






"""simple docstring"""





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_	:				List[str]										= logging.getLogger(__name__)



def     __lowercase					(    _lowerCamelCase :				Optional[Any]       ,		_lowerCamelCase :				Any )					->					List[Any]:
      return (preds == labels).mean()


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



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



def     __lowercase					(    )					->					Any:
      # 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.

      __snake_case      :    Optional[Any]  			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
      __snake_case			,				__snake_case			,				__snake_case      :    Tuple  			=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'''       ,		__UpperCamelCase )

      # Set seed
      set_seed(training_args.seed )

      try:
            __snake_case      :    int  			=processors[data_args.task_name]()
            __snake_case      :    List[str]  			=processor.get_labels()
            __snake_case      :    Union[str, Any]  			=len(__UpperCamelCase )
      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.

      __snake_case      :    Union[str, Any]  			=AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,		num_labels=__UpperCamelCase       ,		finetuning_task=data_args.task_name       ,		cache_dir=model_args.cache_dir       ,		)
      __snake_case      :    List[str]  			=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       ,		)
      __snake_case      :    Any  			=AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path       ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path )       ,		config=__UpperCamelCase       ,		cache_dir=model_args.cache_dir       ,		)

      # Get datasets
      __snake_case      :    Tuple  			=(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,		tokenizer=__UpperCamelCase       ,		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
      )
      __snake_case      :    str  			=(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,		tokenizer=__UpperCamelCase       ,		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(_lowerCamelCase :				Optional[Any] ) -> Dict:
            __snake_case      :    int  			=np.argmax(p.predictions       ,		axis=1 )
            return {"acc": simple_accuracy(__UpperCamelCase       ,		p.label_ids )}

      # Data collator
      __snake_case      :    List[str]  			=DataCollatorWithPadding(__UpperCamelCase       ,		pad_to_multiple_of=8 ) if training_args.fpaa else None

      # Initialize our Trainer
      __snake_case      :    List[Any]  			=Trainer(
          model=__UpperCamelCase       ,		args=__UpperCamelCase       ,		train_dataset=__UpperCamelCase       ,		eval_dataset=__UpperCamelCase       ,		compute_metrics=__UpperCamelCase       ,		data_collator=__UpperCamelCase       ,		)

      # 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
      __snake_case      :    Union[str, Any]  			={}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***''' )

            __snake_case      :    Optional[Any]  			=trainer.evaluate()

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

                        results.update(__UpperCamelCase )

      return results



def     __lowercase					(    _lowerCamelCase :				List[str] )					->					List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_	:				str										= {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Union[str, Any]										= ["""LayoutXLMTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


