





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

 UpperCamelCase     : Union[str, Any]	     =       ["key_proj", "value_proj", "query_proj"]

 UpperCamelCase     : int	     =       {
     "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     : Optional[Any]	     =       prophet
   UpperCamelCase     : Optional[int]	     =       prophet_old
  else:
   UpperCamelCase     : int	     =       prophet.prophetnet
   UpperCamelCase     : str	     =       prophet_old.model

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

   if attribute == "weight":
    assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
    UpperCamelCase     : List[Any]	     =       old_model.weight
    logger.info(F"""{attribute} is initialized."""					)
    UpperCamelCase     : Any	     =       True
    break
   elif attribute == "bias":
    assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
    UpperCamelCase     : Union[str, Any]	     =       old_model.bias
    logger.info(F"""{attribute} is initialized"""					)
    UpperCamelCase     : Optional[Any]	     =       True
    break
   elif attribute in special_keys and hasattr(_lowerCAmelCase							,       "in_proj_weight"					):
    UpperCamelCase     : Optional[Any]	     =       old_model.in_proj_weight.shape[0] // 3
    UpperCamelCase     : List[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[Any]	     =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
     UpperCamelCase     : List[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     : List[Any]	     =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
    elif attribute == "value_proj":
     UpperCamelCase     : List[Any]	     =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
     UpperCamelCase     : int	     =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
    UpperCamelCase     : List[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."
    UpperCamelCase     : List[str]	     =       nn.Parameter(old_model.embed_positions.weight[:512, :]					)
    UpperCamelCase     : str	     =       True
    break

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

    if old_attribute == "":
     UpperCamelCase     : int	     =       old_model
    else:
     if not hasattr(_lowerCAmelCase							,       _lowerCAmelCase					):
      raise ValueError(F"""{old_model} does not have {old_attribute}"""					)
     UpperCamelCase     : List[str]	     =       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	:					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	:					List[Any]			  =					parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







class  A__       :  # Public class to implement a graph





      def __init__(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       row
       UpperCamelCase     : Any	     =       col
       UpperCamelCase     : Optional[Any]	     =       graph





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       UpperCamelCase     : Dict	     =       [-1, 0, 1, -1, 1, -1, 0, 1]
       UpperCamelCase     : Any	     =       True  # Make those cells visited
       for k in range(8       ):
        if self.is_safe(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       ):
         self.diffs(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       )





      def 	__UpperCamelCase(			self       ):  # And finally, count all islands.
       '''simple docstring'''




       UpperCamelCase     : str	     =       [[False for j in range(self.COL       )] for i in range(self.ROW       )]
       UpperCamelCase     : int	     =       0
       for i in range(self.ROW       ):
        for j in range(self.COL       ):
         if visited[i][j] is False and self.graph[i][j] == 1:
          self.diffs(A_	,							A_	,							A_       )
          count += 1
       return count
