






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 							__snake_case:
 lowercase			:							str		   =		field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
 lowercase			:							Optional[str]		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
 lowercase			:							Optional[str]		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
 lowercase			:							Optional[str]		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}  ,							)
@dataclass
class 							__snake_case:
 lowercase			:							str		   =		field(
     metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()   )}   )
 lowercase			:							str		   =		field(
     metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}   )
 lowercase			:							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.'''
         )
     }  ,							)
 lowercase			:							bool		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )
def    a__    (	A       ):
   lowercase__    :						int       =			default_data_collator(A       )
   lowercase__    :						Optional[int]       =			batch.pop("""pairID"""		,					A       )
   return batch
def    a__    (	):
   # 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__    :						str       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
   lowercase__  ,       lowercase__  ,       lowercase__    :						int       =			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:
      lowercase__    :						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__    :						Optional[int]       =			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		,					)
   lowercase__    :						int       =			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__    :						Optional[int]       =			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
   lowercase__    :						List[str]       =			(
       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
   )
   lowercase__    :						Any       =			(
       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
   lowercase__    :						str       =			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 ***"""       )

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

      lowercase__    :						Optional[Any]       =			[ex.pairID for ex in eval_dataset]
      lowercase__    :						Any       =			os.path.join(training_args.output_dir		,					"""hans_predictions.txt"""       )
      lowercase__    :						Dict       =			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       ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()










from __future__ import annotations

from typing import Any
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Any						):  # noqa: A002

    """simple docstring"""






    lowercase__    :						Optional[int]       =			item
    lowercase__    :						Union[str, Any]       =			next
class 							__snake_case:



 def __init__(						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Node | None       =			None
    lowercase__    :						Optional[Any]       =			0



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Any       =			Node(UpperCAmelCase__			,		self.head						)
    self.size += 1



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    if self.head is None:
       return None
    else:
       lowercase__    :						List[Any]       =			self.head.item
       lowercase__    :						Optional[Any]       =			self.head.next
       self.size -= 1
       return item



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    return self.head is None



 def __str__(						self : List[str]						):

    """simple docstring"""






    if self.is_empty():
       return ""
    else:
       lowercase__    :						Optional[Any]       =			self.head
       lowercase__    :						str       =			""""""
       lowercase__    :						list[str]       =			[]
       while iterate:
          item_list.append(str(iterate.item						)						)
          lowercase__    :						List[str]       =			iterate.next

       lowercase__    :						List[str]       =			""" --> """.join(UpperCAmelCase__						)

       return item_str







 def __len__(						self : List[Any]						):

    """simple docstring"""






    return self.size


