'''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 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "naver-clova-ix/donut-base-finetuned-docvqa"
       SCREAMING_SNAKE_CASE							=    (
           "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."
       )
       SCREAMING_SNAKE_CASE							=    "document_qa"
       SCREAMING_SNAKE_CASE							=    AutoProcessor
       SCREAMING_SNAKE_CASE							=    VisionEncoderDecoderModel

       SCREAMING_SNAKE_CASE							=    ["image", "text"]
       SCREAMING_SNAKE_CASE							=    ["text"]

       def __init__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  str:
        if not is_vision_available():
         raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )

        super().__init__(*__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Union[str, Any]:
        lowercase__				:    List[Any]  =					'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
        lowercase__				:    str  =					task_prompt.replace('''{user_input}'''	,   __lowerCAmelCase )
        lowercase__				:    Any  =					self.pre_processor.tokenizer(
            __lowerCAmelCase	,   add_special_tokens=__lowerCAmelCase	,   return_tensors='''pt''' ).input_ids
        lowercase__				:    Dict  =					self.pre_processor(__lowerCAmelCase	,   return_tensors='''pt''' ).pixel_values

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

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Any:
        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=__lowerCAmelCase	,   pad_token_id=self.pre_processor.tokenizer.pad_token_id	,   eos_token_id=self.pre_processor.tokenizer.eos_token_id	,   use_cache=__lowerCAmelCase	,   num_beams=1	,   bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]	,   return_dict_in_generate=__lowerCAmelCase	,   ).sequences


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

        return sequence["answer"]


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square(UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowercase__				:    int  =					update_area_of_max_square(UpperCAmelCase		,     col + 1     )
  lowercase__				:    Any  =					update_area_of_max_square(row + 1		,     col + 1     )
  lowercase__				:    str  =					update_area_of_max_square(row + 1		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    List[Any]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    List[Any]  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Dict  =					[0]
 update_area_of_max_square(0		,     0     )
 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square_using_dp_array(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowercase__				:    int  =					update_area_of_max_square_using_dp_array(UpperCAmelCase		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					update_area_of_max_square_using_dp_array(row + 1		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Any  =					update_area_of_max_square_using_dp_array(row + 1		,     UpperCAmelCase		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    Optional[int]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    Any  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   lowercase__				:    int  =					sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Any  =					[0]
 lowercase__				:    List[Any]  =					[[-1] * cols for _ in range(UpperCAmelCase     )]
 update_area_of_max_square_using_dp_array(0		,     0		,     UpperCAmelCase     )

 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					[[0] * (cols + 1) for _ in range(rows + 1     )]
 lowercase__				:    str  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    str  =					dp_array[row][col + 1]
   lowercase__				:    Optional[Any]  =					dp_array[row + 1][col + 1]
   lowercase__				:    str  =					dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowercase__				:    Dict  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    str  =					max(dp_array[row][col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    Any  =					0

 return largest_square_area
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					[0] * (cols + 1)
 lowercase__				:    str  =					[0] * (cols + 1)
 lowercase__				:    Tuple  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    List[Any]  =					current_row[col + 1]
   lowercase__				:    Any  =					next_row[col + 1]
   lowercase__				:    Optional[Any]  =					next_row[col]

   if mat[row][col] == 1:
    lowercase__				:    List[str]  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    List[str]  =					max(current_row[col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    int  =					0
  lowercase__				:    int  =					current_row

 return largest_square_area


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

