





'''simple docstring'''





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 :  str										=  logging.get_logger(__name__)
logging.set_verbosity_info()



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Tuple:


 if "xprophetnet" in prophetnet_checkpoint_path:
  UpperCamelCase			:      int 			=     XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__  )
  UpperCamelCase			:      Any 			=     XLMProphetNetForConditionalGeneration.from_pretrained(
      lowerCamelCase__							,     output_loading_info=lowerCamelCase__  )
 else:
  UpperCamelCase			:      List[str] 			=     ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__  )
  UpperCamelCase			:      Optional[int] 			=     ProphetNetForConditionalGeneration.from_pretrained(
      lowerCamelCase__							,     output_loading_info=lowerCamelCase__  )

 UpperCamelCase			:      int 			=     ["key_proj", "value_proj", "query_proj"]

 UpperCamelCase			:      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"]:
  UpperCamelCase			:      Union[str, Any] 			=     key.split("."  )

  if attributes[0] == "lm_head":
   UpperCamelCase			:      Tuple 			=     prophet
   UpperCamelCase			:      Tuple 			=     prophet_old
  else:
   UpperCamelCase			:      Tuple 			=     prophet.prophetnet
   UpperCamelCase			:      List[str] 			=     prophet_old.model

  UpperCamelCase			:      int 			=     False
  for attribute in attributes:
   if attribute in mapping:
    UpperCamelCase			:      int 			=     mapping[attribute]
    if not hasattr(lowerCamelCase__							,     lowerCamelCase__  ) and len(lowerCamelCase__  ) > 0:
     UpperCamelCase			:      Dict 			=     attribute
   elif hasattr(lowerCamelCase__							,     lowerCamelCase__  ):
    UpperCamelCase			:      Optional[Any] 			=     attribute

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

    elif attribute == "key_proj":
     UpperCamelCase			:      List[str] 			=     nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
     UpperCamelCase			:      Any 			=     nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
    elif attribute == "value_proj":
     UpperCamelCase			:      Tuple 			=     nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
     UpperCamelCase			:      Union[str, Any] 			=     nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
    UpperCamelCase			:      Tuple 			=     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] == 512, "We want 512 position_embeddings."
    UpperCamelCase			:      List[Any] 			=     nn.Parameter(old_model.embed_positions.weight[:512, :]  )
    UpperCamelCase			:      Union[str, Any] 			=     True
    break

   if attribute.isdigit():
    UpperCamelCase			:      str 			=     model[int(lowerCamelCase__  )]
    UpperCamelCase			:      Union[str, Any] 			=     old_model[int(lowerCamelCase__  )]
   else:
    UpperCamelCase			:      int 			=     getattr(lowerCamelCase__							,     lowerCamelCase__  )

    if old_attribute == "":
     UpperCamelCase			:      str 			=     old_model
    else:
     if not hasattr(lowerCamelCase__							,     lowerCamelCase__  ):
      raise ValueError(F"""{old_model} does not have {old_attribute}"""  )
     UpperCamelCase			:      int 			=     getattr(lowerCamelCase__							,     lowerCamelCase__  )

  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(lowerCamelCase__  )


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  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 :  Tuple										=  parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







from math import loga



def 	A_       (  _lowerCAmelCase  )		->				int:
 if a < 0:
  raise ValueError("Input value must be a positive integer"  )
 elif isinstance(_lowerCAmelCase							,     _lowerCAmelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 return 0 if (a == 0) else int(loga(a & -a  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
