code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  
  import numpy as np
  import torch
  from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class     __a			:
         _lowerCamelCase							:							str       =			field(
             metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,	)
  
  
  
  
  
  
  @dataclass
  class     __a			:
         _lowerCamelCase							:							str       =			field(
             metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()			)}			)
         _lowerCamelCase							:							str       =			field(
             metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
         _lowerCamelCase							:							int       =			field(
             default=128				,	metadata={
                 'help': (
                     'The maximum total input sequence length after tokenization. Sequences longer '
                     'than this will be truncated, sequences shorter will be padded.'
                 )
             }				,	)
         _lowerCamelCase							:							bool       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Overwrite the cached training and evaluation sets'}			)
  
  
  
  def 			__UpperCAmelCase		(		a_			:		List[InputFeatures]				)     ->       Dict[str, torch.Tensor]:
     _UpperCAmelCase   	=					default_data_collator(a_				)
     _UpperCAmelCase   	=					batch.pop("pairID"    ,  a_				)
     return batch
  def 			__UpperCAmelCase		(		)     ->       Any:
     # 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.
  
     _UpperCAmelCase   	=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					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_				)
  
     # Set seed
     set_seed(training_args.seed				)
  
     try:
        _UpperCAmelCase   	=					hans_tasks_num_labels[data_args.task_name]
     except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name)				)
  
     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
  
     _UpperCAmelCase   	=					AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,  num_labels=a_    ,  finetuning_task=data_args.task_name    ,  cache_dir=model_args.cache_dir    ,  )
     _UpperCAmelCase   	=					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    ,  )
     _UpperCAmelCase   	=					AutoModelForSequenceClassification.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
     _UpperCAmelCase   	=					(
         HansDataset(
             data_dir=data_args.data_dir    ,  tokenizer=a_    ,  task=data_args.task_name    ,  max_seq_length=data_args.max_seq_length    ,  overwrite_cache=data_args.overwrite_cache    ,  )
         if training_args.do_train
         else None
     )
     _UpperCAmelCase   	=					(
         HansDataset(
             data_dir=data_args.data_dir    ,  tokenizer=a_    ,  task=data_args.task_name    ,  max_seq_length=data_args.max_seq_length    ,  overwrite_cache=data_args.overwrite_cache    ,  evaluate=a_    ,  )
         if training_args.do_eval
         else None
     )
  
     # Initialize our Trainer
     _UpperCAmelCase   	=					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				)
  
      # Evaluation
     if training_args.do_eval:
        logger.info("*** Evaluate ***"				)
  
        _UpperCAmelCase   	=					trainer.predict(a_				)
        _UpperCAmelCase   	=					output.predictions
        _UpperCAmelCase   	=					np.argmax(a_    ,  axis=1				)
  
        _UpperCAmelCase   	=					[ex.pairID for ex in eval_dataset]
        _UpperCAmelCase   	=					os.path.join(training_args.output_dir    ,  "hans_predictions.txt"				)
        _UpperCAmelCase   	=					eval_dataset.get_labels()
        if trainer.is_world_master():
           with open(a_    ,  "w"				) as writer:
              writer.write("pairID,gold_label\n"				)
              for pid, pred in zip(a_    ,  a_				):
                 writer.write("ex" + str(a_				) + "," + label_list[int(a_				)] + "\n"				)
  
        trainer._log(output.metrics				)
  def 			__UpperCAmelCase		(		a_			:		str				)     ->       Tuple:
     # For xla_spawn (TPUs)
     main()
  
  
  if __name__ == "__main__":
    main()
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
     if "model" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
     if "norm1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
     if "norm2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
     if "norm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
        _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
     if "mha" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
     if "W_q" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
     if "W_k" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
     if "W_v" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
     if "ff.0" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
     if "ff.2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
     if "ff" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
     if "mlm_class" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
     if "mlm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
     if "backbone.backbone.encoders" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
     if "cls" not in orig_key:
        _UpperCAmelCase   	=					"mra." + orig_key
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
     for key in orig_state_dict.copy().keys():
        _UpperCAmelCase   	=					orig_state_dict.pop(a_				)
  
        if ("pooler" in key) or ("sen_class" in key):
           continue
        else:
           _UpperCAmelCase   	=					val
  
     _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
     _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2
  
     return orig_state_dict
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
     _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
     _UpperCAmelCase   	=					MraForMaskedLM(a_				)
  
     _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)
  
     print(model.load_state_dict(a_				)				)
     model.eval()
     model.save_pretrained(a_				)
  
     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Mra model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  