



import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a__      :				Union[str, Any]   	=			logging.get_logger("transformers.models.speecht5")

a__      :				Dict   	=			{
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
a__      :				str   	=			{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
a__      :				str   	=			{
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
a__      :				Tuple   	=			{
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
a__      :				Optional[int]   	=			{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a__      :				Dict   	=			{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a__      :				List[Any]   	=			{
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
a__      :				str   	=			{
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
a__      :				str   	=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a__      :				List[Any]   	=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a__      :				Dict   	=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a__      :				Union[str, Any]   	=			[]
a__      :				Tuple   	=			[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
a__      :				List[Any]   	=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a__      :				int   	=			IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a__      :				Dict   	=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      for attribute in key.split('.'	):
            lowercase__     =  getattr(A__						,  A__	)

      if weight_type is not None:
            lowercase__     =  getattr(A__						,  A__	).shape
      else:
            lowercase__     =  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":
            lowercase__     =  value
      elif weight_type == "weight_g":
            lowercase__     =  value
      elif weight_type == "weight_v":
            lowercase__     =  value
      elif weight_type == "bias":
            lowercase__     =  value
      elif weight_type == "running_mean":
            lowercase__     =  value
      elif weight_type == "running_var":
            lowercase__     =  value
      elif weight_type == "num_batches_tracked":
            lowercase__     =  value
      else:
            lowercase__     =  value

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

def   _lowerCAmelCase   (						A__						,  A__	):
      for key in ignore_keys:
            if key.endswith('.*'	):
                  if name.startswith(key[:-1]	):
                        return True
            elif ".*." in key:
                  lowercase__,	lowercase__     =  key.split('.*.'	)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  []

      if task == "s2t":
            lowercase__     =  hf_model.speechta.encoder.prenet.feature_encoder
            lowercase__     =  MAPPING_S2T
            lowercase__     =  IGNORE_KEYS_S2T
      elif task == "t2s":
            lowercase__     =  None
            lowercase__     =  MAPPING_T2S
            lowercase__     =  IGNORE_KEYS_T2S
      elif task == "s2s":
            lowercase__     =  hf_model.speechta.encoder.prenet.feature_encoder
            lowercase__     =  MAPPING_S2S
            lowercase__     =  IGNORE_KEYS_S2S
      else:
            raise ValueError(F'''Unsupported task: {task}'''	)

      for name, value in fairseq_dict.items():
            if should_ignore(A__						,  A__	):
                  logger.info(F'''{name} was ignored'''	)
                  continue

            lowercase__     =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      A__						,  A__						,  A__						,  A__						,  hf_model.config.feat_extract_norm == 'group'						,  )
                  lowercase__     =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                        if "*" in key:
                              lowercase__,	lowercase__     =  key.split('.*.'	)
                              if prefix in name and suffix in name:
                                    lowercase__     =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              lowercase__     =  True
                              if "*" in mapped_key:
                                    lowercase__     =  name.split(A__	)[0].split('.'	)[-2]
                                    lowercase__     =  mapped_key.replace('*'						,  A__	)
                              if "weight_g" in name:
                                    lowercase__     =  'weight_g'
                              elif "weight_v" in name:
                                    lowercase__     =  'weight_v'
                              elif "bias" in name:
                                    lowercase__     =  'bias'
                              elif "weight" in name:
                                    lowercase__     =  'weight'
                              elif "running_mean" in name:
                                    lowercase__     =  'running_mean'
                              elif "running_var" in name:
                                    lowercase__     =  'running_var'
                              elif "num_batches_tracked" in name:
                                    lowercase__     =  'num_batches_tracked'
                              else:
                                    lowercase__     =  None
                              set_recursively(A__						,  A__						,  A__						,  A__						,  A__	)
                        continue
            if not is_used:
                  unused_weights.append(A__	)

      logger.warning(F'''Unused weights: {unused_weights}'''	)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      lowercase__     =  full_name.split('conv_layers.'	)[-1]
      lowercase__     =  name.split('.'	)
      lowercase__     =  int(items[0]	)
      lowercase__     =  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.'''	)
                  lowercase__     =  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.'''	)
                  lowercase__     =  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.'''	)
                  lowercase__     =  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.'''	)
                  lowercase__     =  value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
      else:
            unused_weights.append(A__	)

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__=None						,  A__=None						,  A__=None						,  ):
      if config_path is not None:
            lowercase__     =  SpeechTaConfig.from_pretrained(A__	)
      else:
            lowercase__     =  SpeechTaConfig()

      if task == "s2t":
            lowercase__     =  config.max_text_positions
            lowercase__     =  SpeechTaForSpeechToText(A__	)
      elif task == "t2s":
            lowercase__     =  1_876
            lowercase__     =  600
            lowercase__     =  config.max_speech_positions
            lowercase__     =  SpeechTaForTextToSpeech(A__	)
      elif task == "s2s":
            lowercase__     =  1_876
            lowercase__     =  config.max_speech_positions
            lowercase__     =  SpeechTaForSpeechToSpeech(A__	)
      else:
            raise ValueError(F'''Unknown task name: {task}'''	)

      if vocab_path:
            lowercase__     =  SpeechTaTokenizer(A__						,  model_max_length=config.max_text_positions	)

            # Mask token behaves like a normal word, i.e. include the space before it
            lowercase__     =  AddedToken('<mask>'						,  lstrip=A__						,  rstrip=A__	)
            lowercase__     =  mask_token
            tokenizer.add_special_tokens({'mask_token': mask_token}	)
            tokenizer.add_tokens(['<ctc_blank>']	)

      lowercase__     =  SpeechTaFeatureExtractor()
      lowercase__     =  SpeechTaProcessor(tokenizer=A__						,  feature_extractor=A__	)
      processor.save_pretrained(A__	)

      lowercase__     =  torch.load(A__	)
      recursively_load_weights(fairseq_checkpoint['model']						,  A__						,  A__	)

      model.save_pretrained(A__	)

      if repo_id:
            print('Pushing to the hub...'	)
            processor.push_to_hub(A__	)
            model.push_to_hub(A__	)


if __name__ == "__main__":
       a__      :				Any   	=			argparse.ArgumentParser()
       parser.add_argument(
           "--task",
           default="s2t",
           type=str,
           help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
       )
       parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
       )

       a__      :				str   	=			parser.parse_args()
       convert_speechta_checkpoint(
           args.task,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.vocab_path,
           args.push_to_hub,
       )





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


a__      :				List[Any]   	=			logging.get_logger(__name__)


a__      :				Optional[int]   	=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
a__      :				List[Any]   	=			{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

a__      :				int   	=			{
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Optional[Any]       				=    VOCAB_FILES_NAMES
   A    :  Dict       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  str       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  int       				=    ["input_ids", "attention_mask"]
   A    :  Any       				=    BartTokenizer


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Any=None			,				lowerCAmelCase  :			Optional[int]=None			,				lowerCAmelCase  :			List[Any]=None			,				lowerCAmelCase  :			str="replace"			,				lowerCAmelCase  :			str="<s>"			,				lowerCAmelCase  :			int="</s>"			,				lowerCAmelCase  :			Optional[int]="</s>"			,				lowerCAmelCase  :			Union[str, Any]="<s>"			,				lowerCAmelCase  :			str="<unk>"			,				lowerCAmelCase  :			int="<pad>"			,				lowerCAmelCase  :			int="<mask>"			,				lowerCAmelCase  :			Dict=False			,				lowerCAmelCase  :			List[Any]=True			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				Optional[Any]:




         """simple docstring"""

         super().__init__(
             lowerCAmelCase			,				lowerCAmelCase			,				tokenizer_file=lowerCAmelCase			,				errors=lowerCAmelCase			,				bos_token=lowerCAmelCase			,				eos_token=lowerCAmelCase			,				sep_token=lowerCAmelCase			,				cls_token=lowerCAmelCase			,				unk_token=lowerCAmelCase			,				pad_token=lowerCAmelCase			,				mask_token=lowerCAmelCase			,				add_prefix_space=lowerCAmelCase			,				trim_offsets=lowerCAmelCase			,				**lowerCAmelCase			,				)

         lowercase__     =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'			,				lowerCAmelCase) != add_prefix_space:
               lowercase__     =  getattr(lowerCAmelCase			,				pre_tok_state.pop('type'))
               lowercase__     =  add_prefix_space
               lowercase__     =  pre_tok_class(**lowerCAmelCase)

         lowercase__     =  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         lowercase__     =  'post_processor'
         lowercase__     =  getattr(self.backend_tokenizer			,				lowerCAmelCase			,				lowerCAmelCase)
         if tokenizer_component_instance:
               lowercase__     =  json.loads(tokenizer_component_instance.__getstate__())

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                     lowercase__     =  tuple(state['sep'])
               if "cls" in state:
                     lowercase__     =  tuple(state['cls'])

               lowercase__     =  False

               if state.get('add_prefix_space'			,				lowerCAmelCase) != add_prefix_space:
                     lowercase__     =  add_prefix_space
                     lowercase__     =  True

               if state.get('trim_offsets'			,				lowerCAmelCase) != trim_offsets:
                     lowercase__     =  trim_offsets
                     lowercase__     =  True

               if changes_to_apply:
                     lowercase__     =  getattr(lowerCAmelCase			,				state.pop('type'))
                     lowercase__     =  component_class(**lowerCAmelCase)
                     setattr(self.backend_tokenizer			,				lowerCAmelCase			,				lowerCAmelCase)


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

         if self._mask_token is None:
               if self.verbose:
                     logger.error('Using mask_token, but it is not set yet.')
               return None
         return str(self._mask_token)


   @mask_token.setter
   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  AddedToken(lowerCAmelCase			,				lstrip=lowerCAmelCase			,				rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase			,				lowerCAmelCase) else value
         lowercase__     =  value


   def 			UpperCAmelCase		(						self  :			List[str]			,				*lowerCAmelCase  :			Tuple			,				**lowerCAmelCase  :			Optional[int])	->				BatchEncoding:




         """simple docstring"""

         lowercase__     =  kwargs.get('is_split_into_words'			,				lowerCAmelCase)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   'to use it with pretokenized inputs.')

         return super()._batch_encode_plus(*lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str			,				*lowerCAmelCase  :			Tuple			,				**lowerCAmelCase  :			str)	->				BatchEncoding:




         """simple docstring"""

         lowercase__     =  kwargs.get('is_split_into_words'			,				lowerCAmelCase)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   'to use it with pretokenized inputs.')

         return super()._encode_plus(*lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

         lowercase__     =  self._tokenizer.model.save(lowerCAmelCase			,				name=lowerCAmelCase)
         return tuple(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[int]=None)	->				Tuple:




         """simple docstring"""

         lowercase__     =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
               return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None)	->				List[int]:




         """simple docstring"""

         lowercase__     =  [self.sep_token_id]
         lowercase__     =  [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]
