





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


_snake_case           =			logging.get_logger(__name__)
logging.set_verbosity_info()


def _A       (   __magic_name__  , __magic_name__	):
   if "xprophetnet" in prophetnet_checkpoint_path:
      lowercase__			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__	)
      lowercase__       ,							lowercase__			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
          __magic_name__  , output_loading_info=__magic_name__	)
   else:
      lowercase__			       =  ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__	)
      lowercase__       ,							lowercase__			       =  ProphetNetForConditionalGeneration.from_pretrained(
          __magic_name__  , output_loading_info=__magic_name__	)

   lowercase__			       =  ["key_proj", "value_proj", "query_proj"]

   lowercase__			       =  {
       "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__			       =  key.split("."	)

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

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

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            lowercase__			       =  old_model.weight
            logger.info(f'''{attribute} is initialized.'''	)
            lowercase__			       =  True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            lowercase__			       =  old_model.bias
            logger.info(f'''{attribute} is initialized'''	)
            lowercase__			       =  True
            break
         elif attribute in special_keys and hasattr(__magic_name__  , "in_proj_weight"	):
            lowercase__			       =  old_model.in_proj_weight.shape[0] // 3
            lowercase__			       =  getattr(__magic_name__  , __magic_name__	)
            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__			       =  nn.Parameter(old_model.in_proj_weight[:embed_dim, :]	)
               lowercase__			       =  nn.Parameter(old_model.in_proj_bias[:embed_dim]	)

            elif attribute == "key_proj":
               lowercase__			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]	)
               lowercase__			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]	)
            elif attribute == "value_proj":
               lowercase__			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]	)
               lowercase__			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]	)
            lowercase__			       =  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."
            lowercase__			       =  nn.Parameter(old_model.embed_positions.weight[:512, :]	)
            lowercase__			       =  True
            break

         if attribute.isdigit():
            lowercase__			       =  model[int(__magic_name__	)]
            lowercase__			       =  old_model[int(__magic_name__	)]
         else:
            lowercase__			       =  getattr(__magic_name__  , __magic_name__	)

            if old_attribute == "":
               lowercase__			       =  old_model
            else:
               if not hasattr(__magic_name__  , __magic_name__	):
                  raise ValueError(f'''{old_model} does not have {old_attribute}'''	)
               lowercase__			       =  getattr(__magic_name__  , __magic_name__	)

      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(__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			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."""
  )
  _snake_case           =			parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)













def _A       (   __magic_name__	):
   lowercase__			       =  ""
   for ch in key:
      if ch == " " or ch not in key_no_dups and ch.isalpha():
         key_no_dups += ch
   return key_no_dups


def _A       (   __magic_name__	):
   lowercase__			       =  [chr(i + 65	) for i in range(26	)]
   # Remove duplicate characters from key
   lowercase__			       =  remove_duplicates(key.upper()	)
   lowercase__			       =  len(__magic_name__	)
   # First fill cipher with key characters
   lowercase__			       =  {alphabet[i]: char for i, char in enumerate(__magic_name__	)}
   # Then map remaining characters in alphabet to
   # the alphabet from the beginning
   for i in range(len(__magic_name__	)  , 26	):
      lowercase__			       =  alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         lowercase__			       =  alphabet[i - offset]
      lowercase__			       =  char
   return cipher_alphabet


def _A       (   __magic_name__  , __magic_name__	):
   return "".join(cipher_map.get(__magic_name__  , __magic_name__	) for ch in message.upper()	)


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  {v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__magic_name__  , __magic_name__	) for ch in message.upper()	)


def _A       (   ):
   lowercase__			       =  input("Enter message to encode or decode: "	).strip()
   lowercase__			       =  input("Enter keyword: "	).strip()
   lowercase__			       =  input("Encipher or decipher? E/D:"	).strip()[0].lower()
   try:
      lowercase__			       =  {"e": encipher, "d": decipher}[option]
   except KeyError:
      raise KeyError("invalid input option"	)
   lowercase__			       =  create_cipher_map(__magic_name__	)
   print(func(__magic_name__  , __magic_name__	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






