
'''simple docstring'''



import warnings
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    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
       SCREAMING_SNAKE_CASE      =  'FlavaImageProcessor'
       SCREAMING_SNAKE_CASE      =  ('BertTokenizer', 'BertTokenizerFast')






       def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""       ,							A__       ,							)
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

          _SCREAMING_SNAKE_CASE      =				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__(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.image_processor






       def __call__(							self       ,							A__ = None       ,							A__ = None       ,							A__ = True       ,							A__ = False       ,							A__ = False       ,							A__ = None       ,							A__ = 0       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = False       ,							A__ = False       ,							A__ = False       ,							A__ = False       ,							A__ = True       ,							A__ = None       ,							**A__       ,							)    ->			str:

          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:
             _SCREAMING_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_token_type_ids=A__       ,							return_attention_mask=A__       ,							return_overflowing_tokens=A__       ,							return_special_tokens_mask=A__       ,							return_offsets_mapping=A__       ,							return_length=A__       ,							verbose=A__       ,							return_tensors=A__       ,							**A__       ,							)
          if images is not None:
             _SCREAMING_SNAKE_CASE      =				self.image_processor(
                 A__       ,							return_image_mask=A__       ,							return_codebook_pixels=A__       ,							return_tensors=A__       ,							**A__       ,							)

          if text is not None and images is not None:
             encoding.update(A__      )
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Dict:
          return self.tokenizer.batch_decode(*A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Any:
          return self.tokenizer.decode(*A__       ,							**A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
          _SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							A__       ,							)
          return self.image_processor_class






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							A__       ,							)
          return self.image_processor








'''simple docstring'''



UpperCamelCase__      :		Dict       							=  {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


UpperCamelCase__      :		str       							=  {value: key for key, value in encode_dict.items()}
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				""""""
   for letter in word.lower():
      if letter.isalpha() or letter == " ":
         encoded += encode_dict[letter]
      else:
         raise Exception("""encode() accepts only letters of the alphabet and spaces"""						)
   return encoded
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   if set(SCREAMING_SNAKE_CASE_						) - {"A", "B", " "} != set():
      raise Exception("""decode() accepts only 'A', 'B' and spaces"""						)
   _SCREAMING_SNAKE_CASE      =				""""""
   for word in coded.split():
      while len(SCREAMING_SNAKE_CASE_						) != 0:
         decoded += decode_dict[word[:5]]
         _SCREAMING_SNAKE_CASE      =				word[5:]
      decoded += " "
   return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()






