




'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

    return orig_key





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    for key in orig_state_dict.copy().keys():
        _a				:							List[Any]							       =      orig_state_dict.pop(UpperCAmelCase_						)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            _a				:							List[Any]							       =      val

    _a				:							List[Any]							       =      orig_state_dict["""cls.predictions.decoder.bias"""]
    _a				:							Any							       =      torch.arange(UpperCAmelCase_						).expand((1, -1)						) + 2

    return orig_state_dict





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							List[Any]							       =      torch.load(UpperCAmelCase_   ,	map_location="""cpu"""						)["""model_state_dict"""]
    _a				:							Dict							       =      MraConfig.from_json_file(UpperCAmelCase_						)
    _a				:							Optional[int]							       =      MraForMaskedLM(UpperCAmelCase_						)

    _a				:							List[str]							       =      convert_checkpoint_helper(config.max_position_embeddings   ,	UpperCAmelCase_						)

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

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


if __name__ == "__main__":
  a		:	Optional[int]       =      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.'''
  )
  a		:	Any       =      parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_					:		int           = '''Update an existing config file with the latest defaults while maintaining the old configuration.'''

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str   )						->     Dict:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      args.config_file
	if config_file is None and Path(__magic_name__   ).exists():
		UpperCamelCase	:int		=      default_config_file
	elif not Path(__magic_name__   ).exists():
		raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""   )
	UpperCamelCase	:Dict		=      load_config_from_file(__magic_name__   )

	if config_file.endswith(""".json"""   ):
		config.to_json_file(__magic_name__   )
	else:
		config.to_yaml_file(__magic_name__   )
	return config_file

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Optional[Any]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      parser.add_parser("""update"""      ,     parents=__magic_name__      ,     help=__magic_name__      ,     formatter_class=__magic_name__   )
	parser.add_argument(
	    """--config_file"""      ,     default=__magic_name__      ,     help=(
	        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )      ,     )

	parser.set_defaults(func=__magic_name__   )
	return parser

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, Any]   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      update_config(__magic_name__   )
	print(f"""Sucessfully updated the configuration file at {config_file}."""   )
