



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_					(  _lowercase    :       str)							->	List[str]:







			"""simple docstring"""
			a__       :						List[Any]													=  torch.exp(_lowercase)
			a__       :						Union[str, Any]													=  torch.sum(_lowercase						,       dim=1)  # sum of exp(x_i)
			a__       :						Optional[Any]													=  torch.sum(x * exp_x						,       dim=1)  # sum of x_i * exp(x_i)
			return torch.log(_lowercase) - B / A

class        snake_case__    (nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




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

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									if (type(__lowercase  ) is float) or (type(__lowercase  ) is int):
												for i in range(len(self.early_exit_entropy  )  ):
															a__       :						List[str]													=  x
									else:
												a__       :						Dict													=  x

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						int													=  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 ,       __lowercase ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       ) ->				Dict:
									"""simple docstring"""




									a__       :						Any													=  ()
									a__       :						Any													=  ()
									a__       :						str													=  ()
									for i, layer_module in enumerate(self.layer  ):
												if self.output_hidden_states:
															a__       :						Tuple													=  all_hidden_states + (hidden_states,)

												a__       :						Optional[int]													=  layer_module(
												    __lowercase ,       __lowercase ,       head_mask[i] ,       __lowercase ,       __lowercase  )
												a__       :						str													=  layer_outputs[0]

												if self.output_attentions:
															a__       :						Optional[Any]													=  all_attentions + (layer_outputs[1],)

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

												a__       :						List[Any]													=  self.highway[i](__lowercase  )
												# logits, pooled_output

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

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

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

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

									a__       :						Optional[Any]													=  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        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									super().__init__(__lowercase  )
									a__       :						Dict													=  config

									a__       :						int													=  BertEmbeddings(__lowercase  )
									a__       :						List[Any]													=  DeeBertEncoder(__lowercase  )
									a__       :						List[Any]													=  BertPooler(__lowercase  )

									self.init_weights()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.encoder.init_highway_pooler(self.pooler  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return self.embeddings.word_embeddings

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[str]													=  value

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




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

						@add_start_docstrings_to_model_forward(__lowercase  )
						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       ) ->				Union[str, 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:
												a__       :						Optional[int]													=  input_ids.size()
									elif inputs_embeds is not None:
												a__       :						Dict													=  inputs_embeds.size()[:-1]
									else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

									a__       :						List[Any]													=  input_ids.device if input_ids is not None else inputs_embeds.device

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

									# 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(__lowercase ,       __lowercase ,       __lowercase  )

									# 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__       :						List[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__       :						Optional[Any]													=  (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]
									a__       :						Union[str, Any]													=  self.get_head_mask(__lowercase ,       self.config.num_hidden_layers  )

									a__       :						Optional[int]													=  self.embeddings(
									    input_ids=__lowercase ,       position_ids=__lowercase ,       token_type_ids=__lowercase ,       inputs_embeds=__lowercase  )
									a__       :						Dict													=  self.encoder(
									    __lowercase ,       attention_mask=__lowercase ,       head_mask=__lowercase ,       encoder_hidden_states=__lowercase ,       encoder_attention_mask=__lowercase ,       )
									a__       :						int													=  encoder_outputs[0]
									a__       :						str													=  self.pooler(__lowercase  )

									a__       :						int													=  (
									    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        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						str													=  message
									a__       :						Union[str, Any]													=  exit_layer  # start from 1!




class        snake_case__    (nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									super().__init__()
									a__       :						Union[str, Any]													=  BertPooler(__lowercase  )
									a__       :						Union[str, Any]													=  nn.Dropout(config.hidden_dropout_prob  )
									a__       :						str													=  nn.Linear(config.hidden_size ,       config.num_labels  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									a__       :						List[str]													=  encoder_outputs[0]
									a__       :						int													=  self.pooler(__lowercase  )
									# "return" pooler_output

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

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

									a__       :						str													=  self.dropout(__lowercase  )
									a__       :						Optional[int]													=  self.classifier(__lowercase  )

									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        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									super().__init__(__lowercase  )
									a__       :						List[str]													=  config.num_labels
									a__       :						Tuple													=  config.num_hidden_layers

									a__       :						Optional[int]													=  DeeBertModel(__lowercase  )
									a__       :						List[Any]													=  nn.Dropout(config.hidden_dropout_prob  )
									a__       :						Any													=  nn.Linear(config.hidden_size ,       self.config.num_labels  )

									self.init_weights()

						@add_start_docstrings_to_model_forward(__lowercase  )
						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=-1 ,       __lowercase=False ,       ) ->				str:
									"""simple docstring"""




									a__       :						List[Any]													=  self.num_layers
									try:
												a__       :						Union[str, Any]													=  self.bert(
												    __lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase ,       position_ids=__lowercase ,       head_mask=__lowercase ,       inputs_embeds=__lowercase ,       )
												# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

												a__       :						int													=  outputs[1]

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

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

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

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









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




