




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


_lowerCamelCase							=      logging.getLogger(__name__)


@dataclass
class        UpperCAmelCase__				:



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



@dataclass
class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    field(
         metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()				)}				)
     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}				)
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    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."
             )
         }      ,		)
     _SCREAMING_SNAKE_CASE      :					List[str]     					=    field(
         default=lowercase__      ,		metadata={"help": "Overwrite the cached training and evaluation sets"}				)





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[InputFeatures]      )->			Optional[Any]:

       """simple docstring"""




       a			   =default_data_collator(_snake_case      )
       a			   =batch.pop("""pairID"""   ,       _snake_case      )
       return batch





def 							lowerCamelCase    (							)->			List[str]:

       """simple docstring"""




       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"""   ,       _snake_case      )

       # 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=_snake_case   ,       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=_snake_case   ,       cache_dir=model_args.cache_dir   ,       )

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

       # Initialize our Trainer
       a			   =Trainer(
           model=_snake_case   ,       args=_snake_case   ,       train_dataset=_snake_case   ,       eval_dataset=_snake_case   ,       data_collator=_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 ***"""      )

              a			   =trainer.predict(_snake_case      )
              a			   =output.predictions
              a			   =np.argmax(_snake_case   ,       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(_snake_case   ,       """w"""      ) as writer:
                            writer.write("""pairID,gold_label\n"""      )
                            for pid, pred in zip(_snake_case   ,       _snake_case      ):
                                   writer.write("""ex""" + str(_snake_case      ) + """,""" + label_list[int(_snake_case      )] + """\n"""      )

              trainer._log(output.metrics      )





def 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]      )->			Union[str, Any]:

       """simple docstring"""




       main()


if __name__ == "__main__":
    main()

"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()