


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




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






def 						_A					() ->	Optional[Any]:




     '''simple docstring'''



     _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         =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          _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'					,    lowercase__		)

     # 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=lowercase__					,    )
     _a         =				AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path					,    from_tf=bool('.ckpt' in model_args.model_name_or_path		)					,    config=lowercase__					,    cache_dir=model_args.cache_dir					,    )

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

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

     # Initialize our Trainer
     _a         =				Trainer(
         model=lowercase__					,    args=lowercase__					,    train_dataset=lowercase__					,    eval_dataset=lowercase__					,    data_collator=lowercase__					,    )

     # 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 						_A					(lowerCAmelCase__	:Any		) ->	str:




     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

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


