

import os
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


UpperCAmelCase__       :   str 			=			logging.get_logger(__name__)

UpperCAmelCase__       :   str 			=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__       :   int 			=			{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

UpperCAmelCase__       :   str 			=			{
    "camembert-base": 512,
}

UpperCAmelCase__       :   List[Any] 			=			"▁"







class 				a__				(      A__				):







  """simple docstring"""




  UpperCAmelCase__					:       Dict					=VOCAB_FILES_NAMES
  UpperCAmelCase__					:       Optional[int]					=PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__					:       Optional[Any]					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__					:       Any					=["""input_ids""", """attention_mask"""]


  def __init__(	self			:       Optional[Any] , UpperCAmelCase__			:       str , UpperCAmelCase__			:       Optional[int]="<s>" , UpperCAmelCase__			:       Any="</s>" , UpperCAmelCase__			:       Tuple="</s>" , UpperCAmelCase__			:       Tuple="<s>" , UpperCAmelCase__			:       str="<unk>" , UpperCAmelCase__			:       Optional[Any]="<pad>" , UpperCAmelCase__			:       Optional[Any]="<mask>" , UpperCAmelCase__			:       Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , UpperCAmelCase__			:       Optional[Dict[str, Any]] = None , **UpperCAmelCase__			:       List[str] , )    ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					str        =      AddedToken(_a , lstrip=_a , rstrip=_a						) if isinstance(_a , _a						) else mask_token

        SCREAMING_SNAKE_CASE       :					Optional[int]        =      {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
        SCREAMING_SNAKE_CASE       :					List[str]        =      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(str(_a						)						)
        SCREAMING_SNAKE_CASE       :					List[Any]        =      vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        SCREAMING_SNAKE_CASE       :					Any        =      {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      len(self.fairseq_tokens_to_ids						)
        SCREAMING_SNAKE_CASE       :					Tuple        =      len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
        SCREAMING_SNAKE_CASE       :					List[str]        =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}


  def        _lowercase					(	self			:       Tuple , UpperCAmelCase__			:       List[int] , UpperCAmelCase__			:       Optional[List[int]] = None						)    ->List[int]:
        """simple docstring"""







        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      [self.cls_token_id]
        SCREAMING_SNAKE_CASE       :					int        =      [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


  def        _lowercase					(	self			:       Optional[Any] , UpperCAmelCase__			:       List[int] , UpperCAmelCase__			:       Optional[List[int]] = None , UpperCAmelCase__			:       bool = False						)    ->List[int]:
        """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        _lowercase					(	self			:       Dict , UpperCAmelCase__			:       List[int] , UpperCAmelCase__			:       Optional[List[int]] = None						)    ->List[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Tuple        =      [self.sep_token_id]
        SCREAMING_SNAKE_CASE       :					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]


  @property
  def        _lowercase					(	self			:       Tuple						)    ->Union[str, Any]:
        """simple docstring"""







        return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


  def        _lowercase					(	self			:       Tuple						)    ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      {self.convert_ids_to_tokens(_a						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab


  def        _lowercase					(	self			:       Optional[int] , UpperCAmelCase__			:       str						)    ->List[str]:
        """simple docstring"""







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


  def        _lowercase					(	self			:       Optional[int] , UpperCAmelCase__			:       Optional[Any]						)    ->Optional[Any]:
        """simple docstring"""







        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(_a						) == 0:
              # Convert sentence piece unk token to fairseq unk token index
              return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(_a						)


  def        _lowercase					(	self			:       Tuple , UpperCAmelCase__			:       Any						)    ->str:
        """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        _lowercase					(	self			:       List[Any] , UpperCAmelCase__			:       List[Any]						)    ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Any        =      []
        SCREAMING_SNAKE_CASE       :					int        =      """"""
        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      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(_a						) + token
                    SCREAMING_SNAKE_CASE       :					int        =      True
                    SCREAMING_SNAKE_CASE       :					List[Any]        =      []
              else:
                    current_sub_tokens.append(_a						)
                    SCREAMING_SNAKE_CASE       :					Tuple        =      False
        out_string += self.sp_model.decode(_a						)
        return out_string.strip()


  def __getstate__(	self			:       str						)    ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      self.__dict__.copy()
        SCREAMING_SNAKE_CASE       :					Optional[Any]        =      None
        return state


  def __setstate__(	self			:       Union[str, Any] , UpperCAmelCase__			:       Optional[Any]						)    ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[Any]        =      d

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

        SCREAMING_SNAKE_CASE       :					int        =      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(self.vocab_file						)




  def        _lowercase					(	self			:       Any , UpperCAmelCase__			:       str , UpperCAmelCase__			:       Optional[str] = None						)    ->Tuple[str]:
        """simple docstring"""







        if not os.path.isdir(_a						):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
              return
        SCREAMING_SNAKE_CASE       :					int        =      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:
                    SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      self.sp_model.serialized_model_proto()
                    fi.write(_a						)

        return (out_vocab_file,)







'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
