'''simple docstring'''
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


lowercase__  :      int        =					logging.get_logger(__name__)
logging.set_verbosity_info()





def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  Dict					):


      '''simple docstring'''


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

      UpperCAmelCase_            =  ['''key_proj''', '''value_proj''', '''query_proj''']

      UpperCAmelCase_            =  {
          '''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_            =  key.split('''.'''					)

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

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

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

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

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

                        if old_attribute == "":
                              UpperCAmelCase_            =  old_model
                        else:
                              if not hasattr(a__							,    a__					):
                                    raise ValueError(F"""{old_model} does not have {old_attribute}"""					)
                              UpperCAmelCase_            =  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__":
      lowercase__  :      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."
      )
      lowercase__  :      Tuple        =					parser.parse_args()
      convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''
lowercase__  :      Union[str, Any]        =					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__  :      str        =					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase__  :      Any        =					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





