

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



def   UpperCamelCase(   lowercase_				)      ->							int:





  '''simple docstring'''





  if "model" in orig_key:
    snake_case_					= orig_key.replace("""model."""     ,   """"""				)
  if "norm1" in orig_key:
    snake_case_					= orig_key.replace("""norm1"""     ,   """attention.output.LayerNorm"""				)
  if "norm2" in orig_key:
    snake_case_					= orig_key.replace("""norm2"""     ,   """output.LayerNorm"""				)
  if "norm" in orig_key:
    snake_case_					= orig_key.replace("""norm"""     ,   """LayerNorm"""				)
  if "transformer" in orig_key:
    snake_case_					= orig_key.split("""."""				)[0].split("""_"""				)[-1]
    snake_case_					= orig_key.replace(f'''transformer_{layer_num}'''     ,   f'''encoder.layer.{layer_num}'''				)
  if "mha.attn" in orig_key:
    snake_case_					= orig_key.replace("""mha.attn"""     ,   """attention.self"""				)
  if "mha" in orig_key:
    snake_case_					= orig_key.replace("""mha"""     ,   """attention"""				)
  if "W_q" in orig_key:
    snake_case_					= orig_key.replace("""W_q"""     ,   """self.query"""				)
  if "W_k" in orig_key:
    snake_case_					= orig_key.replace("""W_k"""     ,   """self.key"""				)
  if "W_v" in orig_key:
    snake_case_					= orig_key.replace("""W_v"""     ,   """self.value"""				)
  if "ff.0" in orig_key:
    snake_case_					= orig_key.replace("""ff.0"""     ,   """intermediate.dense"""				)
  if "ff.2" in orig_key:
    snake_case_					= orig_key.replace("""ff.2"""     ,   """output.dense"""				)
  if "ff" in orig_key:
    snake_case_					= orig_key.replace("""ff"""     ,   """output.dense"""				)
  if "mlm_class" in orig_key:
    snake_case_					= orig_key.replace("""mlm.mlm_class"""     ,   """cls.predictions.decoder"""				)
  if "mlm" in orig_key:
    snake_case_					= orig_key.replace("""mlm"""     ,   """cls.predictions.transform"""				)
  if "backbone.backbone.encoders" in orig_key:
    snake_case_					= orig_key.replace("""backbone.backbone.encoders"""     ,   """encoder.layer"""				)
  if "cls" not in orig_key:
    snake_case_					= """mra.""" + orig_key

  return orig_key



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Dict:





  '''simple docstring'''





  for key in orig_state_dict.copy().keys():
    snake_case_					= orig_state_dict.pop(_A				)

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

  snake_case_					= orig_state_dict["""cls.predictions.decoder.bias"""]
  snake_case_					= torch.arange(_A				).expand((1, -1)				) + 2

  return orig_state_dict



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_				)      ->							Any:





  '''simple docstring'''





  snake_case_					= torch.load(_A     ,   map_location="""cpu"""				)["""model_state_dict"""]
  snake_case_					= MraConfig.from_json_file(_A				)
  snake_case_					= MraForMaskedLM(_A				)

  snake_case_					= 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__":
    lowerCamelCase_					  =   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_					  =   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

UpperCAmelCase__			:  int					     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def        __lowercase   (  cls      )  ->    Any:
        return cls






def        __lowercase   (  _A    ,     _A = None    ,     _A = None    ,     _A = None    ,     _A = None    ,     )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = device
      SCREAMING_SNAKE_CASE			:   Tuple	  = shape[0]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = layout or torch.strided
      SCREAMING_SNAKE_CASE			:   Dict	  = device or torch.device("""cpu"""      )

      if generator is not None:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = generator.device.type if not isinstance(_A    ,     _A      ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = """cpu"""
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."      )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."      )

      if isinstance(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   int	  = (1,) + shape[1:]
            SCREAMING_SNAKE_CASE			:   Any	  = [
                torch.randn(_A    ,     generator=generator[i]    ,     device=_A    ,     dtype=_A    ,     layout=_A      )
                for i in range(_A      )
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(_A    ,     dim=0      ).to(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = torch.randn(_A    ,     generator=_A    ,     device=_A    ,     dtype=_A    ,     layout=_A      ).to(_A      )

      return latents







def        __lowercase   (  _A      )  ->    Dict:
      if is_torch_version("""<"""    ,     """2.0.0"""      ) or not hasattr(_A    ,     """_dynamo"""      ):
            return False
      return isinstance(_A    ,     torch._dynamo.eval_frame.OptimizedModule      )




