"""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_:       int			     =       logging.getLogger(__name__)






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







def        __a (		):






     '''simple docstring'''



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

     lowercase__								=				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     ,					)
     lowercase__								=				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     ,					)
     lowercase__								=				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
     lowercase__								=				hasattr(model.config     ,					"lang2id"   )
     lowercase__								=				(
         SquadDataset(
             A     ,					tokenizer=A     ,					is_language_sensitive=A     ,					cache_dir=model_args.cache_dir   )
         if training_args.do_train
         else None
     )
     lowercase__								=				(
         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
     lowercase__								=				DataCollatorWithPadding(A     ,					pad_to_multiple_of=8   ) if training_args.fpaa else None

     # Initialize our Trainer
     lowercase__								=				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        __a (		A   ):






     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




