




'''simple docstring'''




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ):
 '''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__										=    """nystromformer.""" + orig_key

 return orig_key

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Optional[Any]       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ):
 '''simple docstring'''





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

  if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
   continue
  else:
   UpperCAmelCase__										=    val

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

 return orig_state_dict

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ):
 '''simple docstring'''





 UpperCAmelCase__										=    torch.load(SCREAMING_SNAKE_CASE__       ,						map_location="""cpu"""       )["""model_state_dict"""]
 UpperCAmelCase__										=    NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__       )

 UpperCAmelCase__										=    convert_checkpoint_helper(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

 model.load_state_dict(SCREAMING_SNAKE_CASE__       )
 model.eval()
 model.save_pretrained(SCREAMING_SNAKE_CASE__       )

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


if __name__ == "__main__":
    UpperCAmelCase_       				=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Nystromformer model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''




import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase_       				=      2 << 2_0  # 2mb
UpperCAmelCase_       				=      get_logger(__name__)

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    self._options.get_all_options().get("""runner""" )
   return runner in [None, "DirectRunner", "PortableRunner"]

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Any=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if fs.exists(SCREAMING_SNAKE_CASE__       ):
  if force_upload:
   logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.create(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""rb"""       ) as local_file:
   UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    remote_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )







def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Dict=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if os.path.exists(SCREAMING_SNAKE_CASE__       ):
  if force_download:
   logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.open(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""wb"""       ) as local_file:
   UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    local_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
