(_lowercase )
								snake_case_ :			Union[str, Any]            =      config.num_labels
								snake_case_ :			Tuple            =      config.num_hidden_layers

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

								self.init_weights()





		@add_start_docstrings_to_model_forward(_lowercase )
		def  UpperCAmelCase__				(       self				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None				,     _lowercase=-1				,     _lowercase=False				,     )			->    int:







								'''simple docstring'''

								snake_case_ :			int            =      self.num_layers
								try:
														snake_case_ :			Any            =      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

														snake_case_ :			str            =      outputs[1]

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

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

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

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


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class      A       :
							lowercase_							     =							42
							lowercase_							     =							42



class      A       :







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							[[] for _ in range(lowerCAmelCase_   )]
														_a      	=							size







							def __getitem__(    self			:    Any				,     lowerCAmelCase_			:    int   )     ->				Iterator[Edge]:


														"""simple docstring"""


														return iter(self._graph[vertex]   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


														return self._size







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														if weight not in (0, 1):
																					raise ValueError('''Edge weight must be either 0 or 1.'''   )

														if to_vertex < 0 or to_vertex >= self.size:
																					raise ValueError('''Vertex indexes must be in [0; size).'''   )

														self._graph[from_vertex].append(Edge(lowerCAmelCase_				,     lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				int | None:


														"""simple docstring"""


														_a      	=							deque([start_vertex]   )
														_a      	=							[None] * self.size
														_a      	=							0

														while queue:
																					_a      	=							queue.popleft()
																					_a      	=							distances[current_vertex]
																					if current_distance is None:
																												continue

																					for edge in self[current_vertex]:
																												_a      	=							current_distance + edge.weight
																												_a      	=							distances[edge.destination_vertex]
																												if (
																												    isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																												    and new_distance >= dest_vertex_distance
																												):
																																			continue
																												_a      	=							new_distance
																												if edge.weight == 0:
																																			queue.appendleft(edge.destination_vertex   )
																												else:
																																			queue.append(edge.destination_vertex   )

														if distances[finish_vertex] is None:
																					raise ValueError('''No path from start_vertex to finish_vertex.'''   )

														return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

