




import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_lowerCAmelCase    : Dict		= threading.Lock()
_lowerCAmelCase    : Optional[logging.Handler]		= None

_lowerCAmelCase    : Union[str, Any]		= {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

_lowerCAmelCase    : str		= logging.WARNING

_lowerCAmelCase    : Any		= True



def     lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							=    os.getenv("DIFFUSERS_VERBOSITY"    ,				_lowerCAmelCase						)
 if env_level_str:
  if env_level_str in log_levels:
   return log_levels[env_level_str]
  else:
   logging.getLogger().warning(
       F'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
       F'''has to be one of: { ", ".join(log_levels.keys()						) }'''						)
 return _default_log_level



def     lowerCAmelCase		(		):




 """simple docstring"""







 return __name__.split("."						)[0]



def     lowerCAmelCase		(		):




 """simple docstring"""







 return logging.getLogger(_get_library_name()						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 global _default_handler

 with _lock:
  if _default_handler:
   # This library has already configured the library root logger.
   return
  UpperCAmelCase__							=    logging.StreamHandler()  # Set sys.stderr as stream.
  UpperCAmelCase__							=    sys.stderr.flush

  # Apply our default configuration to the library root logger.
  UpperCAmelCase__							=    _get_library_root_logger()
  library_root_logger.addHandler(_default_handler						)
  library_root_logger.setLevel(_get_default_logging_level()						)
  UpperCAmelCase__							=    False



def     lowerCAmelCase		(		):




 """simple docstring"""







 global _default_handler

 with _lock:
  if not _default_handler:
   return

  UpperCAmelCase__							=    _get_library_root_logger()
  library_root_logger.removeHandler(_default_handler						)
  library_root_logger.setLevel(logging.NOTSET						)
  UpperCAmelCase__							=    None



def     lowerCAmelCase		(		):




 """simple docstring"""







 return log_levels



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[str] = None						):




 """simple docstring"""







 if name is None:
  UpperCAmelCase__							=    _get_library_name()

 _configure_library_root_logger()
 return logging.getLogger(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 _configure_library_root_logger()
 return _get_library_root_logger().getEffectiveLevel()



def     lowerCAmelCase		(		_lowerCAmelCase				: int						):




 """simple docstring"""







 _configure_library_root_logger()
 _get_library_root_logger().setLevel(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 return set_verbosity(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 return set_verbosity(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 return set_verbosity(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 return set_verbosity(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 _configure_library_root_logger()

 assert _default_handler is not None
 _get_library_root_logger().removeHandler(_default_handler						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 _configure_library_root_logger()

 assert _default_handler is not None
 _get_library_root_logger().addHandler(_default_handler						)



def     lowerCAmelCase		(		_lowerCAmelCase				: logging.Handler						):




 """simple docstring"""







 _configure_library_root_logger()

 assert handler is not None
 _get_library_root_logger().addHandler(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: logging.Handler						):




 """simple docstring"""







 _configure_library_root_logger()

 assert handler is not None and handler not in _get_library_root_logger().handlers
 _get_library_root_logger().removeHandler(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 _configure_library_root_logger()
 UpperCAmelCase__							=    False



def     lowerCAmelCase		(		):




 """simple docstring"""







 _configure_library_root_logger()
 UpperCAmelCase__							=    True



def     lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							=    _get_library_root_logger().handlers

 for handler in handlers:
  UpperCAmelCase__							=    logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"						)
  handler.setFormatter(_lowerCAmelCase						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							=    _get_library_root_logger().handlers

 for handler in handlers:
  handler.setFormatter(_lowerCAmelCase						)



def     lowerCAmelCase		(		self				: Union[str, Any]    ,				*_lowerCAmelCase				: Optional[Any]    ,				**_lowerCAmelCase				: Tuple						):




 """simple docstring"""







 UpperCAmelCase__							=    os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS"    ,				_lowerCAmelCase						)
 if no_advisory_warnings:
  return
 self.warning(*_lowerCAmelCase    ,				**_lowerCAmelCase						)


_lowerCAmelCase    : str		= warning_advice
class       _UpperCamelCase   :
      def __init__(      self :List[str]			,   *lowerCamelCase :List[str]			,   **lowerCamelCase :str   )  ->      str:  # pylint: disable=unused-argument
       UpperCAmelCase__							=    args[0] if args else None
      def __iter__(      self :Union[str, Any]   )  ->      Optional[int]:
       return iter(self._iterator   )
      def __getattr__(      self :str			,   lowerCamelCase :Any   )  ->      Any:







       def empty_fn(*lowerCamelCase :Tuple			,   **lowerCamelCase :Optional[Any]   ):  # pylint: disable=unused-argument
        return

       return empty_fn
      def __enter__(      self :Tuple   )  ->      int:
       return self







      def __exit__(      self :Tuple			,   lowerCamelCase :int			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :int   )  ->      Union[str, Any]:
       return



class       _UpperCamelCase   :
      def __call__(      self :str			,   *lowerCamelCase :Union[str, Any]			,   **lowerCamelCase :Dict   )  ->      str:







       if _tqdm_active:
        return tqdm_lib.tqdm(*__lowerCamelCase			,   **__lowerCamelCase   )
       else:
        return EmptyTqdm(*__lowerCamelCase			,   **__lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str			,   *lowerCamelCase :Optional[Any]			,   **lowerCamelCase :Union[str, Any]   )  ->      Tuple:
       UpperCAmelCase__							=    None
       if _tqdm_active:
        return tqdm_lib.tqdm.set_lock(*__lowerCamelCase			,   **__lowerCamelCase   )







      def 						UpperCAmelCase_    (      self :Any   )  ->      str:







       if _tqdm_active:
        return tqdm_lib.tqdm.get_lock()



_lowerCAmelCase    : Dict		= _tqdm_cls()



def     lowerCAmelCase		(		):




 """simple docstring"""







 global _tqdm_active
 return bool(_tqdm_active						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 global _tqdm_active
 UpperCAmelCase__							=    True



def     lowerCAmelCase		(		):




 """simple docstring"""







 global _tqdm_active
 UpperCAmelCase__							=    False






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def   a_					(_lowerCAmelCase  :   Tuple       )->				List[Any]:
    if "model" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""model."""			,	""""""       )
    if "norm1" in orig_key:
        snake_case:						int         =   orig_key.replace("""norm1"""			,	"""attention.output.LayerNorm"""       )
    if "norm2" in orig_key:
        snake_case:						Tuple         =   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:						Any         =   orig_key.split("""."""       )[0].split("""_"""       )[-1]
        snake_case:						List[Any]         =   orig_key.replace(F"transformer_{layer_num}"			,	F"encoder.layer.{layer_num}"       )
    if "mha.attn" in orig_key:
        snake_case:						List[Any]         =   orig_key.replace("""mha.attn"""			,	"""attention.self"""       )
    if "mha" in orig_key:
        snake_case:						str         =   orig_key.replace("""mha"""			,	"""attention"""       )
    if "W_q" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_q"""			,	"""self.query"""       )
    if "W_k" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_k"""			,	"""self.key"""       )
    if "W_v" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_v"""			,	"""self.value"""       )
    if "ff.0" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""ff.0"""			,	"""intermediate.dense"""       )
    if "ff.2" in orig_key:
        snake_case:						Optional[Any]         =   orig_key.replace("""ff.2"""			,	"""output.dense"""       )
    if "ff" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""ff"""			,	"""output.dense"""       )
    if "mlm_class" in orig_key:
        snake_case:						int         =   orig_key.replace("""mlm.mlm_class"""			,	"""cls.predictions.decoder"""       )
    if "mlm" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""mlm"""			,	"""cls.predictions.transform"""       )
    if "backbone.backbone.encoders" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""backbone.backbone.encoders"""			,	"""encoder.layer"""       )
    if "cls" not in orig_key:
        snake_case:						Dict         =   """mra.""" + orig_key

    return orig_key

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Any       )->				Dict:
    for key in orig_state_dict.copy().keys():
        snake_case:						Tuple         =   orig_state_dict.pop(_lowerCAmelCase       )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            snake_case:						int         =   val

    snake_case:						str         =   orig_state_dict["""cls.predictions.decoder.bias"""]
    snake_case:						List[str]         =   torch.arange(_lowerCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   Any       )->				List[Any]:
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )["""model_state_dict"""]
    snake_case:						Optional[int]         =   MraConfig.from_json_file(_lowerCAmelCase       )
    snake_case:						List[str]         =   MraForMaskedLM(_lowerCAmelCase       )

    snake_case:						Any         =   convert_checkpoint_helper(config.max_position_embeddings			,	_lowerCAmelCase       )

    print(model.load_state_dict(_lowerCAmelCase       )       )
    model.eval()
    model.save_pretrained(_lowerCAmelCase       )

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


if __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, Any]										= 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.'
    )
    __lowerCAmelCase    :  Dict										= parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


