



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      if "model" in orig_key:
            lowerCamelCase								=		orig_key.replace("model."   ,   "" )
      if "norm1" in orig_key:
            lowerCamelCase								=		orig_key.replace("norm1"   ,   "attention.output.LayerNorm" )
      if "norm2" in orig_key:
            lowerCamelCase								=		orig_key.replace("norm2"   ,   "output.LayerNorm" )
      if "norm" in orig_key:
            lowerCamelCase								=		orig_key.replace("norm"   ,   "LayerNorm" )
      if "transformer" in orig_key:
            lowerCamelCase								=		orig_key.split("." )[0].split("_" )[-1]
            lowerCamelCase								=		orig_key.replace(F"""transformer_{layer_num}"""   ,   F"""encoder.layer.{layer_num}""" )
      if "mha.attn" in orig_key:
            lowerCamelCase								=		orig_key.replace("mha.attn"   ,   "attention.self" )
      if "mha" in orig_key:
            lowerCamelCase								=		orig_key.replace("mha"   ,   "attention" )
      if "W_q" in orig_key:
            lowerCamelCase								=		orig_key.replace("W_q"   ,   "self.query" )
      if "W_k" in orig_key:
            lowerCamelCase								=		orig_key.replace("W_k"   ,   "self.key" )
      if "W_v" in orig_key:
            lowerCamelCase								=		orig_key.replace("W_v"   ,   "self.value" )
      if "ff1" in orig_key:
            lowerCamelCase								=		orig_key.replace("ff1"   ,   "intermediate.dense" )
      if "ff2" in orig_key:
            lowerCamelCase								=		orig_key.replace("ff2"   ,   "output.dense" )
      if "ff" in orig_key:
            lowerCamelCase								=		orig_key.replace("ff"   ,   "output.dense" )
      if "mlm_class" in orig_key:
            lowerCamelCase								=		orig_key.replace("mlm.mlm_class"   ,   "cls.predictions.decoder" )
      if "mlm" in orig_key:
            lowerCamelCase								=		orig_key.replace("mlm"   ,   "cls.predictions.transform" )
      if "cls" not in orig_key:
            lowerCamelCase								=		"yoso." + orig_key

      return orig_key

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            lowerCamelCase								=		orig_state_dict.pop(UpperCAmelCase__ )

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

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

      return orig_state_dict

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		torch.load(UpperCAmelCase__   ,   map_location="cpu" )["model_state_dict"]
      lowerCamelCase								=		YosoConfig.from_json_file(UpperCAmelCase__ )
      lowerCamelCase								=		YosoForMaskedLM(UpperCAmelCase__ )

      lowerCamelCase								=		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_	     =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_	     =		parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

a_						:     Optional[int]	     =		(3, 9, -1_1, 0, 7, 5, 1, -1)
a_						:     str	     =		(4, 6, 2, 0, 8, 1_0, 3, -2)





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		None
           for i in sorted(__a				,			reverse=__a    ):
                 lowerCamelCase								=		Node(__a				,			self.head    )







     def __iter__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.head
           while node:
                 yield node.data
                 lowerCamelCase								=		node.next_node







     def __len__(self    ):






           '''simple docstring'''



           return sum(1 for _ in self    )







     def __str__(self    ):






           '''simple docstring'''



           return " -> ".join([str(__a    ) for node in self]    )



def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     a_						:     Any	     =		SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))