code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import os
  import random
  from pathlib import Path
  from typing import List
  
  import numpy as np
  import torch
  from torch.cuda.amp import GradScaler
  
  from .utils import (
      MODEL_NAME,
      OPTIMIZER_NAME,
      RNG_STATE_NAME,
      SCALER_NAME,
      SCHEDULER_NAME,
      get_pretty_name,
      is_tpu_available,
      is_xpu_available,
      save,
  )
  
  
  if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
  
  from .logging import get_logger
  from .state import PartialState
  
  
  SCREAMING_SNAKE_CASE_     				=   get_logger(__name__)
  def 			__UpperCAmelCase		(		a_			:		str    ,  a_			:		List[dict]    ,  a_			:		list    ,  a_			:		list    ,  a_			:		int    ,  a_			:		GradScaler = None    ,  )     ->       Dict:
     # Model states
     for i, state in enumerate(a_				):
        _UpperCAmelCase   	=					F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        save(a_    ,  a_				)
        logger.info(F'''Model weights saved in {output_model_file}'''				)
     # Optimizer states
     for i, opt in enumerate(a_				):
        _UpperCAmelCase   	=					opt.state_dict()
        _UpperCAmelCase   	=					F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        save(a_    ,  a_				)
        logger.info(F'''Optimizer state saved in {output_optimizer_file}'''				)
     # Scheduler states
     for i, scheduler in enumerate(a_				):
        _UpperCAmelCase   	=					scheduler.state_dict()
        _UpperCAmelCase   	=					F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        save(a_    ,  a_				)
        logger.info(F'''Scheduler state saved in {output_scheduler_file}'''				)
     # GradScaler state
     if scaler is not None:
        _UpperCAmelCase   	=					scaler.state_dict()
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        torch.save(a_    ,  a_				)
        logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''				)
     # Random number generator states
     _UpperCAmelCase   	=					{}
     _UpperCAmelCase   	=					F'''{RNG_STATE_NAME}_{process_index}.pkl'''
     _UpperCAmelCase   	=					random.getstate()
     _UpperCAmelCase   	=					np.random.get_state()
     _UpperCAmelCase   	=					torch.get_rng_state()
     if is_xpu_available():
        _UpperCAmelCase   	=					torch.xpu.get_rng_state_all()
     else:
        _UpperCAmelCase   	=					torch.cuda.get_rng_state_all()
     if is_tpu_available():
        _UpperCAmelCase   	=					xm.get_rng_state()
     _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
     torch.save(a_    ,  a_				)
     logger.info(F'''Random states saved in {output_states_file}'''				)
     return output_dir
  def 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		int    ,  a_			:		Any    ,  a_			:		Tuple    ,  a_			:		Optional[Any]    ,  a_			:		Tuple=None    ,  a_			:		Union[str, Any]=None    ,  **a_			:		Dict    ,  )     ->       List[Any]:
     if map_location not in [None, "cpu", "on_device"]:
        raise TypeError(
            "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"				)
     if map_location is None:
        _UpperCAmelCase   	=					"cpu"
     elif map_location == "on_device":
        _UpperCAmelCase   	=					PartialState().device
     # Model states
     for i, model in enumerate(a_				):
        _UpperCAmelCase   	=					F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        models[i].load_state_dict(torch.load(a_    ,  map_location=a_				)    ,  **a_				)
     logger.info("All model weights loaded successfully"				)
  
     # Optimizer states
     for i, opt in enumerate(a_				):
        _UpperCAmelCase   	=					F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        _UpperCAmelCase   	=					torch.load(a_    ,  map_location=a_				)
        optimizers[i].load_state_dict(a_				)
     logger.info("All optimizer states loaded successfully"				)
  
     # Scheduler states
     for i, scheduler in enumerate(a_				):
        _UpperCAmelCase   	=					F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        scheduler.load_state_dict(torch.load(a_				)				)
     logger.info("All scheduler states loaded successfully"				)
  
     # GradScaler state
     if scaler is not None:
        _UpperCAmelCase   	=					os.path.join(a_    ,  a_				)
        scaler.load_state_dict(torch.load(a_				)				)
        logger.info("GradScaler state loaded successfully"				)
  
     # Random states
     try:
        _UpperCAmelCase   	=					torch.load(os.path.join(a_    ,  F'''{RNG_STATE_NAME}_{process_index}.pkl'''				)				)
        random.setstate(states["random_state"]				)
        np.random.set_state(states["numpy_random_seed"]				)
        torch.set_rng_state(states["torch_manual_seed"]				)
        if is_xpu_available():
           torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"]				)
        else:
           torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"]				)
        if is_tpu_available():
           xm.set_rng_state(states["xm_seed"]				)
        logger.info("All random states loaded successfully"				)
     except Exception:
        logger.info("Could not load random states"				)
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		str    ,  a_			:		int = 0				)     ->       str:
     _UpperCAmelCase   	=					Path(a_				) / F'''custom_checkpoint_{index}.pkl'''
     logger.info(F'''Saving the state of {get_pretty_name(a_				)} to {save_location}'''				)
     torch.save(obj.state_dict()    ,  a_				)
  
  
  def 			__UpperCAmelCase		(		a_			:		Tuple    ,  a_			:		List[Any]    ,  a_			:		int = 0				)     ->       Optional[Any]:
     _UpperCAmelCase   	=					F'''{path}/custom_checkpoint_{index}.pkl'''
     logger.info(F'''Loading the state of {get_pretty_name(a_				)} from {load_location}'''				)
     obj.load_state_dict(torch.load(a_    ,  map_location="cpu"				)				)
  
  
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)
  
  