





'''simple docstring'''







import warnings
from typing import List, Optional, Union

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										=					'''ViltImageProcessor'''
     _UpperCAmelCase										=					('''BertTokenizer''', '''BertTokenizerFast''')







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	Union[str, Any]:
      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__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      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__    )
      snake_case				=			self.image_processor







     def __call__( self    ,   A__    ,   A__ = None    ,   A__ = True    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = 0    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = False    ,   A__ = False    ,   A__ = False    ,   A__ = False    ,   A__ = True    ,   A__ = None    ,   **A__    ,   ) ->	BatchEncoding:
      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__    ,   )
      # add pixel_values + pixel_mask
      snake_case				=			self.image_processor(A__    ,   return_tensors=A__    )
      encoding.update(A__    )

      return encoding







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Optional[int]:
      return self.tokenizer.batch_decode(*A__    ,   **A__    )







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







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







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








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			value
      snake_case				=			None
      snake_case				=			None




class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			tree







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      if node is None:
       return 0
      return node.value + (
          self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
      )







     def __iter__( self    ) ->	Iterator[int]:
      yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

