

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_UpperCAmelCase    	=			{
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCAmelCase    	=			[
      "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RwkvForCausalLM",
      "RwkvModel",
      "RwkvPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rwkv import (
        RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
        RwkvForCausalLM,
        RwkvModel,
        RwkvPreTrainedModel,
    )
else:
  import sys

  _UpperCAmelCase    	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_UpperCAmelCase    	=			logging.getLogger(__name__)



@dataclass
class        _UpperCAmelCase :
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				str					      =				field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
  SCREAMING_SNAKE_CASE							:				bool					      =				field(default=__lowercase     ,				metadata={'''help''': '''Set this flag to use fast tokenization.'''}			)
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,				)


def 			__UpperCamelCase    ()     ->  Tuple:
      # 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.

      A    =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

      if len(sys.argv     ) == 2 and sys.argv[1].endswith('.json'     ):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            A ,	A ,	A    =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            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',    lowerCAmelCase     )

      # Prepare Question-Answering task
      # 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,    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,    use_fast=lowerCAmelCase,    )
      A    =				AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path,    from_tf=bool('.ckpt' in model_args.model_name_or_path     ),    config=lowerCAmelCase,    cache_dir=model_args.cache_dir,    )

      # Get datasets
      A    =				hasattr(model.config,    'lang2id'     )
      A    =				(
          SquadDataset(
              lowerCAmelCase,    tokenizer=lowerCAmelCase,    is_language_sensitive=lowerCAmelCase,    cache_dir=model_args.cache_dir     )
          if training_args.do_train
          else None
      )
      A    =				(
          SquadDataset(
              lowerCAmelCase,    tokenizer=lowerCAmelCase,    mode='dev',    is_language_sensitive=lowerCAmelCase,    cache_dir=model_args.cache_dir,    )
          if training_args.do_eval
          else None
      )

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

      # Initialize our Trainer
      A    =				Trainer(
          model=lowerCAmelCase,    args=lowerCAmelCase,    train_dataset=lowerCAmelCase,    eval_dataset=lowerCAmelCase,    data_collator=lowerCAmelCase,    )

      # 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     )


def 			__UpperCamelCase    (lowerCAmelCase					:  Tuple     )     ->  List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()



