
"""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


UpperCAmelCase__        =					logging.get_logger(__name__)
logging.set_verbosity_info()
def    _UpperCAmelCase (					__lowerCamelCase  :			Tuple					,   __lowerCamelCase  :			List[Any]						)	->   Any:






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

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

  _snake_case   	=			{
      '''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"]:
    _snake_case   	=			key.split('''.'''						)

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

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

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

        elif attribute == "key_proj":
          _snake_case   	=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
          _snake_case   	=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
        elif attribute == "value_proj":
          _snake_case   	=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
          _snake_case   	=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
        _snake_case   	=			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."
        _snake_case   	=			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
        _snake_case   	=			True
        break

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

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

    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(SCREAMING_SNAKE_CASE_						)


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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     None

SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE		:int						=     {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE		:List[str]						=     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE		:Optional[Any]						=     """▁"""






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:List[Any]										=      BarthezTokenizer






     def __init__(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase="<s>"		,						_lowercase="</s>"		,						_lowercase="</s>"		,						_lowercase="<s>"		,						_lowercase="<unk>"		,						_lowercase="<pad>"		,						_lowercase="<mask>"		,						**_lowercase		,						)->     Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_			  =						AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase	) if isinstance(_lowercase		,						_lowercase	) else mask_token

         super().__init__(
             _lowercase		,						tokenizer_file=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						**_lowercase		,						)

         UpperCamelCase_			  =						vocab_file
         UpperCamelCase_			  =						False if not self.vocab_file else True






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]
         UpperCamelCase_			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         UpperCamelCase_			  =						[self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."	)

         if not os.path.isdir(_lowercase	):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
             return
         UpperCamelCase_			  =						os.path.join(
             _lowercase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	):
             copyfile(self.vocab_file		,						_lowercase	)

         return (out_vocab_file,)
