


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()






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__)


