
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_				:							Optional[int]       =    logging.getLogger(__name__)







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





def        __lowercase(							)       ->   Tuple:
       # 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						       =   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'    ,__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.

       __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=__snake_case    ,)
       __snake_case						       =   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
       __snake_case						       =   hasattr(model.config    ,'lang2id'					)
       __snake_case						       =   (
           SquadDataset(
               __snake_case    ,tokenizer=__snake_case    ,is_language_sensitive=__snake_case    ,cache_dir=model_args.cache_dir					)
           if training_args.do_train
           else None
       )
       __snake_case						       =   (
           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
       __snake_case						       =   DataCollatorWithPadding(__snake_case    ,pad_to_multiple_of=8					) if training_args.fpaa else None

       # Initialize our Trainer
       __snake_case						       =   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        __lowercase(							__snake_case				:		Union[str, Any]					)       ->   Union[str, Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
