'''simple docstring'''




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()
__UpperCAmelCase												=logging.get_logger("transformers.models.speecht5")

__UpperCAmelCase												={
    "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",
}
__UpperCAmelCase												={
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__UpperCAmelCase												={
    "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",
}
__UpperCAmelCase												={
    "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",
}
__UpperCAmelCase												={
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__UpperCAmelCase												={
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__UpperCAmelCase												={
    "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",
}
__UpperCAmelCase												={
    "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",
}
__UpperCAmelCase												={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__UpperCAmelCase												={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCAmelCase												={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCAmelCase												=[]
__UpperCAmelCase												=[
    "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",
]
__UpperCAmelCase												=IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
__UpperCAmelCase												=IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
__UpperCAmelCase												=IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Dict:


    for attribute in key.split('''.'''    ):
        __lowerCamelCase          =  getattr(__UpperCamelCase    ,  __UpperCamelCase    )

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

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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:


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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  []

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

    for name, value in fairseq_dict.items():
        if should_ignore(__UpperCamelCase    ,  __UpperCamelCase    ):
            logger.info(f"""{name} was ignored"""    )
            continue

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    __lowerCamelCase          =  True
                    if "*" in mapped_key:
                        __lowerCamelCase          =  name.split(__UpperCamelCase    )[0].split('''.'''    )[-2]
                        __lowerCamelCase          =  mapped_key.replace('''*'''    ,  __UpperCamelCase    )
                    if "weight_g" in name:
                        __lowerCamelCase          =  '''weight_g'''
                    elif "weight_v" in name:
                        __lowerCamelCase          =  '''weight_v'''
                    elif "bias" in name:
                        __lowerCamelCase          =  '''bias'''
                    elif "weight" in name:
                        __lowerCamelCase          =  '''weight'''
                    elif "running_mean" in name:
                        __lowerCamelCase          =  '''running_mean'''
                    elif "running_var" in name:
                        __lowerCamelCase          =  '''running_var'''
                    elif "num_batches_tracked" in name:
                        __lowerCamelCase          =  '''num_batches_tracked'''
                    else:
                        __lowerCamelCase          =  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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  full_name.split('''conv_layers.'''    )[-1]
    __lowerCamelCase          =  name.split('''.'''    )
    __lowerCamelCase          =  int(items[0]    )
    __lowerCamelCase          =  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."""    )
            __lowerCamelCase          =  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."""    )
            __lowerCamelCase          =  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."""    )
            __lowerCamelCase          =  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."""    )
            __lowerCamelCase          =  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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  )     ->   Dict:


    if config_path is not None:
        __lowerCamelCase          =  SpeechTaConfig.from_pretrained(__UpperCamelCase    )
    else:
        __lowerCamelCase          =  SpeechTaConfig()

    if task == "s2t":
        __lowerCamelCase          =  config.max_text_positions
        __lowerCamelCase          =  SpeechTaForSpeechToText(__UpperCamelCase    )
    elif task == "t2s":
        __lowerCamelCase          =  18_76
        __lowerCamelCase          =  6_00
        __lowerCamelCase          =  config.max_speech_positions
        __lowerCamelCase          =  SpeechTaForTextToSpeech(__UpperCamelCase    )
    elif task == "s2s":
        __lowerCamelCase          =  18_76
        __lowerCamelCase          =  config.max_speech_positions
        __lowerCamelCase          =  SpeechTaForSpeechToSpeech(__UpperCamelCase    )
    else:
        raise ValueError(f"""Unknown task name: {task}"""    )

    if vocab_path:
        __lowerCamelCase          =  SpeechTaTokenizer(__UpperCamelCase    ,  model_max_length=config.max_text_positions    )

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

    __lowerCamelCase          =  SpeechTaFeatureExtractor()
    __lowerCamelCase          =  SpeechTaProcessor(tokenizer=__UpperCamelCase    ,  feature_extractor=__UpperCamelCase    )
    processor.save_pretrained(__UpperCamelCase    )

    __lowerCamelCase          =  torch.load(__UpperCamelCase    )
    recursively_load_weights(fairseq_checkpoint['''model''']    ,  __UpperCamelCase    ,  __UpperCamelCase    )

    model.save_pretrained(__UpperCamelCase    )

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


if __name__ == "__main__":
  __UpperCAmelCase												=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."
  )

  __UpperCAmelCase												=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,
  )

'''simple docstring'''




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												="▁"

__UpperCAmelCase												={"vocab_file": "prophetnet.tokenizer"}

__UpperCAmelCase												={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__UpperCAmelCase												={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__UpperCAmelCase												={
    "microsoft/xprophetnet-large-wiki100-cased": 5_1_2,
}






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  collections.OrderedDict()
    with open(UpperCamelCase__    ,  '''r'''    ,  encoding='''utf-8'''    ) as reader:
        __lowerCamelCase          =  reader.readlines()
    for index, token in enumerate(UpperCamelCase__    ):
        __lowerCamelCase          =  token.rstrip('''\n'''    )
        __lowerCamelCase          =  index
    return vocab




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Optional[Any]							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Any							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Optional[int]							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					Union[str, Any]							=["input_ids", "attention_mask"]






     def __init__(			self		:		int  ,    a		:		List[str]  ,    a		:		Optional[int]="[SEP]"  ,    a		:		int="[SEP]"  ,    a		:		str="[SEP]"  ,    a		:		List[Any]="[UNK]"  ,    a		:		List[Any]="[PAD]"  ,    a		:		str="[CLS]"  ,    a		:		List[str]="[MASK]"  ,    a		:		Optional[Dict[str, Any]] = None  ,    **a		:		str  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  {} if sp_model_kwargs is None else sp_model_kwargs

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

         try:
             import sentencepiece as spm
         except ImportError:
             logger.warning(
                 '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                 ''' pip install sentencepiece'''   )
             raise

         __lowerCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(a   )   )
         __lowerCamelCase          =  vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # put special tokens and [unused] tokens into the vocab
         __lowerCamelCase          =  {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

         for i in range(10   ):
             __lowerCamelCase          =  f"""[unused{i}]"""
             __lowerCamelCase          =  5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         __lowerCamelCase          =  12
         __lowerCamelCase          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         for k in self.fairseq_tokens_to_ids.keys():
             self.unique_no_split_tokens.append(a   )






     def __getstate__(			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.__dict__.copy()
         __lowerCamelCase          =  None
         return state






     def __setstate__(			self		:		int  ,    a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  d
         try:
             import sentencepiece as spm
         except ImportError:
             logger.warning(
                 '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                 ''' pip install sentencepiece'''   )
             raise

         # for backward compatibility
         if not hasattr(self  ,    '''sp_model_kwargs'''   ):
             __lowerCamelCase          =  {}

         __lowerCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    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 ([0] * len(a   )) + [1]
         return ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]






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





         """simple docstring"""



         __lowerCamelCase          =  [self.sep_token_id]

         if token_ids_a is None:
             return len(token_ids_a + sep   ) * [0]
         return len(token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         return len(self.sp_model   ) + self.fairseq_offset






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab






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





         """simple docstring"""



         return self.sp_model.encode(a  ,    out_type=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		int   ):





         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         __lowerCamelCase          =  self.sp_model.PieceToId(a   )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  ''''''.join(a   ).replace(a  ,    ''' '''   ).strip()
         return out_string






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    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
         __lowerCamelCase          =  os.path.join(
             a  ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ) and os.path.isfile(self.vocab_file   ):
             copyfile(self.vocab_file  ,    a   )
         elif not os.path.isfile(self.vocab_file   ):
             with open(a  ,    '''wb'''   ) as fi:
                 __lowerCamelCase          =  self.sp_model.serialized_model_proto()
                 fi.write(a   )

         return (out_vocab_file,)






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





         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.sep_token_id]
         __lowerCamelCase          =  [self.sep_token_id]
         return token_ids_a + sep + token_ids_a + sep
