
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowercase  : Union[str, Any]						   =							{
    """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""",
    """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""",
}
lowercase  : Dict						   =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   str:
       for attribute in key.split("""."""    ):
              lowercase							:					Tuple	     =				getattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       if weight_type is not None:
              lowercase							:					Any	     =				getattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ).shape
       else:
              lowercase							:					List[Any]	     =				hf_pointer.shape

       assert hf_shape == value.shape, (
           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							:					Any	     =				value
       elif weight_type == "weight_g":
              lowercase							:					Any	     =				value
       elif weight_type == "weight_v":
              lowercase							:					int	     =				value
       elif weight_type == "bias":
              lowercase							:					List[Any]	     =				value
       else:
              lowercase							:					int	     =				value

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					Tuple	     =				[]
       lowercase							:					List[Any]	     =				fairseq_model.state_dict()

       lowercase							:					Dict	     =				hf_model.feature_extractor
       lowercase							:					Any	     =				hf_model.adapter

       for name, value in fairseq_dict.items():
              lowercase							:					int	     =				False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     hf_model.config.feat_extract_norm == """group""" ,     )
                     lowercase							:					Any	     =				True
              elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
                     load_adapter(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     lowercase							:					Optional[int]	     =				True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
                                   lowercase							:					List[Any]	     =				True
                                   if "*" in mapped_key:
                                          lowercase							:					List[str]	     =				name.split(SCREAMING_SNAKE_CASE__    )[0].split("""."""    )[-2]
                                          lowercase							:					int	     =				mapped_key.replace("""*""" ,     SCREAMING_SNAKE_CASE__    )
                                   if "weight_g" in name:
                                          lowercase							:					int	     =				"""weight_g"""
                                   elif "weight_v" in name:
                                          lowercase							:					Optional[Any]	     =				"""weight_v"""
                                   elif "bias" in name:
                                          lowercase							:					str	     =				"""bias"""
                                   elif "weight" in name:
                                          lowercase							:					Optional[Any]	     =				"""weight"""
                                   else:
                                          lowercase							:					List[Any]	     =				None
                                   set_recursively(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE__    )

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					int	     =				full_name.split("""conv_layers."""    )[-1]
       lowercase							:					Optional[Any]	     =				name.split("""."""    )
       lowercase							:					Dict	     =				int(items[0]    )
       lowercase							:					Tuple	     =				int(items[1]    )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                     )
                     lowercase							:					List[Any]	     =				value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                     )
                     lowercase							:					str	     =				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:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                         " found."
                     )
                     lowercase							:					List[str]	     =				value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                     )
                     lowercase							:					Tuple	     =				value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					List[Any]	     =				full_name.split("""adaptor."""    )[-1]
       lowercase							:					Tuple	     =				name.split("""."""    )

       if items[1].isdigit():
              lowercase							:					Optional[int]	     =				int(items[1]    )
       else:
              lowercase							:					Union[str, Any]	     =				None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
                            lowercase							:					Any	     =				value
                            logger.info(f"Adapter proj layer norm bias was initialized from {full_name}."    )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
                            lowercase							:					str	     =				value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
                            lowercase							:					Optional[Any]	     =				value
                            logger.info(f"Adapter proj layer bias was initialized from {full_name}."    )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
                            lowercase							:					int	     =				value
                            logger.info(f"Adapter proj layer weight was initialized from {full_name}."    )
       elif isinstance(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
                     lowercase							:					int	     =				value
                     logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."    )
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
                     lowercase							:					str	     =				value
                     logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."    )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   str:
       lowercase							:					Optional[int]	     =				emb.weight.shape
       lowercase							:					int	     =				nn.Linear(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     bias=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				emb.weight.data
       return lin_layer







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     )			->   List[str]:
       lowercase							:					str	     =				WavaVecaConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__ ,     add_adapter=SCREAMING_SNAKE_CASE__ ,     adapter_stride=SCREAMING_SNAKE_CASE__ ,     adapter_kernel_size=SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__ ,     output_hidden_size=SCREAMING_SNAKE_CASE__ ,     )
       lowercase							:					Tuple	     =				MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # load model
       lowercase							:					str	     =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path] ,     arg_overrides={
               """config_yaml""": config_yaml_path,
               """data""": """/""".join(dict_path.split("""/"""    )[:-1]    ),
               """w2v_path""": checkpoint_path,
               """load_pretrained_decoder_from""": None,
           } ,     )
       lowercase							:					Optional[Any]	     =				model[0].eval()

       # load feature extractor
       lowercase							:					str	     =				WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__    )

       # set weights for wav2vec2 encoder
       lowercase							:					Dict	     =				WavaVecaModel(SCREAMING_SNAKE_CASE__    )

       recursively_load_weights_wavaveca(model.encoder ,     SCREAMING_SNAKE_CASE__    )

       # load decoder weights
       lowercase							:					str	     =				MBartForCausalLM(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,     strict=SCREAMING_SNAKE_CASE__    )
       logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"    )
       logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"    )

       lowercase							:					int	     =				SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__ ,     decoder=SCREAMING_SNAKE_CASE__    )
       lowercase							:					Any	     =				False

       lowercase							:					Union[str, Any]	     =				MBartaaTokenizer(SCREAMING_SNAKE_CASE__    )
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__    )

       lowercase							:					List[Any]	     =				hf_wavavec.config.to_dict()
       lowercase							:					Tuple	     =				tokenizer.pad_token_id
       lowercase							:					int	     =				tokenizer.bos_token_id
       lowercase							:					List[Any]	     =				tokenizer.eos_token_id
       lowercase							:					Union[str, Any]	     =				"""mbart50"""
       lowercase							:					List[Any]	     =				"""wav2vec2"""

       lowercase							:					List[Any]	     =				tokenizer.eos_token_id
       lowercase							:					str	     =				250_004
       lowercase							:					List[Any]	     =				tokenizer.eos_token_id

       lowercase							:					str	     =				SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__    )

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__    )
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 lowercase  : Union[str, Any]						   =							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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
 parser.add_argument(
     """--encoder_config_path""",
     default="""facebook/wav2vec2-xls-r-1b""",
     type=str,
     help="""Path to hf encoder wav2vec2 checkpoint config""",
 )
 parser.add_argument(
     """--decoder_config_path""",
     default="""facebook/mbart-large-50-one-to-many-mmt""",
     type=str,
     help="""Path to hf decoder checkpoint config""",
 )
 parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
 parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
 parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
 parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
 parser.add_argument("""--start_token_id""", default=250004, type=int, help="""`decoder_start_token_id` of model config""")

 lowercase  : Any						   =							parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)

lowercase  : Union[str, Any]						   =							{"""vocab_file""": """spiece.model"""}

lowercase  : Any						   =							{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowercase  : List[Any]						   =							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowercase  : Optional[int]						   =							0
lowercase  : Any						   =							1
lowercase  : Any						   =							2
lowercase  : str						   =							3
lowercase  : Optional[Any]						   =							4

class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    VOCAB_FILES_NAMES
    _a		:						Optional[Any]=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						List[str]=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						Union[str, Any]=    "left"
    def __init__(  self		,snake_case		,snake_case=False		,snake_case=True		,snake_case=False		,snake_case="<s>"		,snake_case="</s>"		,snake_case="<unk>"		,snake_case="<sep>"		,snake_case="<pad>"		,snake_case="<cls>"		,snake_case="<mask>"		,snake_case=["<eop>", "<eod>"]		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					str	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else mask_token

           lowercase							:					List[str]	     =				{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=snake_case		,remove_space=snake_case		,keep_accents=snake_case		,bos_token=snake_case		,eos_token=snake_case		,unk_token=snake_case		,sep_token=snake_case		,pad_token=snake_case		,cls_token=snake_case		,mask_token=snake_case		,additional_special_tokens=snake_case		,sp_model_kwargs=self.sp_model_kwargs		,**snake_case		,)

           lowercase							:					str	     =				3

           lowercase							:					str	     =				do_lower_case
           lowercase							:					List[Any]	     =				remove_space
           lowercase							:					Dict	     =				keep_accents
           lowercase							:					Union[str, Any]	     =				vocab_file

           lowercase							:					Any	     =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(snake_case				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return len(self.sp_model				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					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				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.__dict__.copy()
           lowercase							:					List[str]	     =				None
           return state
    def __setstate__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				d

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

           lowercase							:					List[str]	     =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(self.vocab_file				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.remove_space:
                  lowercase							:					Optional[int]	     =				""" """.join(inputs.strip().split()				)
           else:
                  lowercase							:					Optional[int]	     =				inputs
           lowercase							:					Dict	     =				outputs.replace("""``"""		,"""\""""				).replace("""''"""		,"""\""""				)

           if not self.keep_accents:
                  lowercase							:					int	     =				unicodedata.normalize("""NFKD"""		,snake_case				)
                  lowercase							:					Any	     =				"""""".join([c for c in outputs if not unicodedata.combining(snake_case				)]				)
           if self.do_lower_case:
                  lowercase							:					Any	     =				outputs.lower()

           return outputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.preprocess_text(snake_case				)
           lowercase							:					Any	     =				self.sp_model.encode(snake_case		,out_type=snake_case				)
           lowercase							:					Tuple	     =				[]
           for piece in pieces:
                  if len(snake_case				) > 1 and piece[-1] == str(""","""				) and piece[-2].isdigit():
                         lowercase							:					Dict	     =				self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case		,""""""				)				)
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]				) == 1:
                                       lowercase							:					Any	     =				cur_pieces[1:]
                                else:
                                       lowercase							:					List[str]	     =				cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]				)
                         new_pieces.extend(snake_case				)
                  else:
                         new_pieces.append(snake_case				)

           return new_pieces
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.sp_model.PieceToId(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.sp_model.IdToPiece(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				"""""".join(snake_case				).replace(snake_case		,""" """				).strip()
           return out_string
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = False		,snake_case = None		,snake_case = True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					List[str]	     =				kwargs.pop("""use_source_tokenizer"""		,snake_case				)

           lowercase							:					List[str]	     =				self.convert_ids_to_tokens(snake_case		,skip_special_tokens=snake_case				)

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(snake_case				)				)
                                lowercase							:					Union[str, Any]	     =				[]
                         sub_texts.append(snake_case				)
                  else:
                         current_sub_text.append(snake_case				)
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(snake_case				)				)

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           lowercase							:					Any	     =				"""""".join(snake_case				)

           lowercase							:					str	     =				(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowercase							:					int	     =				self.clean_up_tokenization(snake_case				)
                  return clean_text
           else:
                  return text
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					int	     =				[self.sep_token_id]
           lowercase							:					str	     =				[self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = False				):



           '''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				)

           if token_ids_a is not None:
                  return ([0] * len(snake_case				)) + [1] + ([0] * len(snake_case				)) + [1, 1]
           return ([0] * len(snake_case				)) + [1, 1]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				[self.sep_token_id]
           lowercase							:					Any	     =				[2]

           if token_ids_a is None:
                  return len(token_ids_a + sep				) * [0] + cls_segment_id
           return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           if not os.path.isdir(snake_case				):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                  return
           lowercase							:					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:
                         lowercase							:					str	     =				self.sp_model.serialized_model_proto()
                         fi.write(snake_case				)

           return (out_vocab_file,)
