






"""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    __lowerCamelCase    (  __UpperCAmelCase  ):
   a      :				Union[str, Any] 						=['''image_processor''', '''tokenizer''']
   a      :				List[str] 						='''FlavaImageProcessor'''
   a      :				str 						=('''BertTokenizer''', '''BertTokenizerFast''')

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

       UpperCamelCase__         =    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__(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )
       UpperCamelCase__         =    self.image_processor

   def __call__(					self ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = True ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = None ,				snake_case_ = 0 ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = True ,				snake_case_ = None ,				**snake_case_ ,				)		->       List[Any]:

       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(
               text=__SCREAMING_SNAKE_CASE ,				add_special_tokens=__SCREAMING_SNAKE_CASE ,				padding=__SCREAMING_SNAKE_CASE ,				truncation=__SCREAMING_SNAKE_CASE ,				max_length=__SCREAMING_SNAKE_CASE ,				stride=__SCREAMING_SNAKE_CASE ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,				return_token_type_ids=__SCREAMING_SNAKE_CASE ,				return_attention_mask=__SCREAMING_SNAKE_CASE ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE ,				return_length=__SCREAMING_SNAKE_CASE ,				verbose=__SCREAMING_SNAKE_CASE ,				return_tensors=__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE ,				)
       if images is not None:
           UpperCamelCase__         =    self.image_processor(
               __SCREAMING_SNAKE_CASE ,				return_image_mask=__SCREAMING_SNAKE_CASE ,				return_codebook_pixels=__SCREAMING_SNAKE_CASE ,				return_tensors=__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE ,				)

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       Tuple:
       return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       List[str]:
       return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.tokenizer.model_input_names
       UpperCamelCase__         =    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,				__SCREAMING_SNAKE_CASE ,				)
       return self.image_processor_class







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












"""simple docstring"""





from copy import deepcopy
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None ,				snake_case_ = None      )		->       None:

       if arr is None and size is not None:
           UpperCamelCase__         =    size
           UpperCamelCase__         =    [0] * size
       elif arr is not None:
           self.init(snake_case_      )
       else:
           raise ValueError('Either arr or size must be specified'      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    deepcopy(snake_case_      )
       for i in range(1 ,				self.size      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               self.tree[j] += self.tree[i]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       list[int]:
       UpperCamelCase__         =    self.tree[:]
       for i in range(self.size - 1 ,				0 ,				-1      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               arr[j] -= arr[i]
       return arr

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index + (index & (-index))

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index - (index & (-index))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if index == 0:
           self.tree[0] += value
           return
       while index < self.size:
           self.tree[index] += value
           UpperCamelCase__         =    self.next_(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       self.add(snake_case_ ,				value - self.get(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       if right == 0:
           return 0
       UpperCamelCase__         =    self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
           result += self.tree[right]
           UpperCamelCase__         =    self.prev(snake_case_      )
       return result

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       return self.prefix(snake_case_      ) - self.prefix(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       return self.query(snake_case_ ,				index + 1      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       value -= self.tree[0]
       if value < 0:
           return -1

       UpperCamelCase__         =    1  # Largest power of 2 <= size
       while j * 2 < self.size:
           j *= 2

       UpperCamelCase__         =    0

       while j > 0:
           if i + j < self.size and self.tree[i + j] <= value:
               value -= self.tree[i + j]
               i += j
           j //= 2
       return i


if __name__ == "__main__":
  import doctest

  doctest.testmod()




