





'''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 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''naver-clova-ix/donut-base-finetuned-docvqa'''
     lowerCAmelCase						     =			(
         '''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.'''
     )
     lowerCAmelCase						     =			'''document_qa'''
     lowerCAmelCase						     =			AutoProcessor
     lowerCAmelCase						     =			VisionEncoderDecoderModel

     lowerCAmelCase						     =			['''image''', '''text''']
     lowerCAmelCase						     =			['''text''']


     def __init__(		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          if not is_vision_available():
               raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.')

          super().__init__(*_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[int]						=			'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
          __A		:					List[Any]						=			task_prompt.replace('{user_input}'		,      _UpperCAmelCase)
          __A		:					Tuple						=			self.pre_processor.tokenizer(
              _UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase		,      return_tensors='pt').input_ids
          __A		:					Any						=			self.pre_processor(_UpperCAmelCase		,      return_tensors='pt').pixel_values

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          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=_UpperCAmelCase		,      pad_token_id=self.pre_processor.tokenizer.pad_token_id		,      eos_token_id=self.pre_processor.tokenizer.eos_token_id		,      use_cache=_UpperCAmelCase		,      num_beams=1		,      bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,      return_dict_in_generate=_UpperCAmelCase		,      ).sequences


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.pre_processor.batch_decode(_UpperCAmelCase)[0]
          __A		:					Optional[Any]						=			sequence.replace(self.pre_processor.tokenizer.eos_token		,      '')
          __A		:					int						=			sequence.replace(self.pre_processor.tokenizer.pad_token		,      '')
          __A		:					Any						=			re.sub(R'<.*?>'		,      ''		,      _UpperCAmelCase		,      count=1).strip()  # remove first task start token
          __A		:					List[str]						=			self.pre_processor.tokenajson(_UpperCAmelCase)

          return sequence["answer"]






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[Any]:  # noqa: E741
     __A		:					Tuple						=			len(__snake_case     )
     __A		:					Optional[int]						=			0
     __A		:					str						=			[0] * n
     __A		:					int						=			[False] * n
     __A		:					Tuple						=			[False] * n

     def dfs(__snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					int     ):
          if parent == root:
               out_edge_count += 1
          __A		:					str						=			True
          __A		:					Tuple						=			at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    __A		:					Optional[int]						=			dfs(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
                    __A		:					int						=			min(low[at]					,				low[to]     )

                    # AP found via bridge
                    if at < low[to]:
                         __A		:					Tuple						=			True
                    # AP found via cycle
                    if at == low[to]:
                         __A		:					Optional[Any]						=			True
               else:
                    __A		:					Any						=			min(low[at]					,				__snake_case     )
          return out_edge_count

     for i in range(__snake_case     ):
          if not visited[i]:
               __A		:					Tuple						=			0
               __A		:					List[Any]						=			dfs(__snake_case					,				__snake_case					,				-1					,				__snake_case     )
               __A		:					Union[str, Any]						=			out_edge_count > 1

     for x in range(len(__snake_case     )     ):
          if is_art[x] is True:
               print(__snake_case     )


# Adjacency list of graph
lowercase__       : Tuple         =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)