





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					)						->		Dict:






				"""simple docstring"""


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



class    __lowerCAmelCase			(     nn.Module	):






			def __init__(self   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



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

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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[int]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Any:


							'''simple docstring'''



							snake_case_		:       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   ,				__magic_name__   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				)	->  Tuple:


							'''simple docstring'''



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

											snake_case_		:       Union[str, Any]           =    layer_module(
											    __magic_name__   ,				__magic_name__   ,				head_mask[i]   ,				__magic_name__   ,				__magic_name__       )
											snake_case_		:       List[str]           =    layer_outputs[0]

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

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

											snake_case_		:       Dict           =    self.highway[i](__magic_name__       )
											# logits, pooled_output

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

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

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

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

							snake_case_		:       Dict           =    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). ''',	_a,	)
class    __lowerCAmelCase			(     _a	):






			def __init__(self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							super().__init__(__magic_name__       )
							snake_case_		:       Any           =    config

							snake_case_		:       int           =    BertEmbeddings(__magic_name__       )
							snake_case_		:       str           =    DeeBertEncoder(__magic_name__       )
							snake_case_		:       Any           =    BertPooler(__magic_name__       )

							self.init_weights()






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							self.encoder.init_highway_pooler(self.pooler       )






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return self.embeddings.word_embeddings






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       List[str]           =    value






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



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






			@add_start_docstrings_to_model_forward(__magic_name__       )
			def      lowerCamelCase    (self   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				)	->  List[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_		:       Union[str, Any]           =    input_ids.size()
							elif inputs_embeds is not None:
											snake_case_		:       int           =    inputs_embeds.size()[:-1]
							else:
											raise ValueError('''You have to specify either input_ids or inputs_embeds'''       )

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

							if attention_mask is None:
											snake_case_		:       Optional[Any]           =    torch.ones(__magic_name__   ,				device=__magic_name__       )
							if encoder_attention_mask is None:
											snake_case_		:       Optional[int]           =    torch.ones(__magic_name__   ,				device=__magic_name__       )
							if token_type_ids is None:
											snake_case_		:       int           =    torch.zeros(__magic_name__   ,				dtype=torch.long   ,				device=__magic_name__       )

							# 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(__magic_name__   ,				__magic_name__   ,				__magic_name__       )

							# 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_		:       Any           =    encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
											snake_case_		:       List[str]           =    encoder_attention_mask[:, None, None, :]

							snake_case_		:       Optional[Any]           =    encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()       ).dtype       )  # fp16 compatibility
							snake_case_		:       Any           =    (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]
							snake_case_		:       Any           =    self.get_head_mask(__magic_name__   ,				self.config.num_hidden_layers       )

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

							snake_case_		:       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			(     _a	):






			def __init__(self   ,				__magic_name__   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       Dict           =    message
							snake_case_		:       Dict           =    exit_layer  # start from 1!




class    __lowerCAmelCase			(     nn.Module	):






			def __init__(self   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							super().__init__()
							snake_case_		:       Optional[int]           =    BertPooler(__magic_name__       )
							snake_case_		:       Optional[int]           =    nn.Dropout(config.hidden_dropout_prob       )
							snake_case_		:       Union[str, Any]           =    nn.Linear(config.hidden_size   ,				config.num_labels       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       str           =    encoder_outputs[0]
							snake_case_		:       Dict           =    self.pooler(__magic_name__       )
							# "return" pooler_output

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

							# Dropout and classification
							snake_case_		:       Dict           =    bmodel_output[1]

							snake_case_		:       List[Any]           =    self.dropout(__magic_name__       )
							snake_case_		:       Any           =    self.classifier(__magic_name__       )

							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. ''',	_a,	)
class    __lowerCAmelCase			(     _a	):






			def __init__(self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



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

							snake_case_		:       str           =    DeeBertModel(__magic_name__       )
							snake_case_		:       Union[str, 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(__magic_name__       )
			def      lowerCamelCase    (self   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=-1   ,				__magic_name__=False   ,				)	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.num_layers
							try:
											snake_case_		:       int           =    self.bert(
											    __magic_name__   ,				attention_mask=__magic_name__   ,				token_type_ids=__magic_name__   ,				position_ids=__magic_name__   ,				head_mask=__magic_name__   ,				inputs_embeds=__magic_name__   ,				)
											# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

											snake_case_		:       List[str]           =    outputs[1]

											snake_case_		:       List[str]           =    self.dropout(__magic_name__       )
											snake_case_		:       Tuple           =    self.classifier(__magic_name__       )
											snake_case_		:       Optional[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_		:       str           =    outputs[0]

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

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

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





from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



