code: 
  
  
  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
  
  
  SCREAMING_SNAKE_CASE : int          =    logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class 					snake_case__  :
  	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   :							bool         = 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.
  	UpperCAmelCase   :							Optional[str]         = field(
  	    default=__A      ,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,    )
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[int]			=	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_       :   Dict			=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
  							else:
  														a_		,				a_		,				a_       :   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"""	,	SCREAMING_SNAKE_CASE_      )
  
  							# 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_       :   Optional[Any]			=	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_       :   Any			=	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=SCREAMING_SNAKE_CASE_	,	)
  							a_       :   Any			=	AutoModelForQuestionAnswering.from_pretrained(
  							    model_args.model_name_or_path	,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )	,	config=SCREAMING_SNAKE_CASE_	,	cache_dir=model_args.cache_dir	,	)
  
  							# Get datasets
  							a_       :   Optional[Any]			=	hasattr(model.config	,	"""lang2id"""      )
  							a_       :   Any			=	(
  							    SquadDataset(
  							        SCREAMING_SNAKE_CASE_	,	tokenizer=SCREAMING_SNAKE_CASE_	,	is_language_sensitive=SCREAMING_SNAKE_CASE_	,	cache_dir=model_args.cache_dir      )
  							    if training_args.do_train
  							    else None
  							)
  							a_       :   Dict			=	(
  							    SquadDataset(
  							        SCREAMING_SNAKE_CASE_	,	tokenizer=SCREAMING_SNAKE_CASE_	,	mode="""dev"""	,	is_language_sensitive=SCREAMING_SNAKE_CASE_	,	cache_dir=model_args.cache_dir	,	)
  							    if training_args.do_eval
  							    else None
  							)
  
  							# Data collator
  							a_       :   Any			=	DataCollatorWithPadding(SCREAMING_SNAKE_CASE_	,	pad_to_multiple_of=8      ) if training_args.fpaa else None
  
  							# Initialize our Trainer
  							a_       :   Optional[int]			=	Trainer(
  							    model=SCREAMING_SNAKE_CASE_	,	args=SCREAMING_SNAKE_CASE_	,	train_dataset=SCREAMING_SNAKE_CASE_	,	eval_dataset=SCREAMING_SNAKE_CASE_	,	data_collator=SCREAMING_SNAKE_CASE_	,	)
  
  							# 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		(							SCREAMING_SNAKE_CASE_				: List[Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							main()
  
  
  if __name__ == "__main__":
  	main()
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  