






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







def 			A__							(      _a  :					str		,  _a  :					str	):





 '''simple docstring'''







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

 snake_case__  :    Dict							       =["key_proj", "value_proj", "query_proj"]

 snake_case__  :    Optional[Any]							       ={
     "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"]:
  snake_case__  :    Optional[int]							       =key.split("""."""	)

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

  snake_case__  :    Dict							       =False
  for attribute in attributes:
   if attribute in mapping:
    snake_case__  :    Tuple							       =mapping[attribute]
    if not hasattr(_a		,  _a	) and len(_a	) > 0:
     snake_case__  :    Tuple							       =attribute
   elif hasattr(_a		,  _a	):
    snake_case__  :    List[Any]							       =attribute

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

    elif attribute == "key_proj":
     snake_case__  :    Tuple							       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]	)
     snake_case__  :    str							       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]	)
    elif attribute == "value_proj":
     snake_case__  :    Optional[int]							       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]	)
     snake_case__  :    Optional[int]							       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]	)
    snake_case__  :    Union[str, Any]							       =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."
    snake_case__  :    List[Any]							       =nn.Parameter(old_model.embed_positions.weight[:512, :]	)
    snake_case__  :    Dict							       =True
    break

   if attribute.isdigit():
    snake_case__  :    Optional[int]							       =model[int(_a	)]
    snake_case__  :    List[str]							       =old_model[int(_a	)]
   else:
    snake_case__  :    Optional[Any]							       =getattr(_a		,  _a	)

    if old_attribute == "":
     snake_case__  :    int							       =old_model
    else:
     if not hasattr(_a		,  _a	):
      raise ValueError(f"{old_model} does not have {old_attribute}"	)
     snake_case__  :    str							       =getattr(_a		,  _a	)

  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(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		List[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	:		int										=   parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)













def 			A__							(      _a  :					Optional[Any]		,  _a  :					Tuple		,  _a  :					List[str]=False	):





 '''simple docstring'''







 if isinstance(_a		,  _a	) and isinstance(_a		,  _a	):
  snake_case__  :    int							       =len(set_a.intersection(_a	)	)

  if alternative_union:
   snake_case__  :    int							       =len(_a	) + len(_a	)
  else:
   snake_case__  :    Tuple							       =len(set_a.union(_a	)	)

  return intersection / union

 if isinstance(_a		,  (list, tuple)	) and isinstance(_a		,  (list, tuple)	):
  snake_case__  :    Optional[int]							       =[element for element in set_a if element in set_b]

  if alternative_union:
   snake_case__  :    Optional[Any]							       =len(_a	) + len(_a	)
   return len(_a	) / union
  else:
   snake_case__  :    Dict							       =set_a + [element for element in set_b if element not in set_a]
   return len(_a	) / len(_a	)

  return len(_a	) / len(_a	)
 return None


if __name__ == "__main__":
       __lowerCamelCase	:		List[Any]										=   {"""a""", """b""", """c""", """d""", """e"""}
       __lowerCamelCase	:		Optional[int]										=   {"""c""", """d""", """e""", """f""", """h""", """i"""}
       print(jaccard_similarity(set_a, set_b))





