
"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



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

  return orig_key




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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

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

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

  return orig_state_dict




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  torch.load(_UpperCamelCase			,					map_location='''cpu'''    )['''model_state_dict''']
  _lowercase:      List[str]   			=  YosoConfig.from_json_file(_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  YosoForMaskedLM(_UpperCamelCase    )

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


"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


A__ :       List[Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					*A_			,					**A_							)  ->						None:





         """simple docstring"""




         warnings.warn(
             '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use SegformerImageProcessor instead.'''			,					A_			,					)
         super().__init__(*A_			,					**A_							)
