
"""simple docstring"""

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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase			:       Optional[Any]									=				logging.getLogger(__name__)






def 							A		(       snake_case   :Dict       ,      snake_case   :Optional[int]	)   ->			Optional[Any]:
 return (preds == labels).mean()

@dataclass
class __lowerCAmelCase	:
     lowercase		     =		field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     lowercase		     =		field(
         default=snake_case__    ,						metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     lowercase		     =		field(
         default=snake_case__    ,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     lowercase		     =		field(
         default=snake_case__    ,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,						)


@dataclass
class __lowerCAmelCase	:
     lowercase		     =		field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()    )}    )
     lowercase		     =		field(metadata={"help": "Should contain the data files for the task."}    )
     lowercase		     =		field(
         default=128    ,						metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }    ,						)
     lowercase		     =		field(
         default=snake_case__    ,						metadata={"help": "Overwrite the cached training and evaluation sets"}    )






def 							A		(       )   ->			Tuple:
 __UpperCamelCase       						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
 __UpperCamelCase       						= parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir	)
     and os.listdir(training_args.output_dir	)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
      ' --overwrite_output_dir to overcome.'	)

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,      datefmt='%m/%d/%Y %H:%M:%S'       ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,      )
 logger.warning(
     'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'       ,      training_args.local_rank       ,      training_args.device       ,      training_args.n_gpu       ,      bool(training_args.local_rank != -1	)       ,      training_args.fpaa       ,      )
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank	):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('Training/evaluation parameters %s'       ,      __SCREAMING_SNAKE_CASE	)

 # Set seed
 set_seed(training_args.seed	)

 try:
  __UpperCamelCase       						= processors[data_args.task_name]()
  __UpperCamelCase       						= processor.get_labels()
  __UpperCamelCase       						= len(__SCREAMING_SNAKE_CASE	)
 except KeyError:
  raise ValueError('Task not found: %s' % (data_args.task_name)	)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 __UpperCamelCase       						= AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=__SCREAMING_SNAKE_CASE       ,      finetuning_task=data_args.task_name       ,      cache_dir=model_args.cache_dir       ,      )
 __UpperCamelCase       						= AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      )
 __UpperCamelCase       						= AutoModelForMultipleChoice.from_pretrained(
     model_args.model_name_or_path       ,      from_tf=bool('.ckpt' in model_args.model_name_or_path	)       ,      config=__SCREAMING_SNAKE_CASE       ,      cache_dir=model_args.cache_dir       ,      )

 # Get datasets
 __UpperCamelCase       						= (
     MultipleChoiceDataset(
         data_dir=data_args.data_dir       ,      tokenizer=__SCREAMING_SNAKE_CASE       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.train       ,      )
     if training_args.do_train
     else None
 )
 __UpperCamelCase       						= (
     MultipleChoiceDataset(
         data_dir=data_args.data_dir       ,      tokenizer=__SCREAMING_SNAKE_CASE       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.dev       ,      )
     if training_args.do_eval
     else None
 )

 def compute_metrics(snake_case   :int	) -> Dict:
  __UpperCamelCase       						= np.argmax(p.predictions       ,      axis=1	)
  return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE       ,      p.label_ids	)}

 # Data collator
 __UpperCamelCase       						= DataCollatorWithPadding(__SCREAMING_SNAKE_CASE       ,      pad_to_multiple_of=8	) if training_args.fpaa else None

 # Initialize our Trainer
 __UpperCamelCase       						= Trainer(
     model=__SCREAMING_SNAKE_CASE       ,      args=__SCREAMING_SNAKE_CASE       ,      train_dataset=__SCREAMING_SNAKE_CASE       ,      eval_dataset=__SCREAMING_SNAKE_CASE       ,      compute_metrics=__SCREAMING_SNAKE_CASE       ,      data_collator=__SCREAMING_SNAKE_CASE       ,      )

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
 __UpperCamelCase       						= {}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'	)

  __UpperCamelCase       						= trainer.evaluate()

  __UpperCamelCase       						= os.path.join(training_args.output_dir       ,      'eval_results.txt'	)
  if trainer.is_world_master():
   with open(__SCREAMING_SNAKE_CASE       ,      'w'	) as writer:
    logger.info('***** Eval results *****'	)
    for key, value in result.items():
     logger.info('  %s = %s'       ,      __SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE	)
     writer.write('%s = %s\n' % (key, value)	)

    results.update(__SCREAMING_SNAKE_CASE	)

 return results






def 							A		(       snake_case   :Optional[Any]	)   ->			int:
 main()


if __name__ == "__main__":
      main()





"""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 							A		(       snake_case   :Optional[int]	)   ->			str:
 __UpperCamelCase       						= torch.exp(snake_case	)
 __UpperCamelCase       						= torch.sum(snake_case       ,      dim=1	)  # sum of exp(x_i)
 __UpperCamelCase       						= torch.sum(x * exp_x       ,      dim=1	)  # sum of x_i * exp(x_i)
 return torch.log(snake_case	) - B / A

class __lowerCAmelCase	(   nn.Module    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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

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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= pooler.state_dict()
      for highway in self.highway:
       for name, param in highway.pooler.state_dict().items():
        param.copy_(loaded_model[name]   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




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

       __UpperCamelCase       						= layer_module(
           __UpperCAmelCase			,  __UpperCAmelCase			,  head_mask[i]			,  __UpperCAmelCase			,  __UpperCAmelCase   )
       __UpperCamelCase       						= layer_outputs[0]

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

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

       __UpperCamelCase       						= self.highway[i](__UpperCAmelCase   )
       # logits, pooled_output

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

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

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

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

      __UpperCamelCase       						= 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). "    ,						__SCREAMING_SNAKE_CASE    ,						)
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase   )
      __UpperCamelCase       						= config

      __UpperCamelCase       						= BertEmbeddings(__UpperCAmelCase   )
      __UpperCamelCase       						= DeeBertEncoder(__UpperCAmelCase   )
      __UpperCamelCase       						= BertPooler(__UpperCAmelCase   )

      self.init_weights()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.encoder.init_highway_pooler(self.pooler   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.embeddings.word_embeddings





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= value





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

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

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

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

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

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

      __UpperCamelCase       						= (
          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	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= message
      __UpperCamelCase       						= exit_layer  # start from 1!


class __lowerCAmelCase	(   nn.Module    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__()
      __UpperCamelCase       						= BertPooler(__UpperCAmelCase   )
      __UpperCamelCase       						= nn.Dropout(config.hidden_dropout_prob   )
      __UpperCamelCase       						= nn.Linear(config.hidden_size			,  config.num_labels   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= encoder_outputs[0]
      __UpperCamelCase       						= self.pooler(__UpperCAmelCase   )
      # "return" pooler_output

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

      # Dropout and classification
      __UpperCamelCase       						= bmodel_output[1]

      __UpperCamelCase       						= self.dropout(__UpperCAmelCase   )
      __UpperCamelCase       						= 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. "    ,						__SCREAMING_SNAKE_CASE    ,						)
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase   )
      __UpperCamelCase       						= config.num_labels
      __UpperCamelCase       						= config.num_hidden_layers

      __UpperCamelCase       						= DeeBertModel(__UpperCAmelCase   )
      __UpperCamelCase       						= nn.Dropout(config.hidden_dropout_prob   )
      __UpperCamelCase       						= nn.Linear(config.hidden_size			,  self.config.num_labels   )

      self.init_weights()







     @add_start_docstrings_to_model_forward(__UpperCAmelCase   )
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=-1			,  __UpperCAmelCase=False			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_layers
      try:
       __UpperCamelCase       						= 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

       __UpperCamelCase       						= outputs[1]

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

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

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

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



