

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


__UpperCAmelCase    				=     logging.get_logger(__name__)

__UpperCAmelCase    				=     {'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase    				=     {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Any      =						VOCAB_FILES_NAMES
 _lowercase     :					List[Any]      =						PRETRAINED_VOCAB_FILES_MAP
 _lowercase     :					int      =						['''input_ids''', '''attention_mask''']
 _lowercase     :					Dict      =						None

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






      '''simple docstring'''




      super().__init__(
          A_     ,						A_     ,						tokenizer_file=A_     ,						unk_token=A_     ,						bos_token=A_     ,						eos_token=A_     ,						pad_token=A_     ,						add_prefix_space=A_     ,						clean_up_tokenization_spaces=A_     ,						**A_     ,						)
      _lowerCAmelCase   :					Dict        =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get("add_prefix_space"     ,						A_       ) != add_prefix_space:
           _lowerCAmelCase   :					List[str]        =    getattr(A_     ,						pre_tok_state.pop("type"       )       )
           _lowerCAmelCase   :					List[Any]        =    add_prefix_space
           _lowerCAmelCase   :					List[str]        =    pre_tok_class(**A_       )

      _lowerCAmelCase   :					Tuple        =    add_prefix_space

 def    __magic_name__       (		self :	Any     ,						*A_ :	str     ,						**A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    kwargs.get("is_split_into_words"     ,						A_       )
      if not (self.add_prefix_space or not is_split_into_words):
           raise Exception(
               F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
               " pretokenized inputs."       )

      return super()._batch_encode_plus(*A_     ,						**A_       )

 def    __magic_name__       (		self :	int     ,						*A_ :	Tuple     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    kwargs.get("is_split_into_words"     ,						A_       )

      if not (self.add_prefix_space or not is_split_into_words):
           raise Exception(
               F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
               " pretokenized inputs."       )

      return super()._encode_plus(*A_     ,						**A_       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	Optional[str] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self._tokenizer.model.save(A_     ,						name=A_       )
      return tuple(A_       )


 def    __magic_name__       (		self :	Any     ,						A_ :	"Conversation"       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    []
      for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(A_     ,						add_special_tokens=A_       ) + [self.eos_token_id]       )

      if len(A_       ) > self.model_max_length:
           _lowerCAmelCase   :					int        =    input_ids[-self.model_max_length :]
      return input_ids









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






