






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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''FlavaImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







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

								__magic_name__		:Optional[int]   =   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__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = True							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""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:
												__magic_name__		:Any   =   self.tokenizer(
												    text=__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase							,       padding=__lowerCAmelCase							,       truncation=__lowerCAmelCase							,       max_length=__lowerCAmelCase							,       stride=__lowerCAmelCase							,       pad_to_multiple_of=__lowerCAmelCase							,       return_token_type_ids=__lowerCAmelCase							,       return_attention_mask=__lowerCAmelCase							,       return_overflowing_tokens=__lowerCAmelCase							,       return_special_tokens_mask=__lowerCAmelCase							,       return_offsets_mapping=__lowerCAmelCase							,       return_length=__lowerCAmelCase							,       verbose=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase							,       )
								if images is not None:
												__magic_name__		:Tuple   =   self.image_processor(
												    __lowerCAmelCase							,       return_image_mask=__lowerCAmelCase							,       return_codebook_pixels=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase							,       )

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







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor














SCREAMING_SNAKE_CASE__   :      Tuple				=		{
    """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""",
    """ """: """ """,
}


SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		{value: key for key, value in encode_dict.items()}

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   ''''''
				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   __lowercase       (			snake_case			):







				"""simple docstring"""






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


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

					testmod()






