




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				:		Optional[int]     =   logging.get_logger(__name__)
logging.set_verbosity_info()

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      if "xprophetnet" in prophetnet_checkpoint_path:
            lowerCamelCase_		     =    XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase     )
            lowerCamelCase_		     =    XLMProphetNetForConditionalGeneration.from_pretrained(
                _lowerCAmelCase	,							output_loading_info=_lowerCAmelCase     )
      else:
            lowerCamelCase_		     =    ProphetNetForConditionalGenerationOld.from_pretrained(_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] == 5_12, "We want 512 position_embeddings."
                        lowerCamelCase_		     =    nn.Parameter(old_model.embed_positions.weight[:5_12, :]     )
                        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__":
 lowerCamelCase				:		int     =   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				:		str     =   parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''





import string
from math import logaa





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    List[str]    						=      document.translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		).replace("""\n"""     ,   """"""		)
      snake_case__  :    List[str]    						=      document_without_punctuation.split(""" """		)  # word tokenization
      return len([word for word in tokenize_document if word.lower() == term.lower()]		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> tuple[int, int]:
      snake_case__  :    Dict    						=      corpus.lower().translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		)  # strip all punctuation and replace it with ''
      snake_case__  :    Any    						=      corpus_without_punctuation.split("""\n"""		)
      snake_case__  :    int    						=      term.lower()
      return (len([doc for doc in docs if term in doc]		), len(_lowerCAmelCase		))





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> float:
      if smoothing:
            if n == 0:
                  raise ValueError("""log10(0) is undefined."""		)
            return round(1 + logaa(n / (1 + df)		)     ,   3		)

      if df == 0:
            raise ZeroDivisionError("""df must be > 0"""		)
      elif n == 0:
            raise ValueError("""log10(0) is undefined."""		)
      return round(logaa(n / df		)     ,   3		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> float:
      return round(tf * idf     ,   3		)
