import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase__    							= logging.getLogger(__name__)




def        lowerCAmelCase_			(					__A,  __A							)					->    str:







      '''simple docstring'''


      return (preds == labels).mean()





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





@dataclass
class 					A							:
   __UpperCAmelCase			:			str              =      field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()					)}					)
   __UpperCAmelCase			:			str              =      field(metadata={'help': 'Should contain the data files for the task.'}					)
   __UpperCAmelCase			:			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.'
           )
       }		,    )
   __UpperCAmelCase			:			bool              =      field(
       default=_a		,    metadata={'help': 'Overwrite the cached training and evaluation sets'}					)




def        lowerCAmelCase_			(					)					->    Dict:







      '''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",  a__							)

      # Set seed
      set_seed(training_args.seed							)

      try:
            UpperCAmelCase__								=  processors[data_args.task_name]()
            UpperCAmelCase__								=  processor.get_labels()
            UpperCAmelCase__								=  len(a__							)
      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__								=  AutoModelForMultipleChoice.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__								=  (
          MultipleChoiceDataset(
              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,  mode=Split.train,  )
          if training_args.do_train
          else None
      )
      UpperCAmelCase__								=  (
          MultipleChoiceDataset(
              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,  mode=Split.dev,  )
          if training_args.do_eval
          else None
      )

      def compute_metrics(__A							) -> Dict:
            UpperCAmelCase__								=  np.argmax(p.predictions,  axis=1							)
            return {"acc": simple_accuracy(a__,  p.label_ids							)}

      # Data collator
      UpperCAmelCase__								=  DataCollatorWithPadding(a__,  pad_to_multiple_of=8							) if training_args.fpaa else None

      # Initialize our Trainer
      UpperCAmelCase__								=  Trainer(
          model=a__,  args=a__,  train_dataset=a__,  eval_dataset=a__,  compute_metrics=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
      UpperCAmelCase__								=  {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"							)

            UpperCAmelCase__								=  trainer.evaluate()

            UpperCAmelCase__								=  os.path.join(training_args.output_dir,  "eval_results.txt"							)
            if trainer.is_world_master():
                  with open(a__,  "w"							) as writer:
                        logger.info("***** Eval results *****"							)
                        for key, value in result.items():
                              logger.info("  %s = %s",  a__,  a__							)
                              writer.write("%s = %s\n" % (key, value)							)

                        results.update(a__							)

      return results




def        lowerCAmelCase_			(					__A							)					->    List[Any]:







      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()




import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    "compression_format, is_archive",  [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ],  )
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  )					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          "7z": (seven_zip_file, SevenZipExtractor),
          "bz2": (bza_file, BzipaExtractor),
          "gzip": (gz_file, GzipExtractor),
          "lz4": (lza_file, LzaExtractor),
          "tar": (tar_file, TarExtractor),
          "xz": (xz_file, XzExtractor),
          "zip": (zip_file, ZipExtractor),
          "zstd": (zstd_file, ZstdExtractor),
      }
      UpperCAmelCase__		,				UpperCAmelCase__								=  input_paths_and_base_extractors[compression_format]
      if input_path is None:
            UpperCAmelCase__								=  f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__A							)
      assert base_extractor.is_extractable(__A							)
      UpperCAmelCase__								=  tmp_path / ("extracted" if is_archive else "extracted.txt")
      base_extractor.extract(__A,  __A							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  UpperCAmelCase__								=  file_path.read_text(encoding="utf-8"							)
      else:
            UpperCAmelCase__								=  output_path.read_text(encoding="utf-8"							)
      UpperCAmelCase__								=  text_file.read_text(encoding="utf-8"							)
      assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    "compression_format, is_archive",  [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ],  )
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          "7z": seven_zip_file,
          "bz2": bza_file,
          "gzip": gz_file,
          "lz4": lza_file,
          "tar": tar_file,
          "xz": xz_file,
          "zip": zip_file,
          "zstd": zstd_file,
      }
      UpperCAmelCase__								=  input_paths[compression_format]
      if input_path is None:
            UpperCAmelCase__								=  f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__A							)
      UpperCAmelCase__								=  Extractor.infer_extractor_format(__A							)
      assert extractor_format is not None
      UpperCAmelCase__								=  tmp_path / ("extracted" if is_archive else "extracted.txt")
      Extractor.extract(__A,  __A,  __A							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  UpperCAmelCase__								=  file_path.read_text(encoding="utf-8"							)
      else:
            UpperCAmelCase__								=  output_path.read_text(encoding="utf-8"							)
      UpperCAmelCase__								=  text_file.read_text(encoding="utf-8"							)
      assert extracted_file_content == expected_file_content




@pytest.fixture
def        lowerCAmelCase_			(					__A,  __A							)					->    List[str]:







      '''simple docstring'''


      import tarfile

      UpperCAmelCase__								=  tmp_path / "data_dot_dot"
      directory.mkdir()
      UpperCAmelCase__								=  directory / "tar_file_with_dot_dot.tar"
      with tarfile.TarFile(__A,  "w"							) as f:
            f.add(__A,  arcname=os.path.join("..",  text_file.name							)							)
      return path




@pytest.fixture
def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      import tarfile

      UpperCAmelCase__								=  tmp_path / "data_sym_link"
      directory.mkdir()
      UpperCAmelCase__								=  directory / "tar_file_with_sym_link.tar"
      os.symlink("..",  directory / "subdir",  target_is_directory=__A							)
      with tarfile.TarFile(__A,  "w"							) as f:
            f.add(str(directory / "subdir"							),  arcname="subdir"							)  # str required by os.readlink on Windows and Python < 3.8
      return path




@pytest.mark.parametrize(
    "insecure_tar_file, error_log",  [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")],  )
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          "tar_file_with_dot_dot": tar_file_with_dot_dot,
          "tar_file_with_sym_link": tar_file_with_sym_link,
      }
      UpperCAmelCase__								=  insecure_tar_files[insecure_tar_file]
      UpperCAmelCase__								=  tmp_path / "extracted"
      TarExtractor.extract(__A,  __A							)
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg







def        lowerCAmelCase_			(					__A							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  tmpdir / "not_a_zip_file"
      # From: https://github.com/python/cpython/pull/5053
      UpperCAmelCase__								=  (
          B"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
          B"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
          B"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
          B"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
      )
      with not_a_zip_file.open("wb"							) as f:
            f.write(__A							)
      assert zipfile.is_zipfile(str(__A							)							)  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(__A							)  # but we're right



