

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






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

def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	default_data_collator(__A      )
  UpperCAmelCase          =	batch.pop("pairID" ,							__A      )
  return batch
def   _lowerCAmelCase(						):
  # 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.

  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" ,							__A      )

  # 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=__A ,							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=__A ,							cache_dir=model_args.cache_dir ,							)

  # Get datasets
  UpperCAmelCase          =	(
      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
  )
  UpperCAmelCase          =	(
      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
  UpperCAmelCase          =	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 ***"      )

    UpperCAmelCase          =	trainer.predict(__A      )
    UpperCAmelCase          =	output.predictions
    UpperCAmelCase          =	np.argmax(__A ,							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(__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   _lowerCAmelCase(						__A      ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()






def   _lowerCAmelCase(						):

  for a in range(300      ):
    for b in range(a + 1 ,							400      ):
      for c in range(b + 1 ,							500      ):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1
def   _lowerCAmelCase(						):

  for a in range(300      ):
    for b in range(400      ):
      UpperCAmelCase          =	1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1
def   _lowerCAmelCase(						):
  import timeit

  print(
      timeit.timeit("solution()" ,							setup="from __main__ import solution" ,							number=1000      )      )
  print(
      timeit.timeit(
          "solution_fast()" ,							setup="from __main__ import solution_fast" ,							number=1000      )      )


if __name__ == "__main__":
      print(f"{solution() = }")



