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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ="▁"

lowerCamelCase      ={"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase      ={
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase      ={
    "xlm-roberta-base": 5_1_2,
    "xlm-roberta-large": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-english": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-german": 5_1_2,
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





					"""simple docstring"""







					SCREAMING_SNAKE_CASE_		   =  VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		   =  PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		   =  ["input_ids", "attention_mask"]






					def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="<unk>"						,						__SCREAMING_SNAKE_CASE="<pad>"						,						__SCREAMING_SNAKE_CASE="<mask>"						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[Any]:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[int]					=				AddedToken(UpperCamelCase_						,						lstrip=UpperCamelCase_						,						rstrip=UpperCamelCase_  ) if isinstance(UpperCamelCase_						,						UpperCamelCase_  ) else mask_token

											UpperCamelCase__    :   int					=				{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=UpperCamelCase_						,						eos_token=UpperCamelCase_						,						unk_token=UpperCamelCase_						,						sep_token=UpperCamelCase_						,						cls_token=UpperCamelCase_						,						pad_token=UpperCamelCase_						,						mask_token=UpperCamelCase_						,						sp_model_kwargs=self.sp_model_kwargs						,						**UpperCamelCase_						,						)

											UpperCamelCase__    :   Optional[int]					=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
											self.sp_model.Load(str(UpperCamelCase_  )  )
											UpperCamelCase__    :   Union[str, Any]					=				vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# Mimic fairseq token-to-id alignment for the first 4 token
											UpperCamelCase__    :   Optional[Any]					=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											UpperCamelCase__    :   List[Any]					=				1

											UpperCamelCase__    :   Optional[Any]					=				len(self.sp_model  ) + self.fairseq_offset
											UpperCamelCase__    :   str					=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






					def __getstate__(							self  )  ->   Optional[int]:




											"""simple docstring"""







											UpperCamelCase__    :   List[str]					=				self.__dict__.copy()
											UpperCamelCase__    :   str					=				None
											UpperCamelCase__    :   str					=				self.sp_model.serialized_model_proto()
											return state






					def __setstate__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




											"""simple docstring"""







											UpperCamelCase__    :   Union[str, Any]					=				d

											# for backward compatibility
											if not hasattr(self						,						'''sp_model_kwargs'''  ):
																	UpperCamelCase__    :   Tuple					=				{}

											UpperCamelCase__    :   Dict					=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   str:




											"""simple docstring"""







											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											UpperCamelCase__    :   List[Any]					=				[self.cls_token_id]
											UpperCamelCase__    :   Union[str, Any]					=				[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = False  )  ->   Tuple:




											"""simple docstring"""







											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=UpperCamelCase_						,						token_ids_a=UpperCamelCase_						,						already_has_special_tokens=UpperCamelCase_  )

											if token_ids_a is None:
																	return [1] + ([0] * len(UpperCamelCase_  )) + [1]
											return [1] + ([0] * len(UpperCamelCase_  )) + [1, 1] + ([0] * len(UpperCamelCase_  )) + [1]






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   Any:




											"""simple docstring"""







											UpperCamelCase__    :   Dict					=				[self.sep_token_id]
											UpperCamelCase__    :   List[Any]					=				[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]






					@property
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




											"""simple docstring"""







											return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




											"""simple docstring"""







											UpperCamelCase__    :   Union[str, Any]					=				{self.convert_ids_to_tokens(UpperCamelCase_  ): i for i in range(self.vocab_size  )}
											vocab.update(self.added_tokens_encoder  )
											return vocab






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




											"""simple docstring"""







											return self.sp_model.encode(UpperCamelCase_						,						out_type=UpperCamelCase_  )






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




											"""simple docstring"""







											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											UpperCamelCase__    :   Optional[int]					=				self.sp_model.PieceToId(UpperCamelCase_  )

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




											"""simple docstring"""







											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset  )






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[Any]:




											"""simple docstring"""







											UpperCamelCase__    :   Tuple					=				"".join(UpperCamelCase_  ).replace(UpperCamelCase_						,						''' '''  ).strip()
											return out_string






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   Any:




											"""simple docstring"""







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

											if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase_  ) and os.path.isfile(self.vocab_file  ):
																	copyfile(self.vocab_file						,						UpperCamelCase_  )
											elif not os.path.isfile(self.vocab_file  ):
																	with open(UpperCamelCase_						,						'''wb'''  ) as fi:
																							UpperCamelCase__    :   Optional[int]					=				self.sp_model.serialized_model_proto()
																							fi.write(UpperCamelCase_  )

											return (out_vocab_file,)




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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if "xprophetnet" in prophetnet_checkpoint_path:
            UpperCamelCase__    :   int					=				XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				XLMProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)
      else:
            UpperCamelCase__    :   Tuple					=				ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				ProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)

      UpperCamelCase__    :   Optional[Any]					=				['''key_proj''', '''value_proj''', '''query_proj''']

      UpperCamelCase__    :   Tuple					=				{
          '''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__    :   Tuple					=				key.split('''.'''		)

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

            UpperCamelCase__    :   Tuple					=				False
            for attribute in attributes:
                  if attribute in mapping:
                        UpperCamelCase__    :   List[str]					=				mapping[attribute]
                        if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		) and len(UpperCamelCase__		) > 0:
                              UpperCamelCase__    :   Tuple					=				attribute
                  elif hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                        UpperCamelCase__    :   Tuple					=				attribute

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

                        elif attribute == "key_proj":
                              UpperCamelCase__    :   Union[str, Any]					=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                              UpperCamelCase__    :   Optional[int]					=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                        elif attribute == "value_proj":
                              UpperCamelCase__    :   List[str]					=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                              UpperCamelCase__    :   Tuple					=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                        UpperCamelCase__    :   Tuple					=				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_1_2, "We want 512 position_embeddings."
                        UpperCamelCase__    :   Optional[Any]					=				nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
                        UpperCamelCase__    :   List[Any]					=				True
                        break

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

                        if old_attribute == "":
                              UpperCamelCase__    :   List[str]					=				old_model
                        else:
                              if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                                    raise ValueError(f'''{old_model} does not have {old_attribute}'''		)
                              UpperCamelCase__    :   List[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__":
    lowerCamelCase      =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      =parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


