

def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







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







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE      						=[[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_:
                    SCREAMING_SNAKE_CASE      						=max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],    dp[i - 1][w_]			)
               else:
                    SCREAMING_SNAKE_CASE      						=dp[i - 1][w_]

     return dp[n][w_], dp







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







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

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

     SCREAMING_SNAKE_CASE      						=knapsack(_lowerCamelCase,    _lowerCamelCase,    _lowerCamelCase,    _lowerCamelCase			)
     SCREAMING_SNAKE_CASE      						=set()
     _construct_solution(_lowerCamelCase,    _lowerCamelCase,    _lowerCamelCase,    _lowerCamelCase,    _lowerCamelCase			)

     return optimal_val, example_optional_set







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_			):





     """simple docstring"""







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


if __name__ == "__main__":
 _lowerCamelCase							       =[3, 2, 4, 4]
 _lowerCamelCase							       =[4, 3, 2, 3]
 _lowerCamelCase							       =4
 _lowerCamelCase							       =6
 _lowerCamelCase							       =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 _lowerCamelCase							       =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
 _lowerCamelCase							       =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)


"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		"naver-clova-ix/donut-base-finetuned-docvqa"
     _UpperCamelCase							:			Dict							=		(
         "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
         "should be the document containing the information, as well as a `question` that is the question about the "
         "document. It returns a text that contains the answer to the question."
     )
     _UpperCamelCase							:			Optional[int]							=		"document_qa"
     _UpperCamelCase							:			Any							=		AutoProcessor
     _UpperCamelCase							:			Union[str, Any]							=		VisionEncoderDecoderModel

     _UpperCamelCase							:			Union[str, Any]							=		["image", "text"]
     _UpperCamelCase							:			List[str]							=		["text"]






     def __init__(		self				,     *a__				,     **a__  ):
       if not is_vision_available():
         raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""  )

       super().__init__(*a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
       _lowerCAmelCase						:       Dict  			=     task_prompt.replace("""{user_input}"""				,     a__  )
       _lowerCAmelCase						:       str  			=     self.pre_processor.tokenizer(
           a__				,     add_special_tokens=a__				,     return_tensors="""pt"""  ).input_ids
       _lowerCAmelCase						:       Dict  			=     self.pre_processor(a__				,     return_tensors="""pt"""  ).pixel_values

       return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






     def     __A  (		self				,     a__  ):
       return self.model.generate(
           inputs["""pixel_values"""].to(self.device  )				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device  )				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=a__				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=a__				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=a__				,     ).sequences






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     self.pre_processor.batch_decode(a__  )[0]
       _lowerCAmelCase						:       int  			=     sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     re.sub(r"""<.*?>"""				,     """"""				,     a__				,     count=1  ).strip()  # remove first task start token
       _lowerCAmelCase						:       List[str]  			=     self.pre_processor.tokenajson(a__  )

       return sequence["answer"]




