




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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      ["""image_processor""", """tokenizer"""]
    lowerCamelCase_      :	Optional[Any]	     =      """FlavaImageProcessor"""
    lowerCamelCase_      :	Optional[Any]	     =      ("""BertTokenizer""", """BertTokenizerFast""")







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      Tuple			 =					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__     ,  )
                  lowerCamelCase   :      Dict			 =					kwargs.pop("feature_extractor"     )

           lowerCamelCase   :      List[Any]			 =					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__     )
           lowerCamelCase   :      List[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__     ,  )     ->      Optional[int]:

           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:
                  lowerCamelCase   :      Union[str, Any]			 =					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:
                  lowerCamelCase   :      str			 =					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     _lowercase     (    self     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Optional[int]:
           return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Optional[int]:
           return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__     )







    @property
    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Tuple			 =					self.tokenizer.model_input_names
           lowerCamelCase   :      Dict			 =					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







    @property
    def     _lowercase     (    self     )     ->      Tuple:
           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     _lowercase     (    self     )     ->      Any:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,  UpperCamelCase__     ,  )
           return self.image_processor












import random

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> tuple:
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					[], [], []
       for element in data:
              if element < pivot:
                     less.append(_SCREAMING_SNAKE_CASE    )
              elif element > pivot:
                     greater.append(_SCREAMING_SNAKE_CASE    )
              else:
                     equal.append(_SCREAMING_SNAKE_CASE    )
       return less, equal, greater


def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(_SCREAMING_SNAKE_CASE    ) or index < 0:
              return None

       lowerCamelCase   :      List[Any]			 =					items[random.randint(0 ,len(_SCREAMING_SNAKE_CASE    ) - 1    )]
       lowerCamelCase   :      Dict			 =					0
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					_partition(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					len(_SCREAMING_SNAKE_CASE    )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # must be in larger
       else:
              return quick_select(_SCREAMING_SNAKE_CASE ,index - (m + count)    )






