

'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def    _A						(      snake_case	)     ->      List[Any]:




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

   return orig_key







def    _A						(      snake_case						,				snake_case	)     ->      Any:




   for key in orig_state_dict.copy().keys():
      _lowercase     : str     	=  orig_state_dict.pop(lowerCamelCase__	)

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         _lowercase     : Union[str, Any]     	=  val

   _lowercase     : Optional[int]     	=  orig_state_dict["cls.predictions.decoder.bias"]
   _lowercase     : int     	=  torch.arange(lowerCamelCase__	).expand((1, -1)	) + 2

   return orig_state_dict







def    _A						(      snake_case						,				snake_case						,				snake_case	)     ->      Tuple:
   _lowercase     : Tuple     	=  torch.load(lowerCamelCase__						,				map_location="cpu"	)["model_state_dict"]
   _lowercase     : Optional[int]     	=  YosoConfig.from_json_file(lowerCamelCase__	)
   _lowercase     : Optional[int]     	=  YosoForMaskedLM(lowerCamelCase__	)

   _lowercase     : Optional[int]     	=  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__":
    _snake_case      =    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.'
    )
    _snake_case      =    parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

