
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        a_		(       __lowercase   :	Optional[Any] ) ->  Any:
			_snake_case		=			torch.exp(__a )
			_snake_case		=			torch.sum(__a       ,					dim=1 )  # sum of exp(x_i)
			_snake_case		=			torch.sum(x * exp_x       ,					dim=1 )  # sum of x_i * exp(x_i)
			return torch.log(__a ) - B / A




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Any	):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			config.output_attentions
				_snake_case		=			config.output_hidden_states
				_snake_case		=			nn.ModuleList([BertLayer(UpperCamelCase_	) for _ in range(config.num_hidden_layers	)]	)
				_snake_case		=			nn.ModuleList([BertHighway(UpperCamelCase_	) for _ in range(config.num_hidden_layers	)]	)

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



	def  A							(  self	:    Dict			,  lowercase	:    Dict	):


				'''simple docstring'''


				if (type(UpperCamelCase_	) is float) or (type(UpperCamelCase_	) is int):
							for i in range(len(self.early_exit_entropy	)	):
										_snake_case		=			x
				else:
							_snake_case		=			x



	def  A							(  self	:    Tuple			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			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	:    Union[str, Any]			,  lowercase	:    Union[str, Any]			,  lowercase	:    Dict=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    Union[str, Any]=None			,  ):


				'''simple docstring'''


				_snake_case		=			()
				_snake_case		=			()
				_snake_case		=			()
				for i, layer_module in enumerate(self.layer	):
							if self.output_hidden_states:
										_snake_case		=			all_hidden_states + (hidden_states,)

							_snake_case		=			layer_module(
							    UpperCamelCase_			,  UpperCamelCase_			,  head_mask[i]			,  UpperCamelCase_			,  UpperCamelCase_	)
							_snake_case		=			layer_outputs[0]

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

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

							_snake_case		=			self.highway[i](UpperCamelCase_	)
							# logits, pooled_output

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

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

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

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

				_snake_case		=			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    SCREAMING_SNAKE_CASE__     (	A__					):





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    Any	):


				'''simple docstring'''


				super().__init__(UpperCamelCase_	)
				_snake_case		=			config

				_snake_case		=			BertEmbeddings(UpperCamelCase_	)
				_snake_case		=			DeeBertEncoder(UpperCamelCase_	)
				_snake_case		=			BertPooler(UpperCamelCase_	)

				self.init_weights()



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				self.encoder.init_highway_pooler(self.pooler	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				return self.embeddings.word_embeddings



	def  A							(  self	:    Tuple			,  lowercase	:    str	):


				'''simple docstring'''


				_snake_case		=			value



	def  A							(  self	:    List[str]			,  lowercase	:    int	):


				'''simple docstring'''


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





	@add_start_docstrings_to_model_forward(UpperCamelCase_	)
	def  A							(  self	:    str			,  lowercase	:    Optional[int]=None			,  lowercase	:    str=None			,  lowercase	:    List[str]=None			,  lowercase	:    Any=None			,  lowercase	:    List[str]=None			,  lowercase	:    List[Any]=None			,  lowercase	:    Union[str, Any]=None			,  lowercase	:    List[str]=None			,  ):


				'''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		=			input_ids.size()
				elif inputs_embeds is not None:
							_snake_case		=			inputs_embeds.size()[:-1]
				else:
							raise ValueError('You have to specify either input_ids or inputs_embeds'	)

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

				if attention_mask is None:
							_snake_case		=			torch.ones(UpperCamelCase_			,  device=UpperCamelCase_	)
				if encoder_attention_mask is None:
							_snake_case		=			torch.ones(UpperCamelCase_			,  device=UpperCamelCase_	)
				if token_type_ids is None:
							_snake_case		=			torch.zeros(UpperCamelCase_			,  dtype=torch.long			,  device=UpperCamelCase_	)

				# 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		=			self.get_extended_attention_mask(UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_	)

				# 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		=			encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
							_snake_case		=			encoder_attention_mask[:, None, None, :]

				_snake_case		=			encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()	).dtype	)  # fp16 compatibility
				_snake_case		=			(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]
				_snake_case		=			self.get_head_mask(UpperCamelCase_			,  self.config.num_hidden_layers	)

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

				_snake_case		=			(
				    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    SCREAMING_SNAKE_CASE__     (	A__					):





	'''simple docstring'''



	def __init__(  self	:    Any			,  lowercase	:    Tuple			,  lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			message
				_snake_case		=			exit_layer  # start from 1!




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Union[str, Any]			,  lowercase	:    str	):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			BertPooler(UpperCamelCase_	)
				_snake_case		=			nn.Dropout(config.hidden_dropout_prob	)
				_snake_case		=			nn.Linear(config.hidden_size			,  config.num_labels	)





	def  A							(  self	:    int			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			encoder_outputs[0]
				_snake_case		=			self.pooler(UpperCamelCase_	)
				# "return" pooler_output

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

				# Dropout and classification
				_snake_case		=			bmodel_output[1]

				_snake_case		=			self.dropout(UpperCamelCase_	)
				_snake_case		=			self.classifier(UpperCamelCase_	)

				return logits, pooled_output




@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "							,A__							,)
class    SCREAMING_SNAKE_CASE__     (	A__					):





	'''simple docstring'''



	def __init__(  self	:    Any			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				super().__init__(UpperCamelCase_	)
				_snake_case		=			config.num_labels
				_snake_case		=			config.num_hidden_layers

				_snake_case		=			DeeBertModel(UpperCamelCase_	)
				_snake_case		=			nn.Dropout(config.hidden_dropout_prob	)
				_snake_case		=			nn.Linear(config.hidden_size			,  self.config.num_labels	)

				self.init_weights()





	@add_start_docstrings_to_model_forward(UpperCamelCase_	)
	def  A							(  self	:    Optional[int]			,  lowercase	:    List[Any]=None			,  lowercase	:    str=None			,  lowercase	:    Dict=None			,  lowercase	:    Dict=None			,  lowercase	:    Any=None			,  lowercase	:    Tuple=None			,  lowercase	:    Optional[int]=None			,  lowercase	:    List[Any]=-1			,  lowercase	:    Optional[int]=False			,  ):


				'''simple docstring'''


				_snake_case		=			self.num_layers
				try:
							_snake_case		=			self.bert(
							    UpperCamelCase_			,  attention_mask=UpperCamelCase_			,  token_type_ids=UpperCamelCase_			,  position_ids=UpperCamelCase_			,  head_mask=UpperCamelCase_			,  inputs_embeds=UpperCamelCase_			,  )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							_snake_case		=			outputs[1]

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

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

							# work with highway exits
							_snake_case		=			[]
							for highway_exit in outputs[-1]:
										_snake_case		=			highway_exit[0]
										if not self.training:
													highway_logits_all.append(UpperCamelCase_	)
													highway_entropy.append(highway_exit[2]	)
										if self.num_labels == 1:
													#  We are doing regression
													_snake_case		=			MSELoss()
													_snake_case		=			loss_fct(highway_logits.view(-1	)			,  labels.view(-1	)	)
										else:
													_snake_case		=			CrossEntropyLoss()
													_snake_case		=			loss_fct(highway_logits.view(-1			,  self.num_labels	)			,  labels.view(-1	)	)
										highway_losses.append(UpperCamelCase_	)

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



					'''simple docstring'''






					if len(__a   ) != len(__a   ):
										raise ValueError('''String lengths must match!'''   )

					UpperCamelCase__   :Union[str, Any]												= 0

					for chara, chara in zip(__a ,					__a   ):
										if chara != chara:
															count += 1

					return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()