





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


__lowerCAmelCase			:List[Any]	 =     logging.getLogger(__name__)






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







def  A					(				):
					# 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.

					_snake_case							:       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.
										_snake_case							,			_snake_case							,			_snake_case							:       List[Any]												= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
					else:
										_snake_case							,			_snake_case							,			_snake_case							:       int												= 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"	,	UpperCAmelCase					)

					# 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.

					_snake_case							:       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	,	)
					_snake_case							:       int												= 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=UpperCAmelCase	,	)
					_snake_case							:       List[str]												= AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path	,	from_tf=bool(".ckpt" in model_args.model_name_or_path					)	,	config=UpperCAmelCase	,	cache_dir=model_args.cache_dir	,	)

					# Get datasets
					_snake_case							:       List[Any]												= hasattr(model.config	,	"lang2id"					)
					_snake_case							:       str												= (
					    SquadDataset(
					        UpperCAmelCase	,	tokenizer=UpperCAmelCase	,	is_language_sensitive=UpperCAmelCase	,	cache_dir=model_args.cache_dir					)
					    if training_args.do_train
					    else None
					)
					_snake_case							:       int												= (
					    SquadDataset(
					        UpperCAmelCase	,	tokenizer=UpperCAmelCase	,	mode="dev"	,	is_language_sensitive=UpperCAmelCase	,	cache_dir=model_args.cache_dir	,	)
					    if training_args.do_eval
					    else None
					)

					# Data collator
					_snake_case							:       List[Any]												= DataCollatorWithPadding(UpperCAmelCase	,	pad_to_multiple_of=8					) if training_args.fpaa else None

					# Initialize our Trainer
					_snake_case							:       Dict												= Trainer(
					    model=UpperCAmelCase	,	args=UpperCAmelCase	,	train_dataset=UpperCAmelCase	,	eval_dataset=UpperCAmelCase	,	data_collator=UpperCAmelCase	,	)

					# 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					(				UpperCAmelCase					):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

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


__lowerCAmelCase			:Optional[int]	 =     {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Dict	 =     ['MobileNetV1FeatureExtractor']
							__lowerCAmelCase			:List[Any]	 =     ['MobileNetV1ImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Optional[int]	 =     [
							    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MobileNetV1ForImageClassification',
							    'MobileNetV1Model',
							    'MobileNetV1PreTrainedModel',
							    'load_tf_weights_in_mobilenet_v1',
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							__lowerCAmelCase			:Optional[int]	 =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)