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_camembert import CamembertTokenizer
else:
      UpperCamelCase__											=					None


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__											=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__											=					{
    'camembert-base': 5_1_2,
}

UpperCamelCase__											=					'▁'





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : Dict      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Optional[Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : int      =			['input_ids', 'attention_mask']
   __UpperCAmelCase : Tuple      =			CamembertTokenizer


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						Any=None       ,  __UpperCAmelCase     :						Tuple="<s>"       ,  __UpperCAmelCase     :						int="</s>"       ,  __UpperCAmelCase     :						Union[str, Any]="</s>"       ,  __UpperCAmelCase     :						Optional[int]="<s>"       ,  __UpperCAmelCase     :						str="<unk>"       ,  __UpperCAmelCase     :						int="<pad>"       ,  __UpperCAmelCase     :						Dict="<mask>"       ,  __UpperCAmelCase     :						Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase       ,  lstrip=__UpperCAmelCase       ,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) else mask_token

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

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


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         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 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         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 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         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(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  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		):
               copyfile(self.vocab_file       ,  __UpperCAmelCase		)

         return (out_vocab_file,)




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(__A,  __A							)




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




def    lowerCAmelCase_ (     __A,  __A="facebook/mbart-large-en-ro",  __A=False,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

      if finetuned:
            UpperCAmelCase__								=  make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
      )
      parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument(
          '--hf_config',
          default='facebook/mbart-large-cc25',
          type=str,
          help='Which huggingface architecture to use: mbart-large',
      )
      parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
      parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)



