






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Any:
       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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase							=    orig_state_dict.pop(__snake_case )

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

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

       return orig_state_dict




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[Any]:
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )["""model_state_dict"""]
       _UpperCAmelCase							=    YosoConfig.from_json_file(__snake_case )
       _UpperCAmelCase							=    YosoForMaskedLM(__snake_case )

       _UpperCAmelCase							=    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__":
  __a:     Union[str, Any]   					=					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:     Union[str, Any]   					=					parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@register_to_config
	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = False  ,  ):
		super().__init__()

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

