






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 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					["image_processor", "tokenizer"]
							A_						  =					"BlipImageProcessor"
							A_						  =					"AutoTokenizer"

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

												# add QFormer tokenizer
												_A								= qformer_tokenizer

							def __call__(  self:   List[Any]		,     __A:   ImageInput = None		,     __A:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __A:   bool = True		,     __A:   Union[bool, str, PaddingStrategy] = False		,     __A:   Union[bool, str, TruncationStrategy] = None		,     __A:   Optional[int] = None		,     __A:   int = 0		,     __A:   Optional[int] = None		,     __A:   Optional[bool] = None		,     __A:   bool = False		,     __A:   bool = False		,     __A:   bool = False		,     __A:   bool = False		,     __A:   bool = False		,     __A:   bool = True		,     __A:   Optional[Union[str, TensorType]] = None		,     **__A:   Optional[Any]		,     )    ->				BatchFeature:
												if images is None and text is None:
																	raise ValueError('''You have to specify at least images or text.'''   )

												_A								= BatchFeature()

												if text is not None:
																	_A								= 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								= 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								= qformer_text_encoding.pop('''input_ids'''   )
																	_A								= qformer_text_encoding.pop('''attention_mask'''   )

												if images is not None:
																	_A								= self.image_processor(__A		,     return_tensors=__A   )
																	encoding.update(__A   )

												return encoding

							def       __A (  self:   Any		,     *__A:   Any		,     **__A:   Dict   )    ->				Union[str, Any]:
												return self.tokenizer.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Dict		,     *__A:   Tuple		,     **__A:   Any   )    ->				List[Any]:
												return self.tokenizer.decode(*__A		,     **__A   )

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

							def       __A (  self:   Any		,     __A:   Optional[Any]		,     **__A:   int   )    ->				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								= os.path.join(__A		,     '''qformer_tokenizer'''   )
												self.qformer_tokenizer.save_pretrained(__A   )
												return super().save_pretrained(__A		,     **__A   )



							@classmethod
							def       __A (  cls:   Optional[int]		,     __A:   Optional[int]		,     **__A:   List[Any]   )    ->				List[str]:
												_A								= AutoTokenizer.from_pretrained(__A		,     subfolder='''qformer_tokenizer'''   )
												_A								= cls._get_arguments_from_pretrained(__A		,     **__A   )
												args.append(__A   )
												return cls(*__A   )











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															_A								= mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )
										else:
															_A								= max(
															    mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )					,			mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			j - wt[i - 1] ) + val[i - 1]					,			)
										_A								= val
					return f[i][j]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= [[0] * (w + 1) for _ in range(n + 1 )]

					for i in range(1					,			n + 1 ):
										for w_ in range(1					,			w + 1 ):
															if wt[i - 1] <= w_:
																				_A								= max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]					,			dp[i - 1][w_] )
															else:
																				_A								= dp[i - 1][w_]

					return dp[n][w_], dp




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if not (isinstance(_lowercase					,			(list, tuple) ) and isinstance(_lowercase					,			(list, tuple) )):
										raise ValueError(
										    '''Both the weights and values vectors must be either lists or tuples''' )

					_A								= len(_lowercase )
					if num_items != len(_lowercase ):
										_A								= (
										    '''The number of weights must be the same as the number of values.\n'''
										    f"""But got {num_items} weights and {len(_lowercase )} values"""
										)
										raise ValueError(_lowercase )
					for i in range(_lowercase ):
										if not isinstance(wt[i]					,			_lowercase ):
															_A								= (
															    '''All weights must be integers but got weight of '''
															    f"""type {type(wt[i] )} at index {i}"""
															)
															raise TypeError(_lowercase )

					_A							,_A								= knapsack(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= set()
					_construct_solution(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if i > 0 and j > 0:
										if dp[i - 1][j] == dp[i][j]:
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			_lowercase					,			_lowercase )
										else:
															optimal_set.add(_lowercase )
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			j - wt[i - 1]					,			_lowercase )


if __name__ == "__main__":
				__A      =				[3, 2, 4, 4]
				__A      =				[4, 3, 2, 3]
				__A      =				4
				__A      =				6
				__A      =				[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
				__A ,  __A      =				knapsack(w, wt, val, n)
				print(optimal_solution)
				print(mf_knapsack(n, wt, val, w))  # switched the n and w

				# testing the dynamic programming problem with example
				# the optimal subset for the above example are items 3 and 4
				__A ,  __A      =				knapsack_with_example_solution(w, wt, val)
				assert optimal_solution == 8
				assert optimal_subset == {3, 4}
				print('optimal_value = ', optimal_solution)
				print('An optimal subset corresponding to the optimal value', optimal_subset)



