





"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase    :	Tuple						=     logging.getLogger(__name__)







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







@dataclass
class 			a       :
 snake_case__								=					field(
     metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()     )}     )
 snake_case__								=					field(
     metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}     )
 snake_case__								=					field(
     default=1_2_8      ,   metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     }      ,   )
 snake_case__								=					field(
     default=a__      ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )


def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						List[InputFeatures] ):
 lowerCAmelCase													=   default_data_collator(_UpperCAmelCase )
 lowerCAmelCase													=   batch.pop('pairID'     ,    _UpperCAmelCase )
 return batch

def 		_SCREAMING_SNAKE_CASE ():
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCAmelCase													=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
 lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   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'     ,    _UpperCAmelCase )

 # Set seed
 set_seed(training_args.seed )

 try:
  lowerCAmelCase													=   hans_tasks_num_labels[data_args.task_name]
 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.

 lowerCAmelCase													=   AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,    num_labels=_UpperCAmelCase     ,    finetuning_task=data_args.task_name     ,    cache_dir=model_args.cache_dir     ,    )
 lowerCAmelCase													=   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     ,    )
 lowerCAmelCase													=   AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path     ,    from_tf=bool('.ckpt' in model_args.model_name_or_path )     ,    config=_UpperCAmelCase     ,    cache_dir=model_args.cache_dir     ,    )

 # Get datasets
 lowerCAmelCase													=   (
     HansDataset(
         data_dir=data_args.data_dir     ,    tokenizer=_UpperCAmelCase     ,    task=data_args.task_name     ,    max_seq_length=data_args.max_seq_length     ,    overwrite_cache=data_args.overwrite_cache     ,    )
     if training_args.do_train
     else None
 )
 lowerCAmelCase													=   (
     HansDataset(
         data_dir=data_args.data_dir     ,    tokenizer=_UpperCAmelCase     ,    task=data_args.task_name     ,    max_seq_length=data_args.max_seq_length     ,    overwrite_cache=data_args.overwrite_cache     ,    evaluate=_UpperCAmelCase     ,    )
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 lowerCAmelCase													=   Trainer(
     model=_UpperCAmelCase     ,    args=_UpperCAmelCase     ,    train_dataset=_UpperCAmelCase     ,    eval_dataset=_UpperCAmelCase     ,    data_collator=_UpperCAmelCase     ,    )

 # 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
 if training_args.do_eval:
  logger.info('*** Evaluate ***' )

  lowerCAmelCase													=   trainer.predict(_UpperCAmelCase )
  lowerCAmelCase													=   output.predictions
  lowerCAmelCase													=   np.argmax(_UpperCAmelCase     ,    axis=1 )

  lowerCAmelCase													=   [ex.pairID for ex in eval_dataset]
  lowerCAmelCase													=   os.path.join(training_args.output_dir     ,    'hans_predictions.txt' )
  lowerCAmelCase													=   eval_dataset.get_labels()
  if trainer.is_world_master():
   with open(_UpperCAmelCase     ,    'w' ) as writer:
    writer.write('pairID,gold_label\n' )
    for pid, pred in zip(_UpperCAmelCase     ,    _UpperCAmelCase ):
     writer.write('ex' + str(_UpperCAmelCase ) + ',' + label_list[int(_UpperCAmelCase )] + '\n' )

  trainer._log(output.metrics )

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Optional[Any] ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
       main()












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase    :	Dict						=     logging.get_logger(__name__)

__UpperCamelCase    :	List[Any]						=     {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}







class 			a       (      a__     ):
 snake_case__								=					'''mpnet'''



 def __init__(    self							,					_snake_case=3_05_27							,					_snake_case=7_68							,					_snake_case=12							,					_snake_case=12							,					_snake_case=30_72							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=0.02							,					_snake_case=1E-12							,					_snake_case=32							,					_snake_case=1							,					_snake_case=0							,					_snake_case=2							,					**_snake_case							,					):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case							,					bos_token_id=_snake_case							,					eos_token_id=_snake_case							,					**_snake_case    )

  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   layer_norm_eps
  lowerCAmelCase													=   relative_attention_num_buckets





