






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_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"BridgeTowerImageProcessor"
     SCREAMING_SNAKE_CASE   						=				("RobertaTokenizer", "RobertaTokenizerFast")




     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			Union[bool, str, PaddingStrategy] = False ,	UpperCamelCase_       :			Union[bool, str, TruncationStrategy] = None ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			int = 0 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			Optional[bool] = None ,	UpperCamelCase_       :			Optional[bool] = None ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			Optional[Union[str, TensorType]] = None ,	**UpperCamelCase_       :			str ,	):


         """simple docstring"""



         __A         =	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_ ,	)
         # add pixel_values + pixel_mask
         __A         =	self.image_processor(
             UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	do_normalize=UpperCamelCase_ ,	do_center_crop=UpperCamelCase_ ,	**UpperCamelCase_				)
         encoding.update(UpperCamelCase_				)

         return encoding




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			str ,	**UpperCamelCase_       :			Any				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	*UpperCamelCase_       :			List[str] ,	**UpperCamelCase_       :			str				):


         """simple docstring"""



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





     @property
     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """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				)				)












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




