





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCAmelCase										=					{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase										=					['MBart50Tokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase										=					['MBart50TokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbartaa import MBartaaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
     import sys

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






"""simple docstring"""







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


A     =      logging.getLogger(__name__)






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



def 	lowerCAmelCase__      (  )			->				Optional[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.

							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 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()



