
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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

			return orig_key




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			for key in orig_state_dict.copy().keys():
						UpperCAmelCase__		:List[Any] 		=    orig_state_dict.pop(UpperCAmelCase_							)

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

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

			return orig_state_dict




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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

			UpperCAmelCase__		: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__":
						__snake_case					:							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.'
						)
						__snake_case					:							Any           =							parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











'''simple docstring'''





from __future__ import annotations

import sys




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      letter
      _a				:							int							       =      freq
      _a				:							dict[str, str]							       =      {}



  def __repr__(			self ):







      '''simple docstring'''


      return f'''{self.letter}:{self.freq}'''






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      freq
      _a				:							Letter | TreeNode							       =      left
      _a				:							Letter | TreeNode							       =      right






def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							dict[str, int]							       =      {}
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      f.read(1						)
            if not c:
                break
            _a				:							Optional[Any]							       =      chars[c] + 1 if c in chars else 1
    return sorted((Letter(UpperCAmelCase_   ,	UpperCAmelCase_						) for c, f in chars.items())   ,	key=lambda UpperCAmelCase_						: x.freq						)





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							list[Letter | TreeNode]							       =      letters  # type: ignore
    while len(UpperCAmelCase_						) > 1:
        _a				:							List[Any]							       =      response.pop(0						)
        _a				:							List[str]							       =      response.pop(0						)
        _a				:							Dict							       =      left.freq + right.freq
        _a				:							Any							       =      TreeNode(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
        response.append(UpperCAmelCase_						)
        response.sort(key=lambda UpperCAmelCase_						: x.freq						)
    return response[0]





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							int							       =      bitstring
        return [root]
    _a				:							TreeNode							       =      root  # type: ignore
    _a				:							Optional[Any]							       =      []
    letters += traverse_tree(treenode.left   ,	bitstring + """0"""						)
    letters += traverse_tree(treenode.right   ,	bitstring + """1"""						)
    return letters





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      parse_file(UpperCAmelCase_						)
    _a				:							Tuple							       =      build_tree(UpperCAmelCase_						)
    _a				:							Union[str, Any]							       =      {
        k: v for letter in traverse_tree(UpperCAmelCase_   ,	""""""						) for k, v in letter.bitstring.items()
    }
    print(F'''Huffman Coding  of {file_path}: '''						)
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      f.read(1						)
            if not c:
                break
            print(letters[c]   ,	end=""" """						)
    print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])


