




'''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     =				logging.getLogger(__name__)






@dataclass
class 	lowerCAmelCase_  :







  '''simple docstring'''

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






@dataclass
class 	lowerCAmelCase_  :







  '''simple docstring'''

  UpperCamelCase_ :			str        =							field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()       )}       )
  UpperCamelCase_ :			str        =							field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}       )
  UpperCamelCase_ :			int        =							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."""
          )
      }					,				)
  UpperCamelCase_ :			bool        =							field(
      default=lowercase__					,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )






def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Any:
     A:		int        =		default_data_collator(__lowercase  )
     A:		int        =		batch.pop('''pairID'''		,							__lowercase  )
     return batch
def 				SCREAMING_SNAKE_CASE(      ) ->   Any:
     A:		int        =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
     A:		Union[str, 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'''		,							__lowercase  )

     # Set seed
     set_seed(training_args.seed  )

     try:
          A:		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.

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

     # Get datasets
     A:		Any        =		(
         HansDataset(
             data_dir=data_args.data_dir		,							tokenizer=__lowercase		,							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:		str        =		(
         HansDataset(
             data_dir=data_args.data_dir		,							tokenizer=__lowercase		,							task=data_args.task_name		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							evaluate=__lowercase		,							)
         if training_args.do_eval
         else None
     )

     # Initialize our Trainer
     A:		Dict        =		Trainer(
         model=__lowercase		,							args=__lowercase		,							train_dataset=__lowercase		,							eval_dataset=__lowercase		,							data_collator=__lowercase		,							)

     # 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:		List[str]        =		trainer.predict(__lowercase  )
          A:		str        =		output.predictions
          A:		List[str]        =		np.argmax(__lowercase		,							axis=1  )

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

          trainer._log(output.metrics  )
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   str:
     main()


if __name__ == "__main__":
     main()












'''simple docstring'''


from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Tuple:
     if is_torch_available():
          import torch

          if isinstance(__lowercase		,							torch.Tensor  ):
               return True

     return isinstance(__lowercase		,							np.ndarray  )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def 				_snake_case    (   self							:       Union[str, Any]  ) ->		Dict:







       '''simple docstring'''



       A:		List[str]        =		fields(self  )

       # Safety and consistency checks
       if not len(SCREAMING_SNAKE_CASE_  ):
            raise ValueError(f"""{self.__class__.__name__} has no fields."""  )

       A:		Optional[Any]        =		getattr(self				,     class_fields[0].name  )
       A:		Any        =		all(getattr(self				,     field.name  ) is None for field in class_fields[1:]  )

       if other_fields_are_none and isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
            for key, value in first_field.items():
                 A:		Any        =		value
       else:
            for field in class_fields:
                 A:		List[Any]        =		getattr(self				,     field.name  )
                 if v is not None:
                      A:		int        =		v



  def __delitem__(   self							:       Optional[int]				,     *SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     **SCREAMING_SNAKE_CASE_							:       List[str]  ) ->		List[str]:







       '''simple docstring'''



       raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""  )



  def 				_snake_case    (   self							:       int				,     *SCREAMING_SNAKE_CASE_							:       Optional[Any]				,     **SCREAMING_SNAKE_CASE_							:       Any  ) ->		str:







       '''simple docstring'''



       raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""  )



  def 				_snake_case    (   self							:       int				,     *SCREAMING_SNAKE_CASE_							:       Dict				,     **SCREAMING_SNAKE_CASE_							:       Any  ) ->		Optional[Any]:







       '''simple docstring'''



       raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""  )



  def 				_snake_case    (   self							:       int				,     *SCREAMING_SNAKE_CASE_							:       int				,     **SCREAMING_SNAKE_CASE_							:       Optional[Any]  ) ->		int:







       '''simple docstring'''



       raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance."""  )



  def __getitem__(   self							:       int				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Tuple:







       '''simple docstring'''



       if isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
            A:		Any        =		dict(self.items()  )
            return inner_dict[k]
       else:
            return self.to_tuple()[k]



  def __setattr__(   self							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ) ->		Dict:







       '''simple docstring'''



       if name in self.keys() and value is not None:
            # Don't call self.__setitem__ to avoid recursion errors
            super().__setitem__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
       super().__setattr__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



  def __setitem__(   self							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ) ->		int:







       '''simple docstring'''



       super().__setitem__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )




  def 				_snake_case    (   self							:       Optional[Any]  ) ->		Tuple[Any]:







       '''simple docstring'''



       return tuple(self[k] for k in self.keys()  )






