import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class  A_			(							a_			):

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   str = "▁"  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[str, AddedToken] = "<unk>"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, AddedToken] = "</s>"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, AddedToken] = "<pad>"  ,				):
   __a							=	{
       "pad": {"id": 0, "token": pad_token},
       "eos": {"id": 1, "token": eos_token},
       "unk": {"id": 2, "token": unk_token},
   }

   __a							=	[None] * len(self.special_tokens							)
   for token_dict in self.special_tokens.values():
    __a							=	token_dict["token"]

   __a							=	Tokenizer(Unigram()							)

   __a							=	normalizers.Sequence(
       [
           normalizers.Nmt(),
           normalizers.NFKC(),
           normalizers.Replace(Regex(" {2,}"							)  ,				" "							),
           normalizers.Lowercase(),
       ]							)
   __a							=	pre_tokenizers.Sequence(
       [
           pre_tokenizers.Metaspace(replacement=__SCREAMING_SNAKE_CASE  ,				add_prefix_space=__SCREAMING_SNAKE_CASE							),
           pre_tokenizers.Digits(individual_digits=__SCREAMING_SNAKE_CASE							),
           pre_tokenizers.Punctuation(),
       ]							)
   __a							=	decoders.Metaspace(replacement=__SCREAMING_SNAKE_CASE  ,				add_prefix_space=__SCREAMING_SNAKE_CASE							)

   __a							=	TemplateProcessing(
       single=f"""$A {self.special_tokens["eos"]["token"]}"""  ,				special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]  ,				)

   __a							=	{
       "model": "SentencePieceUnigram",
       "replacement": replacement,
       "add_prefix_space": add_prefix_space,
   }

   super().__init__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Union[str, List[str]]  ,				__SCREAMING_SNAKE_CASE			:   int = 80_00  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				):
   __a							=	trainers.UnigramTrainer(
       vocab_size=__SCREAMING_SNAKE_CASE  ,				special_tokens=self.special_tokens_list  ,				show_progress=__SCREAMING_SNAKE_CASE  ,				)

   if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    __a							=	[files]
   self._tokenizer.train(__SCREAMING_SNAKE_CASE  ,				trainer=__SCREAMING_SNAKE_CASE							)

   self.add_unk_id()

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[Iterator[str], Iterator[Iterator[str]]]  ,				__SCREAMING_SNAKE_CASE			:   int = 80_00  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				):
   __a							=	trainers.UnigramTrainer(
       vocab_size=__SCREAMING_SNAKE_CASE  ,				special_tokens=self.special_tokens_list  ,				show_progress=__SCREAMING_SNAKE_CASE  ,				)

   self._tokenizer.train_from_iterator(__SCREAMING_SNAKE_CASE  ,				trainer=__SCREAMING_SNAKE_CASE							)

   self.add_unk_id()

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	json.loads(self._tokenizer.to_str()							)

   __a							=	self.special_tokens["unk"]["id"]

   __a							=	Tokenizer.from_str(json.dumps(__SCREAMING_SNAKE_CASE							)							)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	None
   if "feature_extractor" in kwargs:
    warnings.warn(
        "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
        " instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
    __a							=	kwargs.pop("feature_extractor"							)

   __a							=	image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("You need to specify an `image_processor`."							)
   if tokenizer is None:
    raise ValueError("You need to specify a `tokenizer`."							)

   super().__init__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):

   if text is None and images is None:
    raise ValueError("You have to specify either text or images. Both cannot be none."							)

   if text is not None:
    __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if text is not None and images is not None:
    __a							=	image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE							)  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.tokenizer.model_input_names
   __a							=	self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
   return self.image_processor_class


