

import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						int	    =    "BlipImageProcessor"
							UpperCAmelCase__       :						Dict	    =    "AutoTokenizer"


							def __init__(			self       ,							_a       ,							_a       ,							_a       )    ->    Union[str, Any]:
												super().__init__(_a       ,							_a       )

												# add QFormer tokenizer
												_a	:    int         =		qformer_tokenizer


							def __call__(			self       ,							_a = None       ,							_a = None       ,							_a = True       ,							_a = False       ,							_a = None       ,							_a = None       ,							_a = 0       ,							_a = None       ,							_a = None       ,							_a = False       ,							_a = False       ,							_a = False       ,							_a = False       ,							_a = False       ,							_a = True       ,							_a = None       ,							**_a       ,							)    ->    BatchFeature:
												if images is None and text is None:
																	raise ValueError('''You have to specify at least images or text.'''       )

												_a	:    Union[str, Any]         =		BatchFeature()

												if text is not None:
																	_a	:    Optional[int]         =		self.tokenizer(
																	    text=_a       ,							add_special_tokens=_a       ,							padding=_a       ,							truncation=_a       ,							max_length=_a       ,							stride=_a       ,							pad_to_multiple_of=_a       ,							return_attention_mask=_a       ,							return_overflowing_tokens=_a       ,							return_special_tokens_mask=_a       ,							return_offsets_mapping=_a       ,							return_token_type_ids=_a       ,							return_length=_a       ,							verbose=_a       ,							return_tensors=_a       ,							**_a       ,							)
																	encoding.update(_a       )
																	_a	:    int         =		self.qformer_tokenizer(
																	    text=_a       ,							add_special_tokens=_a       ,							padding=_a       ,							truncation=_a       ,							max_length=_a       ,							stride=_a       ,							pad_to_multiple_of=_a       ,							return_attention_mask=_a       ,							return_overflowing_tokens=_a       ,							return_special_tokens_mask=_a       ,							return_offsets_mapping=_a       ,							return_token_type_ids=_a       ,							return_length=_a       ,							verbose=_a       ,							return_tensors=_a       ,							**_a       ,							)
																	_a	:    Tuple         =		qformer_text_encoding.pop('''input_ids'''       )
																	_a	:    Union[str, Any]         =		qformer_text_encoding.pop('''attention_mask'''       )

												if images is not None:
																	_a	:    Optional[int]         =		self.image_processor(_a       ,							return_tensors=_a       )
																	encoding.update(_a       )

												return encoding


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def 				__lowercase				(			self       )    ->    str:
												_a	:    Union[str, Any]         =		self.tokenizer.model_input_names
												_a	:    Any         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )


							def 				__lowercase				(			self       ,							_a       ,							**_a       )    ->    Dict:
												if os.path.isfile(_a       ):
																	raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""       )
												os.makedirs(_a       ,							exist_ok=_a       )
												_a	:    Union[str, Any]         =		os.path.join(_a       ,							'''qformer_tokenizer'''       )
												self.qformer_tokenizer.save_pretrained(_a       )
												return super().save_pretrained(_a       ,							**_a       )




							@classmethod
							def 				__lowercase				(			cls       ,							_a       ,							**_a       )    ->    List[Any]:
												_a	:    List[Any]         =		AutoTokenizer.from_pretrained(_a       ,							subfolder='''qformer_tokenizer'''       )
												_a	:    List[Any]         =		cls._get_arguments_from_pretrained(_a       ,							**_a       )
												args.append(_a       )
												return cls(*_a       )






def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations(__a      :			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(__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations_with_dp_array(
					    __a      :			int		,__a      :			list[int]	) -> int:
										if target < 0:
															return 0
										if target == 0:
															return 1
										if dp_array[target] != -1:
															return dp_array[target]
										_a	:    Union[str, Any]         =		sum(
										    count_of_possible_combinations_with_dp_array(target - item		,__a	)
										    for item in array	)
										_a	:    Optional[int]         =		answer
										return answer

					_a	:    int         =		[-1] * (target + 1)
					return count_of_possible_combinations_with_dp_array(__a		,__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					_a	:    str         =		[0] * (target + 1)
					_a	:    Optional[Any]         =		1

					for i in range(1		,target + 1	):
										for j in range(__a	):
															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__				=		3
			a__				=		5
			a__				=		[1, 2, 5]
			print(combination_sum_iv(n, array, target))



