


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

lowerCAmelCase__  :Optional[Any]             =							{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase__  :Tuple             =							{
    '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'
        ),
    },
}

lowerCAmelCase__  :Dict             =							{
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

lowerCAmelCase__  :List[str]             =							'▁'

class 		__a				(    _A      ):
		_a     :  Optional[int]           =     VOCAB_FILES_NAMES
		_a     :  Dict           =     PRETRAINED_VOCAB_FILES_MAP
		_a     :  Any           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     :  Optional[int]           =     ['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      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      AddedToken(__SCREAMING_SNAKE_CASE      ,   lstrip=__SCREAMING_SNAKE_CASE      ,   rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE						) else mask_token

									_UpperCAmelCase					=      {} if sp_model_kwargs is None else sp_model_kwargs

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

									_UpperCAmelCase					=      vocab_file
									_UpperCAmelCase					=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(str(__SCREAMING_SNAKE_CASE						)						)

									_UpperCAmelCase					=      {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

									_UpperCAmelCase					=      len(self.sp_model						) - 1
									_UpperCAmelCase					=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	Optional[Any]:




									"""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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False						)			->	str:




									"""simple docstring"""





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

									if token_ids_a is None:
																return [1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1]
									return [1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1]
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	List[Any]:




									"""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]
		@property
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									return len(self.sp_model						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
									vocab.update(self.added_tokens_encoder						)
									return vocab
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									return self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,   out_type=__SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
									_UpperCAmelCase					=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE						)

									return spm_id if spm_id else self.unk_token_id
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      []
									_UpperCAmelCase					=      ''
									_UpperCAmelCase					=      False
									for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																							if not prev_is_special:
																														out_string += " "
																							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE						) + token
																							_UpperCAmelCase					=      True
																							_UpperCAmelCase					=      []
																else:
																							current_sub_tokens.append(__SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      False
									out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE						)
									return out_string.strip()
		def __getstate__(      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.__dict__.copy()
									_UpperCAmelCase					=      None
									return state
		def __setstate__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      d

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

									_UpperCAmelCase					=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(self.vocab_file						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	Optional[Any]:




									"""simple docstring"""





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

									if os.path.abspath(self.vocab_file						) != os.path.abspath(__SCREAMING_SNAKE_CASE						) and os.path.isfile(self.vocab_file						):
																copyfile(self.vocab_file      ,   __SCREAMING_SNAKE_CASE						)
									elif not os.path.isfile(self.vocab_file						):
																with open(__SCREAMING_SNAKE_CASE      ,   'wb'						) as fi:
																							_UpperCAmelCase					=      self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE						)

									return (out_vocab_file,)




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()	)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()	)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--longformer_model''',
		    default=None,
		    type=str,
		    required=True,
		    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
		)
		parser.add_argument(
		    '''--longformer_question_answering_ckpt_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch Lightning Checkpoint.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
