





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Any							=   ['image_processor', 'tokenizer']
      A_						:    str							=   'BlipImageProcessor'
      A_						:    str							=   'AutoTokenizer'


      def __init__(self	:				str    ,      a__	:				int    ,      a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       False
         super().__init__(a__    ,      a__				)
         __snake_case								=       self.image_processor


      def __call__(self	:				Tuple    ,      a__	:				ImageInput = None    ,      a__	:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,      a__	:				bool = True    ,      a__	:				Union[bool, str, PaddingStrategy] = False    ,      a__	:				Union[bool, str, TruncationStrategy] = None    ,      a__	:				Optional[int] = None    ,      a__	:				int = 0    ,      a__	:				Optional[int] = None    ,      a__	:				Optional[bool] = None    ,      a__	:				bool = False    ,      a__	:				bool = False    ,      a__	:				bool = False    ,      a__	:				bool = False    ,      a__	:				bool = False    ,      a__	:				bool = True    ,      a__	:				Optional[Union[str, TensorType]] = None    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         if images is None and text is None:
            raise ValueError('''You have to specify either images or text.'''				)

         # Get only text
         if images is None:
            __snake_case								=       self.tokenizer
            __snake_case								=       self.tokenizer(
                text=a__    ,      add_special_tokens=a__    ,      padding=a__    ,      truncation=a__    ,      max_length=a__    ,      stride=a__    ,      pad_to_multiple_of=a__    ,      return_attention_mask=a__    ,      return_overflowing_tokens=a__    ,      return_special_tokens_mask=a__    ,      return_offsets_mapping=a__    ,      return_token_type_ids=a__    ,      return_length=a__    ,      verbose=a__    ,      return_tensors=a__    ,      **a__    ,      )
            return text_encoding

         # add pixel_values
         __snake_case								=       self.image_processor(a__    ,      return_tensors=a__				)

         if text is not None:
            __snake_case								=       self.tokenizer(
                text=a__    ,      add_special_tokens=a__    ,      padding=a__    ,      truncation=a__    ,      max_length=a__    ,      stride=a__    ,      pad_to_multiple_of=a__    ,      return_attention_mask=a__    ,      return_overflowing_tokens=a__    ,      return_special_tokens_mask=a__    ,      return_offsets_mapping=a__    ,      return_token_type_ids=a__    ,      return_length=a__    ,      verbose=a__    ,      return_tensors=a__    ,      **a__    ,      )
         else:
            __snake_case								=       None

         if text_encoding is not None:
            encoding_image_processor.update(a__				)

         return encoding_image_processor


      def        a			(self	:				int    ,      *a__	:				Union[str, Any]    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__    ,      **a__				)


      def        a			(self	:				List[str]    ,      *a__	:				Any    ,      **a__	:				Optional[int]				):





         """simple docstring"""



         return self.tokenizer.decode(*a__    ,      **a__				)


      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       self.tokenizer.model_input_names
         __snake_case								=       self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)








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


snake_case_           =      logging.get_logger(__name__)

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

snake_case_           =      {
    '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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    List[str]							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    str							=   ['input_ids', 'attention_mask']
      A_						:    Optional[Any]							=   None


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





         """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__    ,      )
         __snake_case								=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
         if pre_tok_state.get('''add_prefix_space'''    ,      a__				) != add_prefix_space:
            __snake_case								=       getattr(a__    ,      pre_tok_state.pop('''type'''				)				)
            __snake_case								=       add_prefix_space
            __snake_case								=       pre_tok_class(**a__				)

         __snake_case								=       add_prefix_space


      def        a			(self	:				int    ,      *a__	:				Tuple    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       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        a			(self	:				List[str]    ,      *a__	:				List[str]    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       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        a			(self	:				List[Any]    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)


      def        a			(self	:				Tuple    ,      a__	:				"Conversation"				):





         """simple docstring"""



         __snake_case								=       []
         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:
            __snake_case								=       input_ids[-self.model_max_length :]
         return input_ids

