'''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


_A										= logging.getLogger(__name__)


@dataclass
class    UpperCAmelCase__	:






   """simple docstring"""
   A				:							str			     =     field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
   A				:							Optional[str]			     =     field(
       default=_snake_case       ,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
   A				:							Optional[str]			     =     field(
       default=_snake_case       ,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
   A				:							Optional[str]			     =     field(
       default=_snake_case       ,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,  )



@dataclass
class    UpperCAmelCase__	:






   """simple docstring"""
   A				:							str			     =     field(
       metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()      )}      )
   A				:							str			     =     field(
       metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}      )
   A				:							int			     =     field(
       default=1_28       ,  metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }       ,  )
   A				:							bool			     =     field(
       default=_snake_case       ,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       default_data_collator(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Optional[Any]  					=       batch.pop('pairID'	,							SCREAMING_SNAKE_CASE_				)
     return batch

def _UpperCamelCase (							):
     # 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.

     lowercase_       :							Union[str, Any]  					=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     lowercase_					,lowercase_					,lowercase_       :							Optional[Any]  					=       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:
          lowercase_       :							List[Any]  					=       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.

     lowercase_       :							int  					=       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	,							)
     lowercase_       :							Optional[Any]  					=       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	,							)
     lowercase_       :							Dict  					=       AutoModelForSequenceClassification.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
     lowercase_       :							Union[str, Any]  					=       (
         HansDataset(
             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	,							)
         if training_args.do_train
         else None
     )
     lowercase_       :							Tuple  					=       (
         HansDataset(
             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	,							evaluate=SCREAMING_SNAKE_CASE_	,							)
         if training_args.do_eval
         else None
     )

     # Initialize our Trainer
     lowercase_       :							List[str]  					=       Trainer(
         model=SCREAMING_SNAKE_CASE_	,							args=SCREAMING_SNAKE_CASE_	,							train_dataset=SCREAMING_SNAKE_CASE_	,							eval_dataset=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
     if training_args.do_eval:
          logger.info('*** Evaluate ***'				)

          lowercase_       :							List[Any]  					=       trainer.predict(SCREAMING_SNAKE_CASE_				)
          lowercase_       :							Optional[int]  					=       output.predictions
          lowercase_       :							Optional[int]  					=       np.argmax(SCREAMING_SNAKE_CASE_	,							axis=1				)

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

          trainer._log(output.metrics				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()





'''simple docstring'''




_A										= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_A										= [{'type': 'code', 'content': INSTALL_CONTENT}]
_A										= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




