
"""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


lowerCAmelCase								=						logging.getLogger(__name__)

@dataclass
class    A_     :



							"""simple docstring"""






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






def        lowerCAmelCase_       (		)       ->Dict:
			# 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.

			lowerCamelCase__     :					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.
						lowerCamelCase__     :					Any		=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
			else:
						lowerCamelCase__     :					str		=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'							,   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.

			lowerCamelCase__     :					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							,   )
			lowerCamelCase__     :					Optional[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=snake_case_							,   )
			lowerCamelCase__     :					Any		=AutoModelForQuestionAnswering.from_pretrained(
			    model_args.model_name_or_path							,   from_tf=bool('.ckpt' in model_args.model_name_or_path      )							,   config=snake_case_							,   cache_dir=model_args.cache_dir							,   )

			# Get datasets
			lowerCamelCase__     :					Optional[Any]		=hasattr(model.config							,   'lang2id'      )
			lowerCamelCase__     :					List[Any]		=(
			    SquadDataset(
			        snake_case_							,   tokenizer=snake_case_							,   is_language_sensitive=snake_case_							,   cache_dir=model_args.cache_dir      )
			    if training_args.do_train
			    else None
			)
			lowerCamelCase__     :					Optional[int]		=(
			    SquadDataset(
			        snake_case_							,   tokenizer=snake_case_							,   mode='dev'							,   is_language_sensitive=snake_case_							,   cache_dir=model_args.cache_dir							,   )
			    if training_args.do_eval
			    else None
			)

			# Data collator
			lowerCamelCase__     :					int		=DataCollatorWithPadding(snake_case_							,   pad_to_multiple_of=8      ) if training_args.fpaa else None

			# Initialize our Trainer
			lowerCamelCase__     :					Optional[int]		=Trainer(
			    model=snake_case_							,   args=snake_case_							,   train_dataset=snake_case_							,   eval_dataset=snake_case_							,   data_collator=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_       (		snake_case_		:      str      )       ->str:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""MobileNetV1FeatureExtractor"""]
							lowerCAmelCase								=						["""MobileNetV1ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """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								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)