





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      List[str]         =    {"""vocab_file""": """spm_char.model"""}

SCREAMING_SNAKE_CASE__							:      Dict         =    {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """microsoft/speecht5_asr""": 1_0_2_4,
    """microsoft/speecht5_tts""": 1_0_2_4,
    """microsoft/speecht5_vc""": 1_0_2_4,
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    str  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Any  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Tuple  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    str  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case="<s>"       ,      snake_case="</s>"       ,      snake_case="<unk>"       ,      snake_case="<pad>"       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





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

      super().__init__(
          bos_token=snake_case       ,      eos_token=snake_case       ,      unk_token=snake_case       ,      pad_token=snake_case       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **snake_case       ,      )

      a__						:    str   		=	vocab_file

      a__						:    Optional[int]   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(snake_case   )





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      return self.sp_model.get_piece_size()





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    List[Any]   		=	self.__dict__.copy()
      a__						:    Union[str, Any]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      a__						:    Optional[int]   		=	d

      # for backward compatibility
      if not hasattr(self       ,      "sp_model_kwargs"   ):
         a__						:    List[Any]   		=	{}

      a__						:    Tuple   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      return self.sp_model.piece_to_id(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





      a__						:    int   		=	self.sp_model.IdToPiece(snake_case   )
      return token





   def 						_snake_case			(			self       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      a__						:    Dict   		=	[]
      a__						:    List[str]   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(snake_case   ) + token
            a__						:    Any   		=	[]
         else:
            current_sub_tokens.append(snake_case   )
      out_string += self.sp_model.decode(snake_case   )
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=None   )					->	List[int]:
      """simple docstring"""





      if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = False   )					->	List[int]:
      """simple docstring"""





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

      a__						:    Any   		=	[1]
      if token_ids_a is None:
         return ([0] * len(snake_case   )) + suffix_ones
      return ([0] * len(snake_case   )) + ([0] * len(snake_case   )) + suffix_ones





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





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

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,      snake_case   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case       ,      "wb"   ) as fi:
            a__						:    Any   		=	self.sp_model.serialized_model_proto()
            fi.write(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 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # 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(lowerCamelCase )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    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."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

