






'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__			=      logging.get_logger(__name__)

UpperCamelCase__			=      {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    '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',
}
UpperCamelCase__			=      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   for attribute in key.split("."				):
      lowercase_						:     Optional[Any]							     =  getattr(_UpperCamelCase    ,		_UpperCamelCase				)

   if weight_type is not None:
      lowercase_						:     Optional[Any]							     =  getattr(_UpperCamelCase    ,		_UpperCamelCase				).shape
   else:
      lowercase_						:     Any							     =  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_						:     List[str]							     =  value
   elif weight_type == "weight_g":
      lowercase_						:     Optional[Any]							     =  value
   elif weight_type == "weight_v":
      lowercase_						:     Union[str, Any]							     =  value
   elif weight_type == "bias":
      lowercase_						:     int							     =  value
   elif weight_type == "running_mean":
      lowercase_						:     Union[str, Any]							     =  value
   elif weight_type == "running_var":
      lowercase_						:     List[str]							     =  value
   elif weight_type == "num_batches_tracked":
      lowercase_						:     int							     =  value
   elif weight_type == "inv_freq":
      lowercase_						:     Dict							     =  value
   else:
      lowercase_						:     str							     =  value

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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  []
   lowercase_						:     List[Any]							     =  fairseq_model.state_dict()

   lowercase_						:     Tuple							     =  hf_model.wavaveca_conformer.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase_						:     Optional[Any]							     =  False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		hf_model.config.feat_extract_norm == "group"    ,		)
         lowercase_						:     Tuple							     =  True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase_						:     str							     =  "wav2vec2_conformer." + 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]:
               lowercase_						:     int							     =  True
               if "*" in mapped_key:
                  lowercase_						:     Optional[Any]							     =  name.split(_UpperCamelCase				)[0].split("."				)[-2]
                  lowercase_						:     Optional[Any]							     =  mapped_key.replace("*"    ,		_UpperCamelCase				)
               if "pos_bias_u" in name:
                  lowercase_						:     Union[str, Any]							     =  None
               elif "pos_bias_v" in name:
                  lowercase_						:     str							     =  None
               elif "weight_g" in name:
                  lowercase_						:     List[str]							     =  "weight_g"
               elif "weight_v" in name:
                  lowercase_						:     Any							     =  "weight_v"
               elif "bias" in name:
                  lowercase_						:     List[Any]							     =  "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  lowercase_						:     str							     =  "weight"
               elif "running_mean" in name:
                  lowercase_						:     List[str]							     =  "running_mean"
               elif "inv_freq" in name:
                  lowercase_						:     Optional[Any]							     =  "inv_freq"
               elif "running_var" in name:
                  lowercase_						:     Optional[int]							     =  "running_var"
               elif "num_batches_tracked" in name:
                  lowercase_						:     str							     =  "num_batches_tracked"
               else:
                  lowercase_						:     str							     =  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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  full_name.split("conv_layers."				)[-1]
   lowercase_						:     str							     =  name.split("."				)
   lowercase_						:     str							     =  int(items[0]				)
   lowercase_						:     Optional[Any]							     =  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_						:     Tuple							     =  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_						:     List[Any]							     =  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_						:     str							     =  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_						:     int							     =  value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
   else:
      unused_weights.append(_UpperCamelCase				)




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=True				):


   """simple docstring"""




   if config_path is not None:
      lowercase_						:     List[Any]							     =  WavaVecaConformerConfig.from_pretrained(_UpperCamelCase    ,		hidden_act="swish"				)
   else:
      lowercase_						:     Union[str, Any]							     =  WavaVecaConformerConfig()

   if "rope" in checkpoint_path:
      lowercase_						:     Dict							     =  "rotary"

   if is_finetuned:
      if dict_path:
         lowercase_						:     Dict							     =  Dictionary.load(_UpperCamelCase				)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         lowercase_						:     Optional[Any]							     =  target_dict.pad_index
         lowercase_						:     Union[str, Any]							     =  target_dict.bos_index
         lowercase_						:     List[Any]							     =  target_dict.eos_index
         lowercase_						:     List[Any]							     =  len(target_dict.symbols				)
         lowercase_						:     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				)
         lowercase_						:     int							     =  target_dict.indices

         # fairseq has the <pad> and <s> switched
         lowercase_						:     Tuple							     =  0
         lowercase_						:     int							     =  1
         with open(_UpperCamelCase    ,		"w"    ,		encoding="utf-8"				) as vocab_handle:
            json.dump(_UpperCamelCase    ,		_UpperCamelCase				)
         lowercase_						:     int							     =  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    ,		)
         lowercase_						:     Union[str, Any]							     =  True if config.feat_extract_norm == "layer" else False
         lowercase_						:     Tuple							     =  WavaVecaFeatureExtractor(
             feature_size=1    ,		sampling_rate=1_6000    ,		padding_value=0    ,		do_normalize=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		)
         lowercase_						:     List[str]							     =  WavaVecaProcessor(feature_extractor=_UpperCamelCase    ,		tokenizer=_UpperCamelCase				)
         processor.save_pretrained(_UpperCamelCase				)

      lowercase_						:     Optional[int]							     =  WavaVecaConformerForCTC(_UpperCamelCase				)
   else:
      lowercase_						:     Optional[Any]							     =  WavaVecaConformerForPreTraining(_UpperCamelCase				)

   if is_finetuned:
      lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]    ,		arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
   else:
      lowercase_						:     Tuple							     =  argparse.Namespace(task="audio_pretraining"				)
      lowercase_						:     Dict							     =  fairseq.tasks.setup_task(_UpperCamelCase				)

      lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,		task=_UpperCamelCase				)

   lowercase_						:     List[str]							     =  model[0].eval()

   recursively_load_weights(_UpperCamelCase    ,		_UpperCamelCase    ,		not is_finetuned				)

   hf_wavavec.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
 UpperCamelCase__			=      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'
 )
 UpperCamelCase__			=      parser.parse_args()
 convert_wavaveca_conformer_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )












'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__			=      logging.get_logger(__name__)


UpperCamelCase__			=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__			=      {
    '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',
    },
}

UpperCamelCase__			=      {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}




@lru_cache()
def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     List[Any]							     =  (
       list(range(ord("!"				)    ,		ord("~"				) + 1				)				) + list(range(ord("¡"				)    ,		ord("¬"				) + 1				)				) + list(range(ord("®"				)    ,		ord("ÿ"				) + 1				)				)
   )
   lowercase_						:     str							     =  bs[:]
   lowercase_						:     Dict							     =  0
   for b in range(2**8				):
      if b not in bs:
         bs.append(_UpperCamelCase				)
         cs.append(2**8 + n				)
         n += 1
   lowercase_						:     Optional[int]							     =  [chr(_UpperCamelCase				) for n in cs]
   return dict(zip(_UpperCamelCase    ,		_UpperCamelCase				)				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  set()
   lowercase_						:     Union[str, Any]							     =  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      lowercase_						:     Dict							     =  char
   return pairs





class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Dict									=     VOCAB_FILES_NAMES
    __lowerCamelCase: int									=     PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase: Dict									=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase: str									=     ['input_ids', 'attention_mask']




    def __init__(      self	:							Tuple	,  a	:							Tuple	,  a	:							Tuple	,  a	:							int="replace"	,  a	:							Optional[int]="<s>"	,  a	:							Tuple="</s>"	,  a	:							Tuple="</s>"	,  a	:							Tuple="<s>"	,  a	:							Optional[Any]="<unk>"	,  a	:							Dict="<pad>"	,  a	:							List[str]="<mask>"	,  a	:							Tuple=False	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else bos_token
       lowercase_						:     str							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else eos_token
       lowercase_						:     str							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else sep_token
       lowercase_						:     Any							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else cls_token
       lowercase_						:     Dict							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else unk_token
       lowercase_						:     List[Any]							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       lowercase_						:     Union[str, Any]							     =  AddedToken(a	,  lstrip=a	,  rstrip=a      ) if isinstance(a	,  a      ) else mask_token

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

       with open(a	,  encoding="utf-8"      ) as vocab_handle:
          lowercase_						:     Any							     =  json.load(a      )
       lowercase_						:     Union[str, Any]							     =  {v: k for k, v in self.encoder.items()}
       lowercase_						:     Dict							     =  errors  # how to handle errors in decoding
       lowercase_						:     Any							     =  bytes_to_unicode()
       lowercase_						:     List[Any]							     =  {v: k for k, v in self.byte_encoder.items()}
       with open(a	,  encoding="utf-8"      ) as merges_handle:
          lowercase_						:     Optional[Any]							     =  merges_handle.read().split("\n"      )[1:-1]
       lowercase_						:     Any							     =  [tuple(merge.split()      ) for merge in bpe_merges]
       lowercase_						:     List[str]							     =  dict(zip(a	,  range(len(a      )      )      )      )
       lowercase_						:     Optional[Any]							     =  {}
       lowercase_						:     Union[str, Any]							     =  add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       lowercase_						:     str							     =  re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"      )




    @property
    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       return len(self.encoder      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return dict(self.encoder	,  **self.added_tokens_encoder      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       if token in self.cache:
          return self.cache[token]
       lowercase_						:     Optional[Any]							     =  tuple(a      )
       lowercase_						:     Tuple							     =  get_pairs(a      )

       if not pairs:
          return token

       while True:
          lowercase_						:     Any							     =  min(a	,  key=lambda a      : self.bpe_ranks.get(a	,  float("inf"      )      )      )
          if bigram not in self.bpe_ranks:
             break
          lowercase_     ,		lowercase_						:     Dict							     =  bigram
          lowercase_						:     List[Any]							     =  []
          lowercase_						:     Optional[Any]							     =  0
          while i < len(a      ):
             try:
                lowercase_						:     Union[str, Any]							     =  word.index(a	,  a      )
             except ValueError:
                new_word.extend(word[i:]      )
                break
             else:
                new_word.extend(word[i:j]      )
                lowercase_						:     Any							     =  j

             if word[i] == first and i < len(a      ) - 1 and word[i + 1] == second:
                new_word.append(first + second      )
                i += 2
             else:
                new_word.append(word[i]      )
                i += 1
          lowercase_						:     Any							     =  tuple(a      )
          lowercase_						:     List[str]							     =  new_word
          if len(a      ) == 1:
             break
          else:
             lowercase_						:     Union[str, Any]							     =  get_pairs(a      )
       lowercase_						:     List[str]							     =  " ".join(a      )
       lowercase_						:     Optional[int]							     =  word
       return word




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							str      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  []
       for token in re.findall(self.pat	,  a      ):
          lowercase_						:     Tuple							     =  "".join(
              self.byte_encoder[b] for b in token.encode("utf-8"      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a      ).split(" "      )      )
       return bpe_tokens




    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							Dict      ):






       '''simple docstring'''






       return self.encoder.get(a	,  self.encoder.get(self.unk_token      )      )




    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							str      ):






       '''simple docstring'''






       return self.decoder.get(a      )




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  "".join(a      )
       lowercase_						:     str							     =  bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"	,  errors=self.errors      )
       return text




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							str	,  a	:							Optional[str] = None      ):






       '''simple docstring'''






       if not os.path.isdir(a      ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
       lowercase_						:     Any							     =  os.path.join(
           a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
       lowercase_						:     Optional[int]							     =  os.path.join(
           a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

       with open(a	,  "w"	,  encoding="utf-8"      ) as f:
          f.write(json.dumps(self.encoder	,  indent=2	,  sort_keys=a	,  ensure_ascii=a      ) + "\n"      )

       lowercase_						:     Dict							     =  0
       with open(a	,  "w"	,  encoding="utf-8"      ) as writer:
          writer.write("#version: 0.2\n"      )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,  key=lambda a      : kv[1]      ):
             if index != token_index:
                logger.warning(
                    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                    " Please check that the tokenizer is not corrupted!"      )
                lowercase_						:     Optional[Any]							     =  token_index
             writer.write(" ".join(a      ) + "\n"      )
             index += 1

       return vocab_file, merge_file




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							List[int]	,  a	:							Optional[List[int]] = None      ):






       '''simple docstring'''






       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase_						:     List[str]							     =  [self.cls_token_id]
       lowercase_						:     List[Any]							     =  [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[int]	,  a	:							Optional[List[int]] = None	,  a	:							bool = False      ):






       '''simple docstring'''






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

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




    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							List[int]	,  a	:							Optional[List[int]] = None      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  [self.sep_token_id]
       lowercase_						:     List[str]							     =  [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]





    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Any	,  a	:							int=False	,  **a	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  kwargs.pop("add_prefix_space"	,  self.add_prefix_space      )
       if (is_split_into_words or add_prefix_space) and (len(a      ) > 0 and not text[0].isspace()):
          lowercase_						:     str							     =  " " + text
       return (text, kwargs)




