
'''simple docstring'''







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def    UpperCAmelCase_						(__a :       str					):






   """simple docstring"""

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

   return orig_key





def    UpperCAmelCase_						(__a :       int					,      __a :       Optional[int]					):






   """simple docstring"""

   for key in orig_state_dict.copy().keys():
      _a							:      Optional[int]     =  orig_state_dict.pop(_snake_case					)

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

   _a							:      Dict     =  orig_state_dict['cls.predictions.decoder.bias']
   _a							:      int     =  torch.arange(_snake_case					).expand((1, -1)					) + 2

   return orig_state_dict





def    UpperCAmelCase_						(__a :       str					,      __a :       Union[str, Any]					,      __a :       Dict					):






   """simple docstring"""

   _a							:      Tuple     =  torch.load(_snake_case					,      map_location='cpu'					)['model_state_dict']
   _a							:      Union[str, Any]     =  YosoConfig.from_json_file(_snake_case					)
   _a							:      int     =  YosoForMaskedLM(_snake_case					)

   _a							:      Union[str, Any]     =  convert_checkpoint_helper(config.max_position_embeddings					,      _snake_case					)

   print(model.load_state_dict(_snake_case					)					)
   model.eval()
   model.save_pretrained(_snake_case					)

   print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""					)


if __name__ == "__main__":
  __lowerCAmelCase   =       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."""
  )
  __lowerCAmelCase   =       parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""



from ...configuration_utils import PretrainedConfig





class 				a			(					lowercase      ):
      UpperCamelCase							:   Union[str, Any]           =     """bert-generation"""



      def __init__(   self     ,	UpperCamelCase_=50_358     ,	UpperCamelCase_=1_024     ,	UpperCamelCase_=24     ,	UpperCamelCase_=16     ,	UpperCamelCase_=4_096     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=512     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=1E-12     ,	UpperCamelCase_=0     ,	UpperCamelCase_=2     ,	UpperCamelCase_=1     ,	UpperCamelCase_="absolute"     ,	UpperCamelCase_=True     ,	**UpperCamelCase_     ,	):
          super().__init__(pad_token_id=UpperCamelCase_     ,	bos_token_id=UpperCamelCase_     ,	eos_token_id=UpperCamelCase_     ,	**UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					vocab_size
          UpperCAmelCase__	:      Union[str, Any]     							=					hidden_size
          UpperCAmelCase__	:      Optional[Any]     							=					num_hidden_layers
          UpperCAmelCase__	:      Dict     							=					num_attention_heads
          UpperCAmelCase__	:      Dict     							=					hidden_act
          UpperCAmelCase__	:      Optional[Any]     							=					intermediate_size
          UpperCAmelCase__	:      Optional[int]     							=					hidden_dropout_prob
          UpperCAmelCase__	:      Tuple     							=					attention_probs_dropout_prob
          UpperCAmelCase__	:      List[Any]     							=					max_position_embeddings
          UpperCAmelCase__	:      Optional[int]     							=					initializer_range
          UpperCAmelCase__	:      Optional[int]     							=					layer_norm_eps
          UpperCAmelCase__	:      Union[str, Any]     							=					position_embedding_type
          UpperCAmelCase__	:      Dict     							=					use_cache

