

'''simple docstring'''
import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCamelCase				:   List[Any]      				=  get_logger(__name__)


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  Optional[Any]       )   ->    Dict:
     """simple docstring"""




     if text.startswith(__snake_case       ):
          return text[len(__snake_case       ) :]
     return text  # or whatever


def  _lowerCAmelCase      (		_UpperCamelCase		:  Union[str, Any]       )   ->    Dict:
     """simple docstring"""




     return {remove_prefix(__snake_case							,				'model.'       ): v for k, v in sd.items()}


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]       )   ->    Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												={}
     for k in state_dicts[0].keys():
          _SCREAMING_SNAKE_CASE												=[sd[k] for sd in state_dicts]
          _SCREAMING_SNAKE_CASE												=sum(__snake_case       ) / len(__snake_case       )
          assert isinstance(__snake_case							,				torch.Tensor       )
          _SCREAMING_SNAKE_CASE												=new_t
     return new_sd


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[int]       )   ->    None:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=AutoModelForSeqaSeqLM.from_pretrained(__snake_case       )
     if os.path.isfile(__snake_case       ):
          _SCREAMING_SNAKE_CASE												=[pl_ckpt_path]
     else:
          assert os.path.isdir(__snake_case       )
          _SCREAMING_SNAKE_CASE												=list(Path(__snake_case       ).glob('*.ckpt'       )       )
          assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

     if len(__snake_case       ) > 1:
          logger.info(f"averaging the weights of {ckpt_files}"       )

     _SCREAMING_SNAKE_CASE												=[sanitize(torch.load(__snake_case							,				map_location='cpu'       )['state_dict']       ) for x in ckpt_files]
     _SCREAMING_SNAKE_CASE												=average_state_dicts(__snake_case       )

     _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=hf_model.load_state_dict(__snake_case							,				strict=__snake_case       )
     assert not missing, f"missing keys: {missing}"
     hf_model.save_pretrained(__snake_case       )
     try:
          _SCREAMING_SNAKE_CASE												=AutoTokenizer.from_pretrained(__snake_case       )
          tok.save_pretrained(__snake_case       )
     except Exception:
          pass
          # dont copy tokenizer if cant


if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)





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


def  _lowerCAmelCase      (		)   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_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.
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_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'							,				_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.

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

     # Initialize our Trainer
     _SCREAMING_SNAKE_CASE												=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  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple       )   ->    List[str]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
