





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[Any]         =   ['''image_processor''', '''tokenizer''']
       _UpperCamelCase				:					Optional[Any]         =   '''AutoImageProcessor'''
       _UpperCamelCase				:					Optional[Any]         =   '''AutoTokenizer'''

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              super().__init__(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.image_processor

       def __call__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: Tuple=None					,   _SCREAMING_SNAKE_CASE: Any=None					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> str:




              """simple docstring"""


              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:
                     UpperCamelCase_          =  self.tokenizer(_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              if images is not None:
                     UpperCamelCase_          =  self.image_processor(_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              if text is not None and images is not None:
                     UpperCamelCase_          =  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 lowercase (		self: List[Any]					,   *_SCREAMING_SNAKE_CASE: Any					,   **_SCREAMING_SNAKE_CASE: List[Any]		)       -> List[Any]:




              """simple docstring"""


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

       def lowercase (		self: List[Any]					,   *_SCREAMING_SNAKE_CASE: str					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> Dict:




              """simple docstring"""


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



       @property
       def lowercase (		self: Optional[Any]		)       -> str:




              """simple docstring"""


              return ["input_ids", "attention_mask", "pixel_values"]








from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCAmelCase        =				{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCAmelCase        =				{
    'facebook/blenderbot_small-90M': 5_1_2,
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					str         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					str         =   BlenderbotSmallTokenizer

       def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=None					,   _SCREAMING_SNAKE_CASE: List[str]=None					,   _SCREAMING_SNAKE_CASE: Optional[int]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[Any]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[str]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: int=True					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> int:




              """simple docstring"""


              super().__init__(
                  ByteLevelBPETokenizer(
                      vocab=_SCREAMING_SNAKE_CASE					,   merges=_SCREAMING_SNAKE_CASE					,   add_prefix_space=_SCREAMING_SNAKE_CASE					,   trim_offsets=_SCREAMING_SNAKE_CASE					,   )					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  add_prefix_space

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Any=None		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [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]

