




'''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'''







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

@dataclass
class 						lowerCAmelCase_   :







  '''simple docstring'''







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

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[InputFeatures]       ):
 '''simple docstring'''





 UpperCAmelCase__										=    default_data_collator(SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    batch.pop("""pairID"""       ,						SCREAMING_SNAKE_CASE__       )
 return batch

def    _UpperCamelCase      (				):
 '''simple docstring'''





 UpperCAmelCase__										=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
 UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    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:
  UpperCAmelCase__										=    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.

 UpperCAmelCase__										=    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       ,						)
 UpperCAmelCase__										=    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       ,						)
 UpperCAmelCase__										=    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
 UpperCAmelCase__										=    (
     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
 )
 UpperCAmelCase__										=    (
     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
 UpperCAmelCase__										=    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 ***"""       )

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

  UpperCAmelCase__										=    [ex.pairID for ex in eval_dataset]
  UpperCAmelCase__										=    os.path.join(training_args.output_dir       ,						"""hans_predictions.txt"""       )
  UpperCAmelCase__										=    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__			:      Union[str, Any]       ):
 '''simple docstring'''





 main()


if __name__ == "__main__":
    main()






'''simple docstring'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






   for i in range(1						,		len(_UpperCAmelCase )						,		2 ):
    iterlist[i] *= -1
   return iterlist


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

   # algorithm for creating a new shuffled list, keys_l, out of key_list_options
   for i in key_list_options:
    temp_list.extend(_UpperCAmelCase )

    # checking breakpoints at which to pivot temporary sublist and add it into
    # keys_l
    if i in breakpoints or i == key_list_options[-1]:
     keys_l.extend(temp_list[::-1] )
     temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
   return keys_l


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # decoding shift like Caesar cipher algorithm implementing negative shift or
   # reverse shift or left shift
   for i in encoded_message:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    decoded_message += self.__key_list[
        (position - self.__shift_key) % -len(self.__key_list )
    ]

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # encoding shift like Caesar cipher algorithm implementing positive shift or
   # forward shift or right shift
   for i in plaintext:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    encoded_message += self.__key_list[
        (position + self.__shift_key) % len(self.__key_list )
    ]

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
