
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_A       =					logging.get_logger(__name__)

_A       =					{
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
   import detectrona







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				Union[str, Any] 	=						"layoutlmv2"





     def __init__(self :       List[Any]							,       _A :       Optional[int]=3_0_5_2_2							,       _A :       Optional[Any]=7_6_8							,       _A :       Any=1_2							,       _A :       List[Any]=1_2							,       _A :       Dict=3_0_7_2							,       _A :       Dict="gelu"							,       _A :       Any=0.1							,       _A :       Optional[int]=0.1							,       _A :       str=5_1_2							,       _A :       int=2							,       _A :       Any=0.02							,       _A :       Dict=1E-12							,       _A :       Dict=0							,       _A :       str=1_0_2_4							,       _A :       Tuple=1_2_8							,       _A :       Optional[int]=3_2							,       _A :       int=True							,       _A :       Dict=2_5_6							,       _A :       Tuple=6_4							,       _A :       List[Any]=True							,       _A :       Tuple=[7, 7, 2_5_6]							,       _A :       Tuple=1_2_8							,       _A :       List[str]=1_2_8							,       _A :       List[Any]=True							,       _A :       Any=True							,       _A :       Union[str, Any]=False							,       _A :       Any=None							,       **_A :       Any							,       )		->		Optional[Any]:
         super().__init__(
             vocab_size=_A							,       hidden_size=_A							,       num_hidden_layers=_A							,       num_attention_heads=_A							,       intermediate_size=_A							,       hidden_act=_A							,       hidden_dropout_prob=_A							,       attention_probs_dropout_prob=_A							,       max_position_embeddings=_A							,       type_vocab_size=_A							,       initializer_range=_A							,       layer_norm_eps=_A							,       pad_token_id=_A							,       **_A							,       )
         snake_case						=   max_ad_position_embeddings
         snake_case						=   max_rel_pos
         snake_case						=   rel_pos_bins
         snake_case						=   fast_qkv
         snake_case						=   max_rel_ad_pos
         snake_case						=   rel_ad_pos_bins
         snake_case						=   convert_sync_batchnorm
         snake_case						=   image_feature_pool_shape
         snake_case						=   coordinate_size
         snake_case						=   shape_size
         snake_case						=   has_relative_attention_bias
         snake_case						=   has_spatial_attention_bias
         snake_case						=   has_visual_segment_embedding
         snake_case						=   (
             detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
         )





     @classmethod
     def 						UpperCAmelCase(self :       Optional[Any]  )		->		List[str]:
         return {
             "MODEL.MASK_ON": True,
             "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
             "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
             "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
             "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
             "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
             "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
             "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
             "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
             "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
             "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
             "MODEL.ROI_HEADS.NUM_CLASSES": 5,
             "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
             "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
             "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
             "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 1_4,
             "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
             "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
             "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
             "MODEL.RESNETS.DEPTH": 1_0_1,
             "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
             "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
             "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
             "MODEL.RESNETS.NUM_GROUPS": 3_2,
             "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
             "MODEL.RESNETS.STRIDE_IN_1X1": False,
         }





     def 						UpperCAmelCase(self :       Any  )		->		Dict:
         snake_case						=   detectrona.config.get_cfg()
         for k, v in self.detectrona_config_args.items():
             snake_case						=   k.split("."  )
             snake_case						=   detectrona_config
             for attribute in attributes[:-1]:
                 snake_case						=   getattr(_A							,       _A  )
             setattr(_A							,       attributes[-1]							,       _A  )

         return detectrona_config



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       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     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 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   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						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        snake_case	,				snake_case	,				snake_case						=   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"					,						A__						)

    # 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						=   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						=   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=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

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

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

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # 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 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

