


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()
SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger('''transformers.models.speecht5''')

SCREAMING_SNAKE_CASE						: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''',
}
SCREAMING_SNAKE_CASE						:int			=  {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
SCREAMING_SNAKE_CASE						:List[Any]			=  {
    '''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''',
}
SCREAMING_SNAKE_CASE						:Any			=  {
    '''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''',
}
SCREAMING_SNAKE_CASE						:Any			=  {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
SCREAMING_SNAKE_CASE						:Any			=  {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
SCREAMING_SNAKE_CASE						:int			=  {
    '''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''',
}
SCREAMING_SNAKE_CASE						:Optional[int]			=  {
    '''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''',
}
SCREAMING_SNAKE_CASE						:Union[str, Any]			=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE						:List[str]			=  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE						:Dict			=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE						:Dict			=  []
SCREAMING_SNAKE_CASE						:Optional[int]			=  [
    '''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''',
]
SCREAMING_SNAKE_CASE						:Tuple			=  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE						:Tuple			=  IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE						:str			=  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Optional[Any]				)->Dict:
   '''simple docstring'''






   for attribute in key.split("."				):
      snake_case_            =       getattr(lowerCAmelCase_		,    lowerCAmelCase_				)

   if weight_type is not None:
      snake_case_            =       getattr(lowerCAmelCase_		,    lowerCAmelCase_				).shape
   else:
      snake_case_            =       hf_pointer.shape

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

   if weight_type == "weight":
      snake_case_            =       value
   elif weight_type == "weight_g":
      snake_case_            =       value
   elif weight_type == "weight_v":
      snake_case_            =       value
   elif weight_type == "bias":
      snake_case_            =       value
   elif weight_type == "running_mean":
      snake_case_            =       value
   elif weight_type == "running_var":
      snake_case_            =       value
   elif weight_type == "num_batches_tracked":
      snake_case_            =       value
   else:
      snake_case_            =       value

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:Union[str, Any]				)->int:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Optional[Any]				)->int:
   '''simple docstring'''






   snake_case_            =       []

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

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

      snake_case_            =       False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    hf_model.config.feat_extract_norm == "group"		,    )
         snake_case_            =       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:
               snake_case_     ,     snake_case_            =       key.split(".*."				)
               if prefix in name and suffix in name:
                  snake_case_            =       suffix

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

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Tuple				)->int:
   '''simple docstring'''






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

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



@torch.no_grad()
def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:List[str]=None		,    lowerCAmelCase_				:Union[str, Any]=None		,    lowerCAmelCase_				:Tuple=None		,    )->Optional[int]:
   '''simple docstring'''






   if config_path is not None:
      snake_case_            =       SpeechTaConfig.from_pretrained(lowerCAmelCase_				)
   else:
      snake_case_            =       SpeechTaConfig()

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

   if vocab_path:
      snake_case_            =       SpeechTaTokenizer(lowerCAmelCase_		,    model_max_length=config.max_text_positions				)

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

   snake_case_            =       SpeechTaFeatureExtractor()
   snake_case_            =       SpeechTaProcessor(tokenizer=lowerCAmelCase_		,    feature_extractor=lowerCAmelCase_				)
   processor.save_pretrained(lowerCAmelCase_				)

   snake_case_            =       torch.load(lowerCAmelCase_				)
   recursively_load_weights(fairseq_checkpoint["model"]		,    lowerCAmelCase_		,    lowerCAmelCase_				)

   model.save_pretrained(lowerCAmelCase_				)

   if repo_id:
      print("Pushing to the hub..."				)
      processor.push_to_hub(lowerCAmelCase_				)
      model.push_to_hub(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:List[str]			=  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.'''
   )

   SCREAMING_SNAKE_CASE						:Optional[int]			=  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
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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


SCREAMING_SNAKE_CASE						:Dict			=  {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE						:Optional[int]			=  {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:List[str]			=  '''</w>'''
SCREAMING_SNAKE_CASE						:int			=  '''@@ '''



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->int:
   '''simple docstring'''






   snake_case_            =       set()
   snake_case_            =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      snake_case_            =       char
   return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE						:Tuple			=  {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']



  def __init__(  self							:							List[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Union[str, Any]="<s>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							Union[str, Any]="</s>"     ,					_lowerCAmelCase							:							str="<unk>"     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							List[str]     ,					) ->			List[str]:




     """simple docstring"""
     super().__init__(
         unk_token=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					do_lower_case=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       do_lower_case

     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as vocab_handle:
        snake_case_            =       json.load(_lowerCAmelCase	)
     snake_case_            =       {v: k for k, v in self.encoder.items()}

     if merges_file is None:
        logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

        snake_case_            =       None
        snake_case_            =       None
     else:
        with open(_lowerCAmelCase     ,					encoding="utf-8"	) as merges_handle:
           snake_case_            =       merges_handle.read().split("\n"	)[:-1]

        snake_case_            =       [tuple(merge.split()[:2]	) for merge in merges]
        snake_case_            =       dict(zip(_lowerCAmelCase     ,					range(len(_lowerCAmelCase	)	)	)	)
        snake_case_            =       {}



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




     """simple docstring"""
     return len(self.decoder	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Dict:




     """simple docstring"""
     return dict(self.encoder     ,					**self.added_tokens_encoder	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
     if token in self.cache:
        return self.cache[token]
     snake_case_            =       get_pairs(_lowerCAmelCase	)

     if not pairs:
        return token

     while True:
        snake_case_            =       min(_lowerCAmelCase     ,					key=lambda _lowerCAmelCase	: self.bpe_ranks.get(_lowerCAmelCase     ,					float("inf"	)	)	)
        if bigram not in self.bpe_ranks:
           break
        snake_case_     ,     snake_case_            =       bigram
        snake_case_            =       []
        snake_case_            =       0
        while i < len(_lowerCAmelCase	):
           try:
              snake_case_            =       word.index(_lowerCAmelCase     ,					_lowerCAmelCase	)
           except ValueError:
              new_word.extend(word[i:]	)
              break
           else:
              new_word.extend(word[i:j]	)
              snake_case_            =       j

           if word[i] == first and i < len(_lowerCAmelCase	) - 1 and word[i + 1] == second:
              new_word.append(first + second	)
              i += 2
           else:
              new_word.append(word[i]	)
              i += 1
        snake_case_            =       tuple(_lowerCAmelCase	)
        snake_case_            =       new_word
        if len(_lowerCAmelCase	) == 1:
           break
        else:
           snake_case_            =       get_pairs(_lowerCAmelCase	)
     snake_case_            =       " ".join(_lowerCAmelCase	)
     if word == "\n  " + BPE_TOKEN_MERGES:
        snake_case_            =       "\n" + BPE_TOKEN_MERGES

     if word.endswith(_lowerCAmelCase	):
        snake_case_            =       word.replace(_lowerCAmelCase     ,					""	)

     snake_case_            =       word.replace(" "     ,					_lowerCAmelCase	)
     snake_case_            =       word
     return word



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[str]	) ->			List[Any]:




     """simple docstring"""

     if self.bpe_ranks is None:
        raise ValueError(
            "This tokenizer was instantiated without a `merges.txt` file, so"
            " that it can only be used for decoding, not for encoding."
            "Make sure to provide `merges.txt` file at instantiation to enable "
            "encoding."	)

     if self.do_lower_case:
        snake_case_            =       text.lower()

     snake_case_            =       text.split()

     snake_case_            =       []
     for token in text:
        if token:
           split_tokens.extend(list(self.bpe(_lowerCAmelCase	).split(" "	)	)	)

     return split_tokens



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							str	) ->			int:




     """simple docstring"""
     return self.encoder.get(_lowerCAmelCase     ,					self.encoder.get(self.unk_token	)	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     snake_case_            =       self.decoder.get(_lowerCAmelCase     ,					self.unk_token	)
     return result



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			str:




     """simple docstring"""
     snake_case_            =       " ".join(_lowerCAmelCase	)

     # make sure @@ tokens are concatenated
     snake_case_            =       "".join(string.split(_lowerCAmelCase	)	)

     return string



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     if not os.path.isdir(_lowerCAmelCase	):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
        return
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

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

     snake_case_            =       0
     if self.bpe_ranks is None:
        return (vocab_file,)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,					key=lambda _lowerCAmelCase	: kv[1]	):
           if index != token_index:
              logger.warning(
                  F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                  " Please check that the tokenizer is not corrupted!"	)
              snake_case_            =       token_index
           writer.write(" ".join(_lowerCAmelCase	) + "\n"	)
           index += 1

     return (vocab_file, merges_file)




