






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__            =	logging.getLogger(__name__)






@dataclass
class 	lowercase_      :






      '''simple docstring'''




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






@dataclass
class 	lowercase_      :






      '''simple docstring'''




      __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_28				,						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=lowercase				,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )
def 		_a					(   a				:List[InputFeatures]				)							->    Dict[str, torch.Tensor]:
 a							=   default_data_collator(a				)
 a							=   batch.pop('''pairID'''			,							a				)
 return batch
def 		_a					(   )							->    int:
 # 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.

 a							=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
 a						,  a						,  a							=   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'''			,							a				)

 # Set seed
 set_seed(training_args.seed				)

 try:
  a							=   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.

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

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

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

 # 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 ***'''				)

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

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

  trainer._log(output.metrics				)
def 		_a					(   a				:str				)							->    Optional[Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()










import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						['''image_processor''', '''tokenizer''']
      __snake_case  						=						'''AutoImageProcessor'''
      __snake_case  						=						'''AutoTokenizer'''







      def __init__(   self   :		Any				,      __UpperCAmelCase   :		Union[str, Any]=None				,      __UpperCAmelCase   :		int=None				,      **__UpperCAmelCase   :		Union[str, Any]						)					->Dict:




       """simple docstring"""


       a							=   None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''				,      __UpperCAmelCase				,      )
        a							=   kwargs.pop('''feature_extractor'''						)

       a							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('''You have to specify an image_processor.'''						)
       if tokenizer is None:
        raise ValueError('''You have to specify a tokenizer.'''						)

       super().__init__(__UpperCAmelCase				,      __UpperCAmelCase						)
       a							=   self.image_processor







      def __call__(   self   :		List[Any]				,      __UpperCAmelCase   :		Optional[Any]=None				,      __UpperCAmelCase   :		Optional[int]=None				,      __UpperCAmelCase   :		str=None				,      **__UpperCAmelCase   :		List[Any]						)					->Dict:




       """simple docstring"""


       if text is None and images is None:
        raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

       if text is not None:
        a							=   self.tokenizer(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

       if images is not None:
        a							=   self.image_processor(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

       if text is not None and images is not None:
        a							=   image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**__UpperCAmelCase						)				,      tensor_type=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[Any]				,      *__UpperCAmelCase   :		Optional[Any]				,      **__UpperCAmelCase   :		List[str]						)					->Optional[Any]:




       """simple docstring"""


       return self.tokenizer.batch_decode(*__UpperCAmelCase				,      **__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		str				,      *__UpperCAmelCase   :		List[Any]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Union[str, Any]:




       """simple docstring"""


       return self.tokenizer.decode(*__UpperCAmelCase				,      **__UpperCAmelCase						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       a							=   self.tokenizer.model_input_names
       a							=   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->Optional[Any]:




       """simple docstring"""


       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,      __UpperCAmelCase				,      )
       return self.image_processor_class







      @property
      def    __lowerCAmelCase      (   self   :		int						)					->Optional[Any]:




       """simple docstring"""


       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,      __UpperCAmelCase				,      )
       return self.image_processor


