
"""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(_lowercase       ):
     __snake_case:	Any						=     ['image_processor', 'tokenizer']
     __snake_case:	List[Any]						=     'ViltImageProcessor'
     __snake_case:	List[str]						=     ('BertTokenizer', 'BertTokenizerFast')



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=None     ,				**__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         a__		=			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     ,				)
             a__		=			kwargs.pop('feature_extractor'   )

         a__		=			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   )
         a__		=			self.image_processor



     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = True     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = 0     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = True     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     BatchEncoding:







         """simple docstring"""



         a__		=			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     ,				)
         # add pixel_values + pixel_mask
         a__		=			self.image_processor(__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         encoding.update(__SCREAMING_SNAKE_CASE   )

         return encoding



     def 			lowercase__  (							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[int]:







         """simple docstring"""



         return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Dict:







         """simple docstring"""



         return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )



     @property
     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.tokenizer.model_input_names
         a__		=			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )



     @property
     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         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 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         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"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		int     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item     ) for item in array     )

    return count_of_possible_combinations(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        a__		=			sum(
            count_of_possible_combinations_with_dp_array(target - item							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

    for i in range(1							,		target + 1     ):
        for j in range(UpperCamelCase     ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


