





'''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   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""image_processor""", """tokenizer"""]
   _UpperCamelCase       		=   """FlavaImageProcessor"""
   _UpperCamelCase       		=   ("""BertTokenizer""", """BertTokenizerFast""")


   def __init__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       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.'''    ,   _UpperCAmelCase    ,   )
           A       :       Dict							=						kwargs.pop('''feature_extractor'''					)

       A       :       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__(_UpperCAmelCase    ,   _UpperCAmelCase					)
       A       :       Union[str, Any]							=						self.image_processor


   def __call__( self    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = 0    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):

       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:
           A       :       Optional[int]							=						self.tokenizer(
               text=_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase    ,   padding=_UpperCAmelCase    ,   truncation=_UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   stride=_UpperCAmelCase    ,   pad_to_multiple_of=_UpperCAmelCase    ,   return_token_type_ids=_UpperCAmelCase    ,   return_attention_mask=_UpperCAmelCase    ,   return_overflowing_tokens=_UpperCAmelCase    ,   return_special_tokens_mask=_UpperCAmelCase    ,   return_offsets_mapping=_UpperCAmelCase    ,   return_length=_UpperCAmelCase    ,   verbose=_UpperCAmelCase    ,   return_tensors=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )
       if images is not None:
           A       :       Optional[int]							=						self.image_processor(
               _UpperCAmelCase    ,   return_image_mask=_UpperCAmelCase    ,   return_codebook_pixels=_UpperCAmelCase    ,   return_tensors=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )

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


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.batch_decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       A       :       Optional[Any]							=						self.tokenizer.model_input_names
       A       :       Optional[int]							=						self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)


   @property
   def 					snake_case				( self					):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,   _UpperCAmelCase    ,   )
       return self.image_processor_class


   @property
   def 					snake_case				( self					):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,   _UpperCAmelCase    ,   )
       return self.image_processor







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : str		)		->  Optional[Any]:
    A       :       Tuple							=						0
    while b > 0:
        if b & 1:
            res += a

        a += a
        b >>= 1

    return res
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]		)		->  Any:
    A       :       str							=						0
    while b > 0:
        if b & 1:
            A       :       List[Any]							=						((res % c) + (a % c)) % c

        a += a
        b >>= 1

    return res
