


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 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   torch.exp(a__  )
			__lowercase 					=   torch.sum(a__       ,			dim=1  )  # sum of exp(x_i)
			__lowercase 					=   torch.sum(x * exp_x       ,			dim=1  )  # sum of x_i * exp(x_i)
			return torch.log(a__  ) - B / A
class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







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







								'''simple docstring'''

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

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






					def A      (				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   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 ,       snake_case_ ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       )   ->			Union[str, Any]:







								'''simple docstring'''

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

											__lowercase 					=   layer_module(
											    __A ,       __A ,       head_mask[i] ,       __A ,       __A						)
											__lowercase 					=   layer_outputs[0]

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

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

											__lowercase 					=   self.highway[i](__A						)
											# logits, pooled_output

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

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

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

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

								__lowercase 					=   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). "     ,     __lowercase     ,     )
class 	lowerCamelCase_       (				__lowercase				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								super().__init__(__A						)
								__lowercase 					=   config

								__lowercase 					=   BertEmbeddings(__A						)
								__lowercase 					=   DeeBertEncoder(__A						)
								__lowercase 					=   BertPooler(__A						)

								self.init_weights()






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								self.encoder.init_highway_pooler(self.pooler						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								return self.embeddings.word_embeddings






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   value






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

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






					@add_start_docstrings_to_model_forward(__A						)
					def A      (				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       )   ->			int:







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

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

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

								# 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.
								__lowercase 					=   self.get_extended_attention_mask(__A ,       __A ,       __A						)

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

								__lowercase 					=   encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()						).dtype						)  # fp16 compatibility
								__lowercase 					=   (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

								# 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]
								__lowercase 					=   self.get_head_mask(__A ,       self.config.num_hidden_layers						)

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

								__lowercase 					=   (
								    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_       (				__lowercase				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   message
								__lowercase 					=   exit_layer  # start from 1!




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			int:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   BertPooler(__A						)
								__lowercase 					=   nn.Dropout(config.hidden_dropout_prob						)
								__lowercase 					=   nn.Linear(config.hidden_size ,       config.num_labels						)






					def A      (				self ,       snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   encoder_outputs[0]
								__lowercase 					=   self.pooler(__A						)
								# "return" pooler_output

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

								# Dropout and classification
								__lowercase 					=   bmodel_output[1]

								__lowercase 					=   self.dropout(__A						)
								__lowercase 					=   self.classifier(__A						)

								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. "     ,     __lowercase     ,     )
class 	lowerCamelCase_       (				__lowercase				):



					'''simple docstring'''







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







								'''simple docstring'''

								super().__init__(__A						)
								__lowercase 					=   config.num_labels
								__lowercase 					=   config.num_hidden_layers

								__lowercase 					=   DeeBertModel(__A						)
								__lowercase 					=   nn.Dropout(config.hidden_dropout_prob						)
								__lowercase 					=   nn.Linear(config.hidden_size ,       self.config.num_labels						)

								self.init_weights()






					@add_start_docstrings_to_model_forward(__A						)
					def A      (				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=-1 ,       snake_case_=False ,       )   ->			Tuple:







								'''simple docstring'''

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

											__lowercase 					=   outputs[1]

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

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

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

											if train_highway:
														__lowercase 					=   (sum(highway_losses[:-1]						),) + outputs
														# exclude the final highway, of course
											else:
														__lowercase 					=   (loss,) + outputs
								if not self.training:
											__lowercase 					=   outputs + ((original_entropy, highway_entropy), exit_layer)
											if output_layer >= 0:
														__lowercase 					=   (
														    (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 ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






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







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)




class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)


