code: 
  
  
  
  
  """simple docstring"""
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  __A 		=			logging.get_logger(__name__)
  
  __A 		=			{
      "post_extract_proj": "feature_projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
      "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
      "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
      "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
      "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
      "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
      "output.dense": "encoder.encoder.layer.*.output.dense",
      "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
      "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
      "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
      "encoder.upsample.0": "encoder.upsample.projection",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     int       ,     lowercase__  :     Tuple       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     int       ,     lowercase__  :     List[Any]   ):
  
  
  
  
  
  
  		for attribute in key.split("."   ):
  				snake_case		:						Tuple								=				getattr(UpperCamelCase_       ,     UpperCamelCase_   )
  
  		if weight_type is not None:
  				snake_case		:						str								=				getattr(UpperCamelCase_       ,     UpperCamelCase_   ).shape
  		else:
  				snake_case		:						List[str]								=				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":
  				snake_case		:						Union[str, Any]								=				value
  		elif weight_type == "weight_g":
  				snake_case		:						int								=				value
  		elif weight_type == "weight_v":
  				snake_case		:						Union[str, Any]								=				value
  		elif weight_type == "bias":
  				snake_case		:						Any								=				value
  		else:
  				snake_case		:						int								=				value
  
  		logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''   )
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]   ):
  		snake_case		:						int								=				[]
  		snake_case		:						List[Any]								=				fairseq_model.state_dict()
  
  		snake_case		:						Optional[Any]								=				hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
  		for name, value in fairseq_dict.items():
  				snake_case		:						Optional[Any]								=				False
  				if "conv_layers" in name:
  						load_conv_layer(
  						    UpperCamelCase_       ,     UpperCamelCase_       ,     UpperCamelCase_       ,     UpperCamelCase_       ,     hf_model.config.feat_extract_norm == "group"       ,     )
  						snake_case		:						Optional[Any]								=				True
  				else:
  						for key, mapped_key in MAPPING.items():
  								snake_case		:						Optional[Any]								=				"sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
  
  								if key in name or key.split("w2v_model."   )[-1] == name.split("."   )[0]:
  										snake_case		:						str								=				True
  										if "*" in mapped_key:
  												snake_case		:						List[Any]								=				name.split(UpperCamelCase_   )[0].split("."   )[-2]
  												if not layer_index.isnumeric():
  														continue
  												snake_case		:						str								=				mapped_key.replace("*"       ,     UpperCamelCase_   )
  										if "weight_g" in name:
  												snake_case		:						List[Any]								=				"weight_g"
  										elif "weight_v" in name:
  												snake_case		:						Dict								=				"weight_v"
  										elif "weight" in name:
  												snake_case		:						Optional[int]								=				"weight"
  										elif "bias" in name:
  												snake_case		:						List[Any]								=				"bias"
  										else:
  												snake_case		:						Optional[Any]								=				None
  										set_recursively(UpperCamelCase_       ,     UpperCamelCase_       ,     UpperCamelCase_       ,     UpperCamelCase_       ,     UpperCamelCase_   )
  								continue
  				if not is_used:
  						unused_weights.append(UpperCamelCase_   )
  
  		logger.warning(F'''Unused weights: {unused_weights}'''   )
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     int       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     Optional[Any]       ,     lowercase__  :     int       ,     lowercase__  :     str   ):
  		snake_case		:						Optional[int]								=				full_name.split("conv_layers."   )[-1]
  		snake_case		:						Union[str, Any]								=				name.split("."   )
  		snake_case		:						Optional[Any]								=				int(items[0]   )
  		snake_case		:						Optional[Any]								=				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.'''
  						)
  						snake_case		:						str								=				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.'''
  						)
  						snake_case		:						Tuple								=				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."
  						)
  						snake_case		:						Tuple								=				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.'''
  						)
  						snake_case		:						str								=				value
  						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
  		else:
  				unused_weights.append(UpperCamelCase_   )
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Tuple       ,     lowercase__  :     int   ):
  		snake_case		:						Dict								=				SEWDConfig()
  		if is_finetuned:
  				snake_case		:						List[Any]								=				model.wav_encoder.wav_model.cfg
  		else:
  				snake_case		:						List[Any]								=				model.cfg
  
  		snake_case		:						Optional[int]								=				fs_config.conv_bias
  		snake_case		:						Union[str, Any]								=				eval(fs_config.conv_feature_layers   )
  		snake_case		:						Any								=				[x[0] for x in conv_layers]
  		snake_case		:						List[str]								=				[x[1] for x in conv_layers]
  		snake_case		:						Optional[Any]								=				[x[2] for x in conv_layers]
  		snake_case		:						List[str]								=				"gelu"
  		snake_case		:						List[Any]								=				"layer" if fs_config.extractor_mode == "layer_norm" else "group"
  		snake_case		:						List[str]								=				0.0
  		snake_case		:						Optional[int]								=				fs_config.activation_fn.name
  		snake_case		:						Dict								=				fs_config.encoder_embed_dim
  		snake_case		:						Union[str, Any]								=				0.02
  		snake_case		:						List[Any]								=				fs_config.encoder_ffn_embed_dim
  		snake_case		:						List[str]								=				1E-5
  		snake_case		:						List[Any]								=				fs_config.encoder_layerdrop
  		snake_case		:						Dict								=				fs_config.encoder_attention_heads
  		snake_case		:						List[Any]								=				fs_config.conv_pos_groups
  		snake_case		:						List[str]								=				fs_config.conv_pos
  		snake_case		:						Union[str, Any]								=				len(UpperCamelCase_   )
  		snake_case		:						Union[str, Any]								=				fs_config.encoder_layers
  		snake_case		:						List[Any]								=				fs_config.squeeze_factor
  		# DeBERTa-specific parameters:
  		snake_case		:						Union[str, Any]								=				fs_config.max_position_embeddings
  		snake_case		:						List[Any]								=				fs_config.position_buckets
  		snake_case		:						Any								=				fs_config.share_att_key
  		snake_case		:						Dict								=				fs_config.relative_attention
  		snake_case		:						List[str]								=				fs_config.position_biased_input
  		snake_case		:						Optional[int]								=				tuple(fs_config.pos_att_type.split("|"   )   )
  		snake_case		:						Any								=				fs_config.norm_rel_ebd
  
  		# take care of any params that are overridden by the Wav2VecCtc model
  		if is_finetuned:
  				snake_case		:						Union[str, Any]								=				model.cfg
  				snake_case		:						Tuple								=				fs_config.final_dropout
  				snake_case		:						List[Any]								=				fs_config.layerdrop
  		snake_case		:						Union[str, Any]								=				fs_config.activation_dropout
  		snake_case		:						List[str]								=				fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  		snake_case		:						int								=				fs_config.attention_dropout
  		snake_case		:						Dict								=				fs_config.dropout_input
  		snake_case		:						Any								=				fs_config.dropout
  		snake_case		:						str								=				fs_config.mask_channel_length
  		snake_case		:						Optional[int]								=				fs_config.mask_channel_prob
  		snake_case		:						List[Any]								=				fs_config.mask_length
  		snake_case		:						Tuple								=				fs_config.mask_prob
  
  		snake_case		:						Tuple								=				"Wav2Vec2FeatureExtractor"
  		snake_case		:						Union[str, Any]								=				"Wav2Vec2CTCTokenizer"
  
  		return config
  
  
  
  @torch.no_grad()
  def   UpperCamelCase__     (		lowercase__  :     Optional[int]       ,     lowercase__  :     Optional[int]       ,     lowercase__  :     Any=None       ,     lowercase__  :     Optional[int]=None       ,     lowercase__  :     List[Any]=True   ):
  
  
  
  
  
  
  		if is_finetuned:
  				snake_case			,				snake_case			,				snake_case		:						str								=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
  				    [checkpoint_path]       ,     arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   )}   )
  		else:
  				snake_case			,				snake_case			,				snake_case		:						int								=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )
  
  		if config_path is not None:
  				snake_case		:						str								=				SEWDConfig.from_pretrained(UpperCamelCase_   )
  		else:
  				snake_case		:						Tuple								=				convert_config(model[0]       ,     UpperCamelCase_   )
  		snake_case		:						str								=				model[0].eval()
  
  		snake_case		:						Optional[int]								=				True if config.feat_extract_norm == "layer" else False
  		snake_case		:						Dict								=				WavaVecaFeatureExtractor(
  		    feature_size=1       ,     sampling_rate=1_6000       ,     padding_value=0       ,     do_normalize=UpperCamelCase_       ,     return_attention_mask=UpperCamelCase_       ,     )
  
  		if is_finetuned:
  				if dict_path:
  						snake_case		:						Union[str, Any]								=				Dictionary.load(UpperCamelCase_   )
  
  						# important change bos & pad token id since CTC symbol is <pad> and
  						# not <s> as in fairseq
  						snake_case		:						str								=				target_dict.pad_index
  						snake_case		:						Union[str, Any]								=				target_dict.bos_index
  						snake_case		:						Optional[Any]								=				target_dict.pad_index
  						snake_case		:						Optional[int]								=				target_dict.bos_index
  						snake_case		:						Dict								=				target_dict.eos_index
  						snake_case		:						str								=				len(target_dict.symbols   )
  						snake_case		:						Any								=				os.path.join(UpperCamelCase_       ,     "vocab.json"   )
  						if not os.path.isdir(UpperCamelCase_   ):
  								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCamelCase_   )   )
  								return
  						os.makedirs(UpperCamelCase_       ,     exist_ok=UpperCamelCase_   )
  						with open(UpperCamelCase_       ,     "w"       ,     encoding="utf-8"   ) as vocab_handle:
  								json.dump(target_dict.indices       ,     UpperCamelCase_   )
  						snake_case		:						str								=				WavaVecaCTCTokenizer(
  						    UpperCamelCase_       ,     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=UpperCamelCase_       ,     )
  						snake_case		:						Optional[int]								=				WavaVecaProcessor(feature_extractor=UpperCamelCase_       ,     tokenizer=UpperCamelCase_   )
  						processor.save_pretrained(UpperCamelCase_   )
  
  				snake_case		:						str								=				SEWDForCTC(UpperCamelCase_   )
  		else:
  				snake_case		:						str								=				SEWDModel(UpperCamelCase_   )
  				feature_extractor.save_pretrained(UpperCamelCase_   )
  
  		recursively_load_weights(UpperCamelCase_       ,     UpperCamelCase_       ,     UpperCamelCase_   )
  
  		hf_model.save_pretrained(UpperCamelCase_   )
  
  
  if __name__ == "__main__":
  							__A 		=			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(
  							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  							)
  							__A 		=			parser.parse_args()
  							convert_sew_checkpoint(
  							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import os
  import re
  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 logging
  
  
  __A 		=			logging.get_logger(__name__)
  __A 		=			{"vocab_file": "spiece.model"}
  
  __A 		=			{
      "vocab_file": {
          "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
          "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
      },
  }
  
  __A 		=			{
      "google/fnet-base": 512,
      "google/fnet-large": 512,
  }
  
  __A 		=			"▁"
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Dict								=     VOCAB_FILES_NAMES
  			a__ :      Tuple								=     PRETRAINED_VOCAB_FILES_MAP
  			a__ :      int								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			a__ :      List[Any]								=     ["""input_ids""", """token_type_ids"""]
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE="<unk>"				, SCREAMING_SNAKE_CASE="[SEP]"				, SCREAMING_SNAKE_CASE="<pad>"				, SCREAMING_SNAKE_CASE="[CLS]"				, SCREAMING_SNAKE_CASE="[MASK]"				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				(
  					    AddedToken(SCREAMING_SNAKE_CASE				, lstrip=SCREAMING_SNAKE_CASE				, rstrip=SCREAMING_SNAKE_CASE				, normalized=SCREAMING_SNAKE_CASE							)
  					    if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					    else mask_token
  					)
  
  					snake_case		:						Optional[int]								=				{} if sp_model_kwargs is None else sp_model_kwargs
  
  					super().__init__(
  					    do_lower_case=SCREAMING_SNAKE_CASE				, remove_space=SCREAMING_SNAKE_CASE				, keep_accents=SCREAMING_SNAKE_CASE				, unk_token=SCREAMING_SNAKE_CASE				, sep_token=SCREAMING_SNAKE_CASE				, pad_token=SCREAMING_SNAKE_CASE				, cls_token=SCREAMING_SNAKE_CASE				, mask_token=SCREAMING_SNAKE_CASE				, sp_model_kwargs=self.sp_model_kwargs				, **SCREAMING_SNAKE_CASE				, )
  
  					snake_case		:						List[str]								=				do_lower_case
  					snake_case		:						Any								=				remove_space
  					snake_case		:						Union[str, Any]								=				keep_accents
  					snake_case		:						Optional[int]								=				vocab_file
  
  					snake_case		:						Dict								=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  					self.sp_model.Load(SCREAMING_SNAKE_CASE							)
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return len(self.sp_model							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				{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 __getstate__(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				self.__dict__.copy()
  					snake_case		:						List[Any]								=				None
  					return state
  
  
  			def __setstate__(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				d
  
  					# for backward compatibility
  					if not hasattr(self				, "sp_model_kwargs"							):
  							snake_case		:						List[Any]								=				{}
  
  					snake_case		:						Tuple								=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  					self.sp_model.Load(self.vocab_file							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if self.remove_space:
  							snake_case		:						int								=				" ".join(inputs.strip().split()							)
  					else:
  							snake_case		:						Union[str, Any]								=				inputs
  					snake_case		:						Union[str, Any]								=				outputs.replace("``"				, "\""							).replace("''"				, "\""							)
  
  					if not self.keep_accents:
  							snake_case		:						List[Any]								=				unicodedata.normalize("NFKD"				, SCREAMING_SNAKE_CASE							)
  							snake_case		:						int								=				"".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE							)]							)
  					if self.do_lower_case:
  							snake_case		:						Union[str, Any]								=				outputs.lower()
  
  					return outputs
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[str]								=				self.preprocess_text(SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[str]								=				self.sp_model.encode(SCREAMING_SNAKE_CASE				, out_type=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				[]
  					for piece in pieces:
  							if len(SCREAMING_SNAKE_CASE							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
  									snake_case		:						Union[str, Any]								=				self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE				, ""							)							)
  									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
  											if len(cur_pieces[0]							) == 1:
  													snake_case		:						List[Any]								=				cur_pieces[1:]
  											else:
  													snake_case		:						Dict								=				cur_pieces[0][1:]
  									cur_pieces.append(piece[-1]							)
  									new_pieces.extend(SCREAMING_SNAKE_CASE							)
  							else:
  									new_pieces.append(SCREAMING_SNAKE_CASE							)
  
  					return new_pieces
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				[]
  					snake_case		:						Any								=				""
  					snake_case		:						Dict								=				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
  									snake_case		:						Optional[int]								=				True
  									snake_case		:						Union[str, Any]								=				[]
  							else:
  									current_sub_tokens.append(SCREAMING_SNAKE_CASE							)
  									snake_case		:						Tuple								=				False
  					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE							)
  					return out_string.strip()
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = True				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				kwargs.pop("use_source_tokenizer"				, SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Tuple								=				self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE				, skip_special_tokens=SCREAMING_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
  					snake_case		:						Any								=				[]
  					snake_case		:						Union[str, Any]								=				[]
  					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(SCREAMING_SNAKE_CASE							)							)
  											snake_case		:						Optional[Any]								=				[]
  									sub_texts.append(SCREAMING_SNAKE_CASE							)
  							else:
  									current_sub_text.append(SCREAMING_SNAKE_CASE							)
  					if current_sub_text:
  							sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE							)							)
  
  					# Mimic the behavior of the Rust tokenizer:
  					# No space after <unk>
  					if spaces_between_special_tokens:
  							snake_case		:						Any								=				re.sub(r"(<unk>) "				, r"\1"				, " ".join(SCREAMING_SNAKE_CASE							)							)
  					else:
  							snake_case		:						Optional[int]								=				"".join(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Optional[int]								=				(
  					    clean_up_tokenization_spaces
  					    if clean_up_tokenization_spaces is not None
  					    else self.clean_up_tokenization_spaces
  					)
  					if clean_up_tokenization_spaces:
  							snake_case		:						List[Any]								=				self.clean_up_tokenization(SCREAMING_SNAKE_CASE							)
  							return clean_text
  					else:
  							return text
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				[self.sep_token_id]
  					snake_case		:						List[str]								=				[self.cls_token_id]
  					if token_ids_a is None:
  							return cls + token_ids_a + sep
  					return cls + token_ids_a + sep + token_ids_a + sep
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = False							):
  
  
  
  					"""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 not None:
  							return [1] + ([0] * len(SCREAMING_SNAKE_CASE							)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE							)) + [1]
  					return [1] + ([0] * len(SCREAMING_SNAKE_CASE							)) + [1]
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				[self.sep_token_id]
  					snake_case		:						Optional[int]								=				[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							) * [0] + len(token_ids_a + sep							) * [1]
  
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if not os.path.isdir(SCREAMING_SNAKE_CASE							):
  							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
  							return
  					snake_case		:						Tuple								=				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:
  									snake_case		:						List[str]								=				self.sp_model.serialized_model_proto()
  									fi.write(SCREAMING_SNAKE_CASE							)
  
  					return (out_vocab_file,)
  
  