
"""simple docstring"""





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_ = "▁"				,   lowercase_ = True				,   lowercase_ = "<unk>"				,   lowercase_ = "</s>"				,   lowercase_ = "<pad>"				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    {
         "pad": {"id": 0, "token": pad_token},
         "eos": {"id": 1, "token": eos_token},
         "unk": {"id": 2, "token": unk_token},
     }

     UpperCAmelCase_					:					Optional[Any]			     =    [None] * len(self.special_tokens  )
     for token_dict in self.special_tokens.values():
        UpperCAmelCase_					:					Any			     =    token_dict["token"]

     UpperCAmelCase_					:					Optional[int]			     =    Tokenizer(Unigram()  )

     UpperCAmelCase_					:					Union[str, Any]			     =    normalizers.Sequence(
         [
             normalizers.Nmt(),
             normalizers.NFKC(),
             normalizers.Replace(Regex(" {2,}"  )				,   " "  ),
             normalizers.Lowercase(),
         ]  )
     UpperCAmelCase_					:					Tuple			     =    pre_tokenizers.Sequence(
         [
             pre_tokenizers.Metaspace(replacement=lowercase_				,   add_prefix_space=lowercase_  ),
             pre_tokenizers.Digits(individual_digits=lowercase_  ),
             pre_tokenizers.Punctuation(),
         ]  )
     UpperCAmelCase_					:					Tuple			     =    decoders.Metaspace(replacement=lowercase_				,   add_prefix_space=lowercase_  )

     UpperCAmelCase_					:					str			     =    TemplateProcessing(
         single=F"""$A {self.special_tokens["eos"]["token"]}"""				,   special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]				,   )

     UpperCAmelCase_					:					Optional[int]			     =    {
         "model": "SentencePieceUnigram",
         "replacement": replacement,
         "add_prefix_space": add_prefix_space,
     }

     super().__init__(lowercase_				,   lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = 8000				,   lowercase_ = True				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    trainers.UnigramTrainer(
         vocab_size=lowercase_				,   special_tokens=self.special_tokens_list				,   show_progress=lowercase_				,   )

     if isinstance(lowercase_				,   lowercase_  ):
        UpperCAmelCase_					:					List[str]			     =    [files]
     self._tokenizer.train(lowercase_				,   trainer=lowercase_  )

     self.add_unk_id()







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = 8000				,   lowercase_ = True				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    trainers.UnigramTrainer(
         vocab_size=lowercase_				,   special_tokens=self.special_tokens_list				,   show_progress=lowercase_				,   )

     self._tokenizer.train_from_iterator(lowercase_				,   trainer=lowercase_  )

     self.add_unk_id()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    json.loads(self._tokenizer.to_str()  )

     UpperCAmelCase_					:					Optional[Any]			     =    self.special_tokens["unk"]["id"]

     UpperCAmelCase_					:					Any			     =    Tokenizer.from_str(json.dumps(lowercase_  )  )



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)



