



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 	__a					(   lowerCAmelCase_       : int    )				->			List[str]:






      '''simple docstring'''



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

      return orig_key



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Any    )				->			List[Any]:






      '''simple docstring'''



      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_=       orig_state_dict.pop(lowerCAmelCase_    )

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

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

      return orig_state_dict



def 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[str]    )				->			Tuple:






      '''simple docstring'''



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

      UpperCAmelCase_=       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__":
 __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 typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
							self.builder.download_and_prepare(
							    download_mode=__UpperCAmelCase						,  file_format=self._file_format						,  )
							return self.builder.as_dataset(split=self.split					)




