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_     (						snake_case__							)     ->	Tuple:
							lowerCAmelCase			=		torch.exp(A__							)
							lowerCAmelCase			=		torch.sum(A__   ,  dim=1							)  # sum of exp(x_i)
							lowerCAmelCase			=		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		,			__SCREAMING_SNAKE_CASE				)						->Union[str, Any]:
								super().__init__()
								lowerCAmelCase			=		config.output_attentions
								lowerCAmelCase			=		config.output_hidden_states
								lowerCAmelCase			=		nn.ModuleList([BertLayer(lowerCAmelCase_				) for _ in range(config.num_hidden_layers				)]				)
								lowerCAmelCase			=		nn.ModuleList([BertHighway(lowerCAmelCase_				) for _ in range(config.num_hidden_layers				)]				)

								lowerCAmelCase			=		[-1 for _ in range(config.num_hidden_layers				)]


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Dict:
								if (type(lowerCAmelCase_				) is float) or (type(lowerCAmelCase_				) is int):
															for i in range(len(self.early_exit_entropy				)				):
																						lowerCAmelCase			=		x
								else:
															lowerCAmelCase			=		x


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Tuple:
								lowerCAmelCase			=		pooler.state_dict()
								for highway in self.highway:
															for name, param in highway.pooler.state_dict().items():
																						param.copy_(loaded_model[name]				)




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			)						->str:
								lowerCAmelCase			=		()
								lowerCAmelCase			=		()
								lowerCAmelCase			=		()
								for i, layer_module in enumerate(self.layer				):
															if self.output_hidden_states:
																						lowerCAmelCase			=		all_hidden_states + (hidden_states,)

															lowerCAmelCase			=		layer_module(
															    lowerCAmelCase_		,			lowerCAmelCase_		,			head_mask[i]		,			lowerCAmelCase_		,			lowerCAmelCase_				)
															lowerCAmelCase			=		layer_outputs[0]

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

															lowerCAmelCase			=		(hidden_states,)
															if self.output_hidden_states:
																						lowerCAmelCase			=		current_outputs + (all_hidden_states,)
															if self.output_attentions:
																						lowerCAmelCase			=		current_outputs + (all_attentions,)

															lowerCAmelCase			=		self.highway[i](lowerCAmelCase_				)
															# logits, pooled_output

															if not self.training:
																						lowerCAmelCase			=		highway_exit[0]
																						lowerCAmelCase			=		entropy(lowerCAmelCase_				)
																						lowerCAmelCase			=		highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																						lowerCAmelCase			=		all_highway_exits + (highway_exit,)

																						if highway_entropy < self.early_exit_entropy[i]:
																													lowerCAmelCase			=		(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																													raise HighwayException(lowerCAmelCase_		,			i + 1				)
															else:
																						lowerCAmelCase			=		all_highway_exits + (highway_exit,)

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

								lowerCAmelCase			=		(hidden_states,)
								if self.output_hidden_states:
															lowerCAmelCase			=		outputs + (all_hidden_states,)
								if self.output_attentions:
															lowerCAmelCase			=		outputs + (all_attentions,)

								lowerCAmelCase			=		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    lowercase_  (  __A						):



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE				)						->List[str]:
								super().__init__(lowerCAmelCase_				)
								lowerCAmelCase			=		config

								lowerCAmelCase			=		BertEmbeddings(lowerCAmelCase_				)
								lowerCAmelCase			=		DeeBertEncoder(lowerCAmelCase_				)
								lowerCAmelCase			=		BertPooler(lowerCAmelCase_				)

								self.init_weights()


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Dict:
								self.encoder.init_highway_pooler(self.pooler				)


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								return self.embeddings.word_embeddings


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Any:
								lowerCAmelCase			=		value


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->List[Any]:
								for layer, heads in heads_to_prune.items():
															self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase_				)




	@add_start_docstrings_to_model_forward(lowerCAmelCase_				)
	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			)						->Optional[int]:
								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:
															lowerCAmelCase			=		input_ids.size()
								elif inputs_embeds is not None:
															lowerCAmelCase			=		inputs_embeds.size()[:-1]
								else:
															raise ValueError('''You have to specify either input_ids or inputs_embeds'''				)

								lowerCAmelCase			=		input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
															lowerCAmelCase			=		torch.ones(lowerCAmelCase_		,			device=lowerCAmelCase_				)
								if encoder_attention_mask is None:
															lowerCAmelCase			=		torch.ones(lowerCAmelCase_		,			device=lowerCAmelCase_				)
								if token_type_ids is None:
															lowerCAmelCase			=		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.
								lowerCAmelCase			=		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:
															lowerCAmelCase			=		encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
															lowerCAmelCase			=		encoder_attention_mask[:, None, None, :]

								lowerCAmelCase			=		encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()				).dtype				)  # fp16 compatibility
								lowerCAmelCase			=		(1.0 - encoder_extended_attention_mask) * -10000.0

								# 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]
								lowerCAmelCase			=		self.get_head_mask(lowerCAmelCase_		,			self.config.num_hidden_layers				)

								lowerCAmelCase			=		self.embeddings(
								    input_ids=lowerCAmelCase_		,			position_ids=lowerCAmelCase_		,			token_type_ids=lowerCAmelCase_		,			inputs_embeds=lowerCAmelCase_				)
								lowerCAmelCase			=		self.encoder(
								    lowerCAmelCase_		,			attention_mask=lowerCAmelCase_		,			head_mask=lowerCAmelCase_		,			encoder_hidden_states=lowerCAmelCase_		,			encoder_attention_mask=lowerCAmelCase_		,			)
								lowerCAmelCase			=		encoder_outputs[0]
								lowerCAmelCase			=		self.pooler(lowerCAmelCase_				)

								lowerCAmelCase			=		(
								    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_  (  __A						):



	"""simple docstring"""





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




class    lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE				)						->Dict:
								super().__init__()
								lowerCAmelCase			=		BertPooler(lowerCAmelCase_				)
								lowerCAmelCase			=		nn.Dropout(config.hidden_dropout_prob				)
								lowerCAmelCase			=		nn.Linear(config.hidden_size		,			config.num_labels				)




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Optional[int]:
								# Pooler
								lowerCAmelCase			=		encoder_outputs[0]
								lowerCAmelCase			=		self.pooler(lowerCAmelCase_				)
								# "return" pooler_output

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

								# Dropout and classification
								lowerCAmelCase			=		bmodel_output[1]

								lowerCAmelCase			=		self.dropout(lowerCAmelCase_				)
								lowerCAmelCase			=		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    lowercase_  (  __A						):



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE				)						->List[Any]:
								super().__init__(lowerCAmelCase_				)
								lowerCAmelCase			=		config.num_labels
								lowerCAmelCase			=		config.num_hidden_layers

								lowerCAmelCase			=		DeeBertModel(lowerCAmelCase_				)
								lowerCAmelCase			=		nn.Dropout(config.hidden_dropout_prob				)
								lowerCAmelCase			=		nn.Linear(config.hidden_size		,			self.config.num_labels				)

								self.init_weights()




	@add_start_docstrings_to_model_forward(lowerCAmelCase_				)
	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=-1		,			__SCREAMING_SNAKE_CASE=False		,			)						->Tuple:
								lowerCAmelCase			=		self.num_layers
								try:
															lowerCAmelCase			=		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

															lowerCAmelCase			=		outputs[1]

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

								if not self.training:
															lowerCAmelCase			=		entropy(lowerCAmelCase_				)
															lowerCAmelCase			=		[]
															lowerCAmelCase			=		[]
								if labels is not None:
															if self.num_labels == 1:
																						#  We are doing regression
																						lowerCAmelCase			=		MSELoss()
																						lowerCAmelCase			=		loss_fct(logits.view(-1				)		,			labels.view(-1				)				)
															else:
																						lowerCAmelCase			=		CrossEntropyLoss()
																						lowerCAmelCase			=		loss_fct(logits.view(-1		,			self.num_labels				)		,			labels.view(-1				)				)

															# work with highway exits
															lowerCAmelCase			=		[]
															for highway_exit in outputs[-1]:
																						lowerCAmelCase			=		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
																													lowerCAmelCase			=		MSELoss()
																													lowerCAmelCase			=		loss_fct(highway_logits.view(-1				)		,			labels.view(-1				)				)
																						else:
																													lowerCAmelCase			=		CrossEntropyLoss()
																													lowerCAmelCase			=		loss_fct(highway_logits.view(-1		,			self.num_labels				)		,			labels.view(-1				)				)
																						highway_losses.append(lowerCAmelCase_				)

															if train_highway:
																						lowerCAmelCase			=		(sum(highway_losses[:-1]				),) + outputs
																						# exclude the final highway, of course
															else:
																						lowerCAmelCase			=		(loss,) + outputs
								if not self.training:
															lowerCAmelCase			=		outputs + ((original_entropy, highway_entropy), exit_layer)
															if output_layer >= 0:
																						lowerCAmelCase			=		(
																						    (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 torch import nn

def 				snake_case      (   A__						):
			if act_fn in ["swish", "silu"]:
						return nn.SiLU()
			elif act_fn == "mish":
						return nn.Mish()
			elif act_fn == "gelu":
						return nn.GELU()
			else:
						raise ValueError(F"""Unsupported activation function: {act_fn}"""						)



