import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__lowerCamelCase			: int							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__lowerCamelCase			: Optional[Any]							   =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {}
  with open(_lowerCAmelCase ,     """r"""	) as file:
    for line_number, line in enumerate(_lowerCAmelCase	):
      SCREAMING_SNAKE_CASE__										=      line.strip()
      if line:
        SCREAMING_SNAKE_CASE__										=      line.split()
        SCREAMING_SNAKE_CASE__										=      line_number
        SCREAMING_SNAKE_CASE__										=      words[0]
        SCREAMING_SNAKE_CASE__										=      value
  return result



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       str	)							->					int:







  """simple docstring"""





  for attribute in key.split("""."""	):
    SCREAMING_SNAKE_CASE__										=      getattr(_lowerCAmelCase ,     _lowerCAmelCase	)

  SCREAMING_SNAKE_CASE__										=      None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(_lowerCAmelCase	):
      SCREAMING_SNAKE_CASE__										=      PARAM_MAPPING[full_name.split("""."""	)[-1]]
      SCREAMING_SNAKE_CASE__										=      '''param'''

  if weight_type is not None and weight_type != "param":
    SCREAMING_SNAKE_CASE__										=      getattr(_lowerCAmelCase ,     _lowerCAmelCase	).shape
  elif weight_type is not None and weight_type == "param":
    SCREAMING_SNAKE_CASE__										=      hf_pointer
    for attribute in hf_param_name.split("""."""	):
      SCREAMING_SNAKE_CASE__										=      getattr(_lowerCAmelCase ,     _lowerCAmelCase	)
    SCREAMING_SNAKE_CASE__										=      shape_pointer.shape

    # let's reduce dimension
    SCREAMING_SNAKE_CASE__										=      value[0]
  else:
    SCREAMING_SNAKE_CASE__										=      hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
        f""" {value.shape} for {full_name}"""	)

  if weight_type == "weight":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "weight_g":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "weight_v":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "bias":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""	):
      SCREAMING_SNAKE_CASE__										=      getattr(_lowerCAmelCase ,     _lowerCAmelCase	)
    SCREAMING_SNAKE_CASE__										=      value
  else:
    SCREAMING_SNAKE_CASE__										=      value

  logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Dict ,     __UpperCamelCase    :       List[Any]	)							->					List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(_lowerCAmelCase	):
      SCREAMING_SNAKE_CASE__										=      PARAM_MAPPING[full_name.split("""."""	)[-1]]
      SCREAMING_SNAKE_CASE__										=      '''param'''

  if weight_type is not None and weight_type != "param":
    SCREAMING_SNAKE_CASE__										=      '''.'''.join([key, weight_type]	)
  elif weight_type is not None and weight_type == "param":
    SCREAMING_SNAKE_CASE__										=      '''.'''.join([key, hf_param_name]	)
  else:
    SCREAMING_SNAKE_CASE__										=      key

  SCREAMING_SNAKE_CASE__										=      value if '''lm_head''' in full_key else value[0]


__lowerCamelCase			: Tuple							   =		{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Any=None ,     __UpperCamelCase    :       Optional[Any]=None	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      False
  for key, mapped_key in MAPPING.items():
    SCREAMING_SNAKE_CASE__										=      '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
      SCREAMING_SNAKE_CASE__										=      True
      if "*" in mapped_key:
        SCREAMING_SNAKE_CASE__										=      name.split(_lowerCAmelCase	)[0].split("""."""	)[-2]
        SCREAMING_SNAKE_CASE__										=      mapped_key.replace("""*""" ,     _lowerCAmelCase	)
      if "weight_g" in name:
        SCREAMING_SNAKE_CASE__										=      '''weight_g'''
      elif "weight_v" in name:
        SCREAMING_SNAKE_CASE__										=      '''weight_v'''
      elif "bias" in name:
        SCREAMING_SNAKE_CASE__										=      '''bias'''
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        SCREAMING_SNAKE_CASE__										=      '''weight'''
      else:
        SCREAMING_SNAKE_CASE__										=      None
      if hf_dict is not None:
        rename_dict(_lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase	)
      else:
        set_recursively(_lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase	)
      return is_used
  return is_used



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Any	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE__										=      hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE__										=      False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     hf_model.config.feat_extract_norm == """group""" ,     )
      SCREAMING_SNAKE_CASE__										=      True
    else:
      SCREAMING_SNAKE_CASE__										=      load_wavaveca_layer(_lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase	)
    if not is_used:
      unused_weights.append(_lowerCAmelCase	)

  logger.warning(f"""Unused weights: {unused_weights}"""	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Optional[int]	)							->					Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      full_name.split("""conv_layers."""	)[-1]
  SCREAMING_SNAKE_CASE__										=      name.split("""."""	)
  SCREAMING_SNAKE_CASE__										=      int(items[0]	)
  SCREAMING_SNAKE_CASE__										=      int(items[1]	)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
      SCREAMING_SNAKE_CASE__										=      value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
      SCREAMING_SNAKE_CASE__										=      value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
      SCREAMING_SNAKE_CASE__										=      value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
      SCREAMING_SNAKE_CASE__										=      value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
  else:
    unused_weights.append(_lowerCAmelCase	)



@torch.no_grad()
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Dict=None ,     __UpperCamelCase    :       List[Any]=None ,     __UpperCamelCase    :       int=True ,     __UpperCamelCase    :       Optional[int]=False	)							->					Dict:







  """simple docstring"""





  if config_path is not None:
    SCREAMING_SNAKE_CASE__										=      WavaVecaConfig.from_pretrained(_lowerCAmelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      WavaVecaConfig()

  if is_seq_class:
    SCREAMING_SNAKE_CASE__										=      read_txt_into_dict(_lowerCAmelCase	)
    SCREAMING_SNAKE_CASE__										=      idalabel
    SCREAMING_SNAKE_CASE__										=      WavaVecaForSequenceClassification(_lowerCAmelCase	)
    SCREAMING_SNAKE_CASE__										=      WavaVecaFeatureExtractor(
        feature_size=1 ,     sampling_rate=1_60_00 ,     padding_value=0 ,     do_normalize=_lowerCAmelCase ,     return_attention_mask=_lowerCAmelCase ,     )
    feature_extractor.save_pretrained(_lowerCAmelCase	)

  elif is_finetuned:
    if dict_path:
      SCREAMING_SNAKE_CASE__										=      Dictionary.load(_lowerCAmelCase	)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      SCREAMING_SNAKE_CASE__										=      target_dict.pad_index
      SCREAMING_SNAKE_CASE__										=      target_dict.bos_index
      SCREAMING_SNAKE_CASE__										=      target_dict.eos_index
      SCREAMING_SNAKE_CASE__										=      len(target_dict.symbols	)
      SCREAMING_SNAKE_CASE__										=      os.path.join(_lowerCAmelCase ,     """vocab.json"""	)
      if not os.path.isdir(_lowerCAmelCase	):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCAmelCase	)	)
        return
      os.makedirs(_lowerCAmelCase ,     exist_ok=_lowerCAmelCase	)
      SCREAMING_SNAKE_CASE__										=      target_dict.indices

      # fairseq has the <pad> and <s> switched
      SCREAMING_SNAKE_CASE__										=      0
      SCREAMING_SNAKE_CASE__										=      1
      with open(_lowerCAmelCase ,     """w""" ,     encoding="""utf-8"""	) as vocab_handle:
        json.dump(_lowerCAmelCase ,     _lowerCAmelCase	)
      SCREAMING_SNAKE_CASE__										=      WavaVecaCTCTokenizer(
          _lowerCAmelCase ,     unk_token=target_dict.unk_word ,     pad_token=target_dict.pad_word ,     bos_token=target_dict.bos_word ,     eos_token=target_dict.eos_word ,     word_delimiter_token="""|""" ,     do_lower_case=_lowerCAmelCase ,     )
      SCREAMING_SNAKE_CASE__										=      True if config.feat_extract_norm == '''layer''' else False
      SCREAMING_SNAKE_CASE__										=      WavaVecaFeatureExtractor(
          feature_size=1 ,     sampling_rate=1_60_00 ,     padding_value=0 ,     do_normalize=_lowerCAmelCase ,     return_attention_mask=_lowerCAmelCase ,     )
      SCREAMING_SNAKE_CASE__										=      WavaVecaProcessor(feature_extractor=_lowerCAmelCase ,     tokenizer=_lowerCAmelCase	)
      processor.save_pretrained(_lowerCAmelCase	)

    SCREAMING_SNAKE_CASE__										=      WavaVecaForCTC(_lowerCAmelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      WavaVecaForPreTraining(_lowerCAmelCase	)

  if is_finetuned or is_seq_class:
    SCREAMING_SNAKE_CASE__										=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path] ,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
  else:
    SCREAMING_SNAKE_CASE__										=      argparse.Namespace(task="""audio_pretraining"""	)
    SCREAMING_SNAKE_CASE__										=      fairseq.tasks.setup_task(_lowerCAmelCase	)

    SCREAMING_SNAKE_CASE__										=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=_lowerCAmelCase	)

  SCREAMING_SNAKE_CASE__										=      model[0].eval()

  recursively_load_weights(_lowerCAmelCase ,     _lowerCAmelCase ,     not is_finetuned	)

  hf_wavavec.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
   parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   parser.add_argument(
       '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   parser.add_argument(
       '''--is_seq_class''',
       action='''store_true''',
       help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
   )
   __lowerCamelCase			: Any							   =		parser.parse_args()

   __lowerCamelCase			: int							   =		not args.not_finetuned and not args.is_seq_class
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.dict_path,
       is_finetuned,
       args.is_seq_class,
   )





'''simple docstring'''






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


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "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"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






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







	def __init__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple="<s>"		,			__snake_case			:     List[Any]="</s>"		,			__snake_case			:     int="</s>"		,			__snake_case			:     Any="<s>"		,			__snake_case			:     Optional[int]="<unk>"		,			__snake_case			:     Union[str, Any]="<pad>"		,			__snake_case			:     Union[str, Any]="<mask>"		,			__snake_case			:     Optional[Dict[str, Any]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase :       int 							=		AddedToken(__snake_case		,			lstrip=__snake_case		,			rstrip=__snake_case		) if isinstance(__snake_case		,			__snake_case		) else mask_token

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

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

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

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







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								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		)

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







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[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      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       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      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		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(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, 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:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





