


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


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





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      if "xprophetnet" in prophetnet_checkpoint_path:
            __lowerCamelCase     :      str       						=						XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCAmelCase      )
            __lowerCamelCase						, __lowerCamelCase     :      Any       						=						XLMProphetNetForConditionalGeneration.from_pretrained(
                UpperCAmelCase						,	output_loading_info=UpperCAmelCase      )
      else:
            __lowerCamelCase     :      int       						=						ProphetNetForConditionalGenerationOld.from_pretrained(UpperCAmelCase      )
            __lowerCamelCase						, __lowerCamelCase     :      int       						=						ProphetNetForConditionalGeneration.from_pretrained(
                UpperCAmelCase						,	output_loading_info=UpperCAmelCase      )

      __lowerCamelCase     :      Any       						=						["""key_proj""", """value_proj""", """query_proj"""]

      __lowerCamelCase     :      Optional[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"]:
            __lowerCamelCase     :      List[Any]       						=						key.split("""."""      )

            if attributes[0] == "lm_head":
                  __lowerCamelCase     :      str       						=						prophet
                  __lowerCamelCase     :      Dict       						=						prophet_old
            else:
                  __lowerCamelCase     :      Union[str, Any]       						=						prophet.prophetnet
                  __lowerCamelCase     :      Dict       						=						prophet_old.model

            __lowerCamelCase     :      Union[str, Any]       						=						False
            for attribute in attributes:
                  if attribute in mapping:
                        __lowerCamelCase     :      Any       						=						mapping[attribute]
                        if not hasattr(UpperCAmelCase						,	UpperCAmelCase      ) and len(UpperCAmelCase      ) > 0:
                              __lowerCamelCase     :      Dict       						=						attribute
                  elif hasattr(UpperCAmelCase						,	UpperCAmelCase      ):
                        __lowerCamelCase     :      Dict       						=						attribute

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

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

                  if attribute.isdigit():
                        __lowerCamelCase     :      Optional[int]       						=						model[int(UpperCAmelCase      )]
                        __lowerCamelCase     :      Tuple       						=						old_model[int(UpperCAmelCase      )]
                  else:
                        __lowerCamelCase     :      int       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

                        if old_attribute == "":
                              __lowerCamelCase     :      str       						=						old_model
                        else:
                              if not hasattr(UpperCAmelCase						,	UpperCAmelCase      ):
                                    raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
                              __lowerCamelCase     :      Union[str, Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

            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(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						List[str]     =							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.'
    )
    __UpperCamelCase    :						Any     =							parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







def   _UpperCAmelCase       (     UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						hex_num.strip()
      if not hex_num:
            raise ValueError("""No value was passed to the function"""      )

      __lowerCamelCase     :      Dict       						=						hex_num[0] == """-"""
      if is_negative:
            __lowerCamelCase     :      Optional[Any]       						=						hex_num[1:]

      try:
            __lowerCamelCase     :      Any       						=						int(UpperCAmelCase						,	16      )
      except ValueError:
            raise ValueError("""Invalid value was passed to the function"""      )

      __lowerCamelCase     :      List[str]       						=						""""""
      while int_num > 0:
            __lowerCamelCase     :      Dict       						=						str(int_num % 2      ) + bin_str
            int_num >>= 1

      return int(("""-""" + bin_str) if is_negative else bin_str      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



