



"""simple docstring"""


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						'''▁'''

UpperCAmelCase__  		=						{'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

UpperCAmelCase__  		=						{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

UpperCAmelCase__  		=						{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

UpperCAmelCase__  		=						{
    '''ernie-m-base''': 5_1_4,
    '''ernie-m-large''': 5_1_4,
}

UpperCAmelCase__  		=						{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}


class        a				(		snake_case__   ):
    _snake_case     :  List[str]     							=							["input_ids"]

    _snake_case     :  Optional[int]     							=							VOCAB_FILES_NAMES
    _snake_case     :  Union[str, Any]     							=							PRETRAINED_INIT_CONFIGURATION
    _snake_case     :  List[str]     							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case     :  Union[str, Any]     							=							PRETRAINED_VOCAB_FILES_MAP
    _snake_case     :  Any     							=							RESOURCE_FILES_NAMES


    def __init__(					self	:  List[str]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]=None   ,				__lowerCAmelCase	:  Optional[Any]=False   ,				__lowerCAmelCase	:  int="utf8"   ,				__lowerCAmelCase	:  List[Any]="[UNK]"   ,				__lowerCAmelCase	:  List[Any]="[SEP]"   ,				__lowerCAmelCase	:  Any="[PAD]"   ,				__lowerCAmelCase	:  Optional[Any]="[CLS]"   ,				__lowerCAmelCase	:  Tuple="[MASK]"   ,				__lowerCAmelCase	:  Any = None   ,				**__lowerCAmelCase	:  Union[str, Any]   ,				):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.

      _UpperCAmelCase						 =      {} if sp_model_kwargs is None else sp_model_kwargs
      super().__init__(
          do_lower_case=_A   ,				unk_token=_A   ,				sep_token=_A   ,				pad_token=_A   ,				cls_token=_A   ,				mask_token=_A   ,				vocab_file=_A   ,				encoding=_A   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**_A   ,				)
      _UpperCAmelCase						 =      do_lower_case
      _UpperCAmelCase						 =      sentencepiece_model_ckpt
      _UpperCAmelCase						 =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
      self.sp_model.Load(_A					)

      # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
      if vocab_file is not None:
        _UpperCAmelCase						 =      self.load_vocab(filepath=_A					)
      else:
        _UpperCAmelCase						 =      {self.sp_model.id_to_piece(_A					): id for id in range(self.sp_model.get_piece_size()					)}
      _UpperCAmelCase						 =      {v: k for k, v in self.vocab.items()}


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict					):
      if text is None:
        return None

      _UpperCAmelCase						 =      self.tokenize(_A					)
      _UpperCAmelCase						 =      '', []

      for i, ch in enumerate(_A					):
        if ch in self.SP_CHAR_MAPPING:
          _UpperCAmelCase						 =      self.SP_CHAR_MAPPING.get(_A					)
        else:
          _UpperCAmelCase						 =      unicodedata.normalize("""NFKC"""   ,				_A					)
        if self.is_whitespace(_A					):
          continue
        normalized_text += ch
        char_mapping.extend([i] * len(_A					)					)

      _UpperCAmelCase						 =      normalized_text, [], 0

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

      for token in split_tokens:
        if token[:1] == "▁":
          _UpperCAmelCase						 =      token[1:]
        _UpperCAmelCase						 =      text[offset:].index(_A					) + offset
        _UpperCAmelCase						 =      start + len(_A					)

        token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)					)
        _UpperCAmelCase						 =      end
      return token_mapping


    @property
    def      lowerCAmelCase_  (					self	:  str					):
      return len(self.vocab					)


    def      lowerCAmelCase_  (					self	:  str					):
      return dict(self.vocab   ,				**self.added_tokens_encoder					)


    def __getstate__(					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.__dict__.copy()
      _UpperCAmelCase						 =      None
      return state


    def __setstate__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      d

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

      _UpperCAmelCase						 =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
      self.sp_model.Load(self.sentencepiece_model_ckpt					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  List[str]					):
      return "".join((self.SP_CHAR_MAPPING.get(_A   ,				_A					) for c in text)					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]=False   ,				__lowerCAmelCase	:  Any=64   ,				__lowerCAmelCase	:  List[Any]=0.1					):

      if self.sp_model_kwargs.get("""enable_sampling"""					) is True:
        _UpperCAmelCase						 =      True
      if self.sp_model_kwargs.get("""alpha"""					) is not None:
        _UpperCAmelCase						 =      self.sp_model_kwargs.get("""alpha"""					)
      if self.sp_model_kwargs.get("""nbest_size"""					) is not None:
        _UpperCAmelCase						 =      self.sp_model_kwargs.get("""nbest_size"""					)

      if not enable_sampling:
        _UpperCAmelCase						 =      self.sp_model.EncodeAsPieces(_A					)
      else:
        _UpperCAmelCase						 =      self.sp_model.SampleEncodeAsPieces(_A   ,				_A   ,				_A					)
      _UpperCAmelCase						 =      []
      for pi, piece in enumerate(_A					):
        if piece == SPIECE_UNDERLINE:
          if not pieces[pi + 1].startswith(_A					) and pi != 0:
            new_pieces.append(_A					)
            continue
          else:
            continue
        _UpperCAmelCase						 =      0
        for i, chunk in enumerate(_A					):
          if chunk == SPIECE_UNDERLINE:
            continue
          if self.is_ch_char(_A					) or self.is_punct(_A					):
            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
              new_pieces.append(piece[lst_i:i]					)
            new_pieces.append(_A					)
            _UpperCAmelCase						 =      i + 1
          elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
              new_pieces.append(piece[lst_i:i]					)
            _UpperCAmelCase						 =      i
          elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
              new_pieces.append(piece[lst_i:i]					)
            _UpperCAmelCase						 =      i
        if len(_A					) > lst_i:
          new_pieces.append(piece[lst_i:]					)
      return new_pieces


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      ''.join(_A					).replace(_A   ,				""" """					).strip()
      return out_string


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  List[Any]					):
      _UpperCAmelCase						 =      self.convert_ids_to_tokens(_A					)
      _UpperCAmelCase						 =      ''.join(_A					).replace(_A   ,				""" """					).strip()
      return out_string


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
      return self.vocab.get(_A   ,				self.vocab.get(self.unk_token					)					)


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      return self.reverse_vocab.get(_A   ,				self.unk_token					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str=None					):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _UpperCAmelCase						 =      [self.cls_token_id]
      _UpperCAmelCase						 =      [self.sep_token_id]
      return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[int]=None					):
      if offset_mapping_a is None:
        return [(0, 0)] + offset_mapping_a + [(0, 0)]

      return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  Optional[int]=False					):

      if already_has_special_tokens:
        if token_ids_a is not None:
          raise ValueError(
              """You should not supply a second sequence if the provided sequence of """
              """ids is already formatted with special tokens for the model."""					)
        return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is not None:
        return [1] + ([0] * len(_A					)) + [1, 1] + ([0] * len(_A					)) + [1]
      return [1] + ([0] * len(_A					)) + [1]


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int] = None					):
      # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
      if token_ids_a is None:
        # [CLS] X [SEP]
        return (len(_A					) + 2) * [0]

      # [CLS] A [SEP] [SEP] B [SEP]
      return [0] * (len(_A					) + 1) + [1] * (len(_A					) + 3)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple					):
      if "\u4e00" <= char <= "\u9fff":
        return True
      return False


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
      if ("a" <= char <= "z") or ("A" <= char <= "Z"):
        return True
      return False


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[str]					):
      if char in ",;:.?!~，；：。？！《》【】":
        return True
      return False


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  List[Any]					):
      if char == " " or char == "\t" or char == "\n" or char == "\r":
        return True
      if len(_A					) == 1:
        _UpperCAmelCase						 =      unicodedata.category(_A					)
        if cat == "Zs":
          return True
      return False


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      {}
      with io.open(_A   ,				"""r"""   ,				encoding="""utf-8"""					) as f:
        for index, line in enumerate(_A					):
          _UpperCAmelCase						 =      line.rstrip("""\n"""					)
          _UpperCAmelCase						 =      int(_A					)

      return token_to_idx


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Union[str, Any] = None					):
      _UpperCAmelCase						 =      0
      if os.path.isdir(_A					):
        _UpperCAmelCase						 =      os.path.join(
            _A   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
      else:
        _UpperCAmelCase						 =      (filename_prefix + '-' if filename_prefix else '') + save_directory
      with open(_A   ,				"""w"""   ,				encoding="""utf-8"""					) as writer:
        for token, token_index in sorted(self.vocab.items()   ,				key=lambda __lowerCAmelCase					: kv[1]					):
          if index != token_index:
            logger.warning(
                f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                """ Please check that the vocabulary is not corrupted!"""					)
            _UpperCAmelCase						 =      token_index
          writer.write(token + """\n"""					)
          index += 1

      _UpperCAmelCase						 =      os.path.join(_A   ,				"""sentencepiece.bpe.model"""					)
      with open(_A   ,				"""wb"""					) as fi:
        _UpperCAmelCase						 =      self.sp_model.serialized_model_proto()
        fi.write(_A					)

      return (vocab_file,)

"""simple docstring"""


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    UpperCAmelCase__  		=						argparse.ArgumentParser()

    parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

    parser.add_argument(
        """--txt2img_unclip""",
        default="""kakaobrain/karlo-v1-alpha""",
        type=str,
        required=False,
        help="""The pretrained txt2img unclip.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)
