




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



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str      ):
     """simple docstring"""

     if "xprophetnet" in prophetnet_checkpoint_path:
          lowerCAmelCase__          =			XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_      )
          lowerCAmelCase__     ,		lowerCAmelCase__          =			XLMProphetNetForConditionalGeneration.from_pretrained(
              lowerCAmelCase_       ,    output_loading_info=lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_      )
          lowerCAmelCase__     ,		lowerCAmelCase__          =			ProphetNetForConditionalGeneration.from_pretrained(
              lowerCAmelCase_       ,    output_loading_info=lowerCAmelCase_      )

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

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

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

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

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

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

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

                    if old_attribute == "":
                         lowerCAmelCase__          =			old_model
                    else:
                         if not hasattr(lowerCAmelCase_       ,    lowerCAmelCase_      ):
                              raise ValueError(F'{old_model} does not have {old_attribute}'      )
                         lowerCAmelCase__          =			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__":
     UpperCamelCase								=					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								=					parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


if __name__ == "__main__":
     fire.Fire(save_randomly_initialized_version)
