





"""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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->Optional[int]:






	'''simple docstring'''






	a :						Any      =							torch.exp(_lowercase   )
	a :						Any      =							torch.sum(_lowercase       ,			dim=1   )  # sum of exp(x_i)
	a :						Tuple      =							torch.sum(x * exp_x       ,			dim=1   )  # sum of x_i * exp(x_i)
	return torch.log(_lowercase   ) - B / A


class 			__UpperCamelCase							(		nn.Module		):






							def __init__(  self ,   lowerCAmelCase__ )      ->					Optional[int]:
								super().__init__()
								a :						Dict      =							config.output_attentions
								a :						Optional[int]      =							config.output_hidden_states
								a :						Optional[Any]      =							nn.ModuleList([BertLayer(lowerCAmelCase__ ) for _ in range(config.num_hidden_layers )] )
								a :						Union[str, Any]      =							nn.ModuleList([BertHighway(lowerCAmelCase__ ) for _ in range(config.num_hidden_layers )] )

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






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Optional[Any]:
								if (type(lowerCAmelCase__ ) is float) or (type(lowerCAmelCase__ ) is int):
									for i in range(len(self.early_exit_entropy ) ):
										a :						Any      =							x
								else:
									a :						Tuple      =							x






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[Any]:
								a :						Any      =							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 ,   lowerCAmelCase__ ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   )      ->					Union[str, Any]:
								a :						Tuple      =							()
								a :						str      =							()
								a :						List[Any]      =							()
								for i, layer_module in enumerate(self.layer ):
									if self.output_hidden_states:
										a :						str      =							all_hidden_states + (hidden_states,)

									a :						List[Any]      =							layer_module(
									    lowerCAmelCase__ ,   lowerCAmelCase__ ,   head_mask[i] ,   lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						str      =							layer_outputs[0]

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

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

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

									if not self.training:
										a :						Optional[Any]      =							highway_exit[0]
										a :						Tuple      =							entropy(lowerCAmelCase__ )
										a :						int      =							highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
										a :						Dict      =							all_highway_exits + (highway_exit,)

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

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

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

								a :						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 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ )      ->					Union[str, Any]:
								super().__init__(lowerCAmelCase__ )
								a :						Union[str, Any]      =							config

								a :						Tuple      =							BertEmbeddings(lowerCAmelCase__ )
								a :						Optional[Any]      =							DeeBertEncoder(lowerCAmelCase__ )
								a :						Any      =							BertPooler(lowerCAmelCase__ )

								self.init_weights()






							def        __a  (  self )      ->					Optional[Any]:
								self.encoder.init_highway_pooler(self.pooler )






							def        __a  (  self )      ->					int:
								return self.embeddings.word_embeddings






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Optional[int]:
								a :						List[Any]      =							value






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Optional[int]:
								for layer, heads in heads_to_prune.items():
									self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__ )






							@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
							def        __a  (  self ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   )      ->					Tuple:
								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 :						Optional[Any]      =							input_ids.size()
								elif inputs_embeds is not None:
									a :						str      =							inputs_embeds.size()[:-1]
								else:
									raise ValueError("You have to specify either input_ids or inputs_embeds" )

								a :						Tuple      =							input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
									a :						List[str]      =							torch.ones(lowerCAmelCase__ ,   device=lowerCAmelCase__ )
								if encoder_attention_mask is None:
									a :						int      =							torch.ones(lowerCAmelCase__ ,   device=lowerCAmelCase__ )
								if token_type_ids is None:
									a :						List[Any]      =							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.
								a :						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:
									a :						Optional[Any]      =							encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
									a :						Union[str, Any]      =							encoder_attention_mask[:, None, None, :]

								a :						Optional[Any]      =							encoder_extended_attention_mask.to(
								    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
								a :						List[str]      =							(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]
								a :						Optional[int]      =							self.get_head_mask(lowerCAmelCase__ ,   self.config.num_hidden_layers )

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

								a :						List[str]      =							(
								    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 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[Any]:
								a :						List[Any]      =							message
								a :						int      =							exit_layer  # start from 1!


class 			__UpperCamelCase							(		nn.Module		):






							def __init__(  self ,   lowerCAmelCase__ )      ->					int:
								super().__init__()
								a :						Tuple      =							BertPooler(lowerCAmelCase__ )
								a :						Any      =							nn.Dropout(config.hidden_dropout_prob )
								a :						Any      =							nn.Linear(config.hidden_size ,   config.num_labels )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Optional[int]:
								# Pooler
								a :						List[Any]      =							encoder_outputs[0]
								a :						str      =							self.pooler(lowerCAmelCase__ )
								# "return" pooler_output

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

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

								a :						Tuple      =							self.dropout(lowerCAmelCase__ )
								a :						List[str]      =							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. """						,			a__						,			)
class 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ )      ->					Tuple:
								super().__init__(lowerCAmelCase__ )
								a :						str      =							config.num_labels
								a :						Union[str, Any]      =							config.num_hidden_layers

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

								self.init_weights()






							@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
							def        __a  (  self ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   lowerCAmelCase__=-1 ,   lowerCAmelCase__=False ,   )      ->					Union[str, Any]:
								a :						Optional[int]      =							self.num_layers
								try:
									a :						List[str]      =							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

									a :						Union[str, Any]      =							outputs[1]

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

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

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

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




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Optional[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	a :						List[str]      =							set({"(", "[", "{"}   )
	a :						int      =							set({")", "]", "}"}   )
	a :						int      =							{"{": "}", "[": "]", "(": ")"}

	for i in range(len(_lowercase   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(_lowercase   ) == 0 or (len(_lowercase   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(_lowercase   ) == 0






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						Any      =							input("Enter sequence of brackets: "   )
	if is_balanced(_lowercase   ):
		print(_lowercase       ,			"is balanced"   )
	else:
		print(_lowercase       ,			"is not balanced"   )


if __name__ == "__main__":
		main()


