
import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowerCamelCase__    :		int          =   logging.get_logger(__name__)
logging.set_verbosity_info()

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  if "xprophetnet" in prophetnet_checkpoint_path:
    lowercase__  :   Dict    		=      XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase_   )
    lowercase__		,    lowercase__  :   str    		=      XLMProphetNetForConditionalGeneration.from_pretrained(
        lowercase_ , output_loading_info=lowercase_   )
  else:
    lowercase__  :   Optional[Any]    		=      ProphetNetForConditionalGenerationOld.from_pretrained(lowercase_   )
    lowercase__		,    lowercase__  :   Any    		=      ProphetNetForConditionalGeneration.from_pretrained(
        lowercase_ , output_loading_info=lowercase_   )

  lowercase__  :   Optional[int]    		=      ["""key_proj""", """value_proj""", """query_proj"""]

  lowercase__  :   str    		=      {
      """self_attn""": """ngram_self_attn""",
      """cross_attn""": """encoder_attn""",
      """cross_attn_layer_norm""": """encoder_attn_layer_norm""",
      """feed_forward_layer_norm""": """final_layer_norm""",
      """feed_forward""": """""",
      """intermediate""": """fc1""",
      """output""": """fc2""",
      """key_proj""": """k_proj""",
      """query_proj""": """q_proj""",
      """value_proj""": """v_proj""",
      """word_embeddings""": """embed_tokens""",
      """embeddings_layer_norm""": """emb_layer_norm""",
      """relative_pos_embeddings""": """relative_linear""",
      """ngram_embeddings""": """ngram_input_embed""",
      """position_embeddings""": """embed_positions""",
  }

  for key in loading_info["missing_keys"]:
    lowercase__  :   List[str]    		=      key.split("""."""   )

    if attributes[0] == "lm_head":
      lowercase__  :   Tuple    		=      prophet
      lowercase__  :   str    		=      prophet_old
    else:
      lowercase__  :   int    		=      prophet.prophetnet
      lowercase__  :   Optional[int]    		=      prophet_old.model

    lowercase__  :   Dict    		=      False
    for attribute in attributes:
      if attribute in mapping:
        lowercase__  :   str    		=      mapping[attribute]
        if not hasattr(lowercase_ , lowercase_   ) and len(lowercase_   ) > 0:
          lowercase__  :   Dict    		=      attribute
      elif hasattr(lowercase_ , lowercase_   ):
        lowercase__  :   str    		=      attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        lowercase__  :   Dict    		=      old_model.weight
        logger.info(F'{attribute} is initialized.'   )
        lowercase__  :   str    		=      True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        lowercase__  :   Dict    		=      old_model.bias
        logger.info(F'{attribute} is initialized'   )
        lowercase__  :   Any    		=      True
        break
      elif attribute in special_keys and hasattr(lowercase_ , """in_proj_weight"""   ):
        lowercase__  :   Any    		=      old_model.in_proj_weight.shape[0] // 3
        lowercase__  :   Tuple    		=      getattr(lowercase_ , lowercase_   )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          lowercase__  :   List[Any]    		=      nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
          lowercase__  :   int    		=      nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

        elif attribute == "key_proj":
          lowercase__  :   Union[str, Any]    		=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
          lowercase__  :   Union[str, Any]    		=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
        elif attribute == "value_proj":
          lowercase__  :   Optional[Any]    		=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
          lowercase__  :   int    		=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
        lowercase__  :   str    		=      True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
        lowercase__  :   str    		=      nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
        lowercase__  :   str    		=      True
        break

      if attribute.isdigit():
        lowercase__  :   List[str]    		=      model[int(lowercase_   )]
        lowercase__  :   str    		=      old_model[int(lowercase_   )]
      else:
        lowercase__  :   Optional[int]    		=      getattr(lowercase_ , lowercase_   )

        if old_attribute == "":
          lowercase__  :   Union[str, Any]    		=      old_model
        else:
          if not hasattr(lowercase_ , lowercase_   ):
            raise ValueError(F'{old_model} does not have {old_attribute}'   )
          lowercase__  :   List[str]    		=      getattr(lowercase_ , lowercase_   )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'   )

  print(F'Saving model to {pytorch_dump_folder_path}'   )
  prophet.save_pretrained(lowercase_   )


if __name__ == "__main__":
     lowerCamelCase__    :		Dict          =   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     lowerCamelCase__    :		Union[str, Any]          =   parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








def 		UpperCamelCase	(      lowercase_ = 10_00   )		->	int:


  '''simple docstring'''






  return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1   )   )


if __name__ == "__main__":
     print(solution())






