





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


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







@dataclass
class        __snake_case			:





       '''simple docstring'''







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







def      A__				(       )  ->       List[str]:
  # 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:		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.
    __snake_case    ,  __snake_case    ,  __snake_case:		int					    =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
  else:
    __snake_case    ,  __snake_case    ,  __snake_case:		Union[str, Any]					    =		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"""   ,   SCREAMING_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:		int					    =		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:		List[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=SCREAMING_SNAKE_CASE__   ,   )
  __snake_case:		Union[str, Any]					    =		AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path   ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path)   ,   config=SCREAMING_SNAKE_CASE__   ,   cache_dir=model_args.cache_dir   ,   )

  # Get datasets
  __snake_case:		Optional[int]					    =		hasattr(model.config   ,   """lang2id""")
  __snake_case:		Dict					    =		(
      SquadDataset(
          SCREAMING_SNAKE_CASE__   ,   tokenizer=SCREAMING_SNAKE_CASE__   ,   is_language_sensitive=SCREAMING_SNAKE_CASE__   ,   cache_dir=model_args.cache_dir)
      if training_args.do_train
      else None
  )
  __snake_case:		Dict					    =		(
      SquadDataset(
          SCREAMING_SNAKE_CASE__   ,   tokenizer=SCREAMING_SNAKE_CASE__   ,   mode="""dev"""   ,   is_language_sensitive=SCREAMING_SNAKE_CASE__   ,   cache_dir=model_args.cache_dir   ,   )
      if training_args.do_eval
      else None
  )

  # Data collator
  __snake_case:		Dict					    =		DataCollatorWithPadding(SCREAMING_SNAKE_CASE__   ,   pad_to_multiple_of=8) if training_args.fpaa else None

  # Initialize our Trainer
  __snake_case:		Optional[Any]					    =		Trainer(
      model=SCREAMING_SNAKE_CASE__   ,   args=SCREAMING_SNAKE_CASE__   ,   train_dataset=SCREAMING_SNAKE_CASE__   ,   eval_dataset=SCREAMING_SNAKE_CASE__   ,   data_collator=SCREAMING_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      A__				(       SCREAMING_SNAKE_CASE__)  ->       Any:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()













import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  if "model" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""model."""   ,   """""")
  if "norm1" in orig_key:
    __snake_case:		Tuple					    =		orig_key.replace("""norm1"""   ,   """attention.output.LayerNorm""")
  if "norm2" in orig_key:
    __snake_case:		Any					    =		orig_key.replace("""norm2"""   ,   """output.LayerNorm""")
  if "norm" in orig_key:
    __snake_case:		List[str]					    =		orig_key.replace("""norm"""   ,   """LayerNorm""")
  if "transformer" in orig_key:
    __snake_case:		str					    =		orig_key.split(""".""")[0].split("""_""")[-1]
    __snake_case:		int					    =		orig_key.replace(F'''transformer_{layer_num}'''   ,   F'''encoder.layer.{layer_num}''')
  if "mha.attn" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""mha.attn"""   ,   """attention.self""")
  if "mha" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mha"""   ,   """attention""")
  if "W_q" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""W_q"""   ,   """self.query""")
  if "W_k" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""W_k"""   ,   """self.key""")
  if "W_v" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""W_v"""   ,   """self.value""")
  if "ff1" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""ff1"""   ,   """intermediate.dense""")
  if "ff2" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""ff2"""   ,   """output.dense""")
  if "ff" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""ff"""   ,   """output.dense""")
  if "mlm_class" in orig_key:
    __snake_case:		Union[str, Any]					    =		orig_key.replace("""mlm.mlm_class"""   ,   """cls.predictions.decoder""")
  if "mlm" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mlm"""   ,   """cls.predictions.transform""")
  if "cls" not in orig_key:
    __snake_case:		Dict					    =		"""nystromformer.""" + orig_key

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      __snake_case:		Tuple					    =		val

  __snake_case:		str					    =		orig_state_dict["""cls.predictions.decoder.bias"""]
  __snake_case:		int					    =		(
      torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
  )

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		convert_checkpoint_helper(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  model.load_state_dict(SCREAMING_SNAKE_CASE__)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

  print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''')


if __name__ == "__main__":
      __UpperCAmelCase			:  str										=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The json file for Nystromformer model config.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      __UpperCAmelCase			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






