'''simple docstring'''

from __future__ import annotations

A_     :						Tuple    							=list[tuple[int, int]]

A_     :						List[Any]    							=[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

A_     :						str    							=([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
     lowerCAmelCase_			=      pos_x
     lowerCAmelCase_			=      pos_y
     lowerCAmelCase_			=      (pos_y, pos_x)
     lowerCAmelCase_			=      goal_x
     lowerCAmelCase_			=      goal_y
     lowerCAmelCase_			=      g_cost
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      self.calculate_heuristic()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      abs(self.pos_x - self.goal_x )
     lowerCAmelCase_			=      abs(self.pos_y - self.goal_y )
     return dx + dy







  def __lt__(	self    ,     _lowerCamelCase ):
     return self.f_cost < other.f_cost






class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      Node(start[1]    ,     start[0]    ,     goal[1]    ,     goal[0]    ,     0    ,     _lowerCamelCase )
     lowerCAmelCase_			=      Node(goal[1]    ,     goal[0]    ,     goal[1]    ,     goal[0]    ,     9_9999    ,     _lowerCamelCase )

     lowerCAmelCase_			=      [self.start]
     lowerCAmelCase_			=      []

     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        lowerCAmelCase_			=      self.open_nodes.pop(0 )

        if current_node.pos == self.target.pos:
           lowerCAmelCase_			=      True
           return self.retrace_path(_lowerCamelCase )

        self.closed_nodes.append(_lowerCamelCase )
        lowerCAmelCase_			=      self.get_successors(_lowerCamelCase )

        for child_node in successors:
           if child_node in self.closed_nodes:
              continue

           if child_node not in self.open_nodes:
              self.open_nodes.append(_lowerCamelCase )
           else:
              # retrieve the best current path
              lowerCAmelCase_			=      self.open_nodes.pop(self.open_nodes.index(_lowerCamelCase ) )

              if child_node.g_cost < better_node.g_cost:
                 self.open_nodes.append(_lowerCamelCase )
              else:
                 self.open_nodes.append(_lowerCamelCase )

     if not self.reached:
        return [self.start.pos]
     return None





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      []
     for action in delta:
        lowerCAmelCase_			=      parent.pos_x + action[1]
        lowerCAmelCase_			=      parent.pos_y + action[0]

        if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_lowerCamelCase ) - 1):
           continue

        if grid[pos_y][pos_x] != 0:
           continue

        successors.append(
            Node(
                _lowerCamelCase    ,     _lowerCamelCase    ,     self.target.pos_y    ,     self.target.pos_x    ,     parent.g_cost + 1    ,     _lowerCamelCase    ,     ) )
     return successors







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      node
     lowerCAmelCase_			=      []
     while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x) )
        lowerCAmelCase_			=      current_node.parent
     path.reverse()
     return path


if __name__ == "__main__":
 A_     :						Any    							=(0, 0)
 A_     :						Dict    							=(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 print('''------''')

 A_     :						Union[str, Any]    							=GreedyBestFirst(init, goal)
 A_     :						Union[str, Any]    							=greedy_bf.search()
 if path:
  for pos_x, pos_y in path:
   A_     :						Optional[int]    							=2

  for elem in grid:
   print(elem)






'''simple docstring'''

import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image

 from transformers.image_utils import load_image
else:


 class     __UpperCAmelCase       :





   @staticmethod
   def 						UpperCAmelCase_	(	*_lowerCamelCase    ,     **_lowerCamelCase ):
      pass






 def snake_case_      (					__snake_case  :							int)			->					Union[str, Any]:
    return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A_     :						Any    							=(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)


@is_pipeline_test
@require_torch
@require_vision
class     __UpperCAmelCase       ( unittest.TestCase		):
  __A :				Union[str, Any]											= MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





  @require_pytesseract
  @require_vision
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model=_lowerCamelCase    ,     tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase )

     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      list(zip(*apply_tesseract(load_image(_lowerCamelCase )    ,     _lowerCamelCase    ,     '''''' ) ) )
     lowerCAmelCase_			=      '''What is the placebo?'''
     lowerCAmelCase_			=      [
         {
             '''image''': load_image(_lowerCamelCase ),
             '''question''': question,
         },
         {
             '''image''': image,
             '''question''': question,
         },
         {
             '''image''': image,
             '''question''': question,
             '''word_boxes''': word_boxes,
         },
     ]
     return dqa_pipeline, examples





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      dqa_pipeline(_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         _lowerCamelCase    ,     [
             [
                 {'''score''': ANY(_lowerCamelCase ), '''answer''': ANY(_lowerCamelCase ), '''start''': ANY(_lowerCamelCase ), '''end''': ANY(_lowerCamelCase )},
                 {'''score''': ANY(_lowerCamelCase ), '''answer''': ANY(_lowerCamelCase ), '''start''': ANY(_lowerCamelCase ), '''end''': ANY(_lowerCamelCase )},
             ]
         ]
         * 3    ,     )





  @require_torch
  @require_detectrona
  @require_pytesseract
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline('''document-question-answering'''    ,     model='''hf-internal-testing/tiny-random-layoutlmv2''' )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''How many cats are there?'''

     lowerCAmelCase_			=      [
         {'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
         {'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
     ]
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     _lowerCamelCase )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     _lowerCamelCase )

     # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
     # Empty answer probably
     lowerCAmelCase_			=      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(_lowerCamelCase    ,     [] )

     # We can optionnally pass directly the words and bounding boxes
     lowerCAmelCase_			=      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
     lowerCAmelCase_			=      []
     lowerCAmelCase_			=      []
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     words=_lowerCamelCase    ,     boxes=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(_lowerCamelCase    ,     [] )





  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''    ,     revision='''9977165'''    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ],
         ]
         * 2    ,     )





  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''    ,     revision='''9977165'''    ,     max_seq_len=50    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]
         ]
         * 2    ,     )





  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      AutoTokenizer.from_pretrained(
         '''impira/layoutlm-document-qa'''    ,     revision='''3dc6de3'''    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''impira/layoutlm-document-qa'''    ,     tokenizer=_lowerCamelCase    ,     revision='''3dc6de3'''    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline({'''image''': image, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]
         ]
         * 2    ,     )

     lowerCAmelCase_			=      list(zip(*apply_tesseract(load_image(_lowerCamelCase )    ,     _lowerCamelCase    ,     '''''' ) ) )

     # This model should also work if `image` is set to None
     lowerCAmelCase_			=      dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
         ]    ,     )





  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      AutoTokenizer.from_pretrained(
         '''impira/layoutlm-document-qa'''    ,     revision='''3dc6de3'''    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''impira/layoutlm-document-qa'''    ,     tokenizer=_lowerCamelCase    ,     revision='''3dc6de3'''    ,     max_seq_len=50    ,     )
     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''

     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )

     lowerCAmelCase_			=      dqa_pipeline(
         [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             [
                 {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]
         ]
         * 2    ,     )

     lowerCAmelCase_			=      list(zip(*apply_tesseract(load_image(_lowerCamelCase )    ,     _lowerCamelCase    ,     '''''' ) ) )

     # This model should also work if `image` is set to None
     lowerCAmelCase_			=      dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}    ,     top_k=2 )
     self.assertEqual(
         nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [
             {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
         ]    ,     )





  @slow
  @require_torch
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      pipeline(
         '''document-question-answering'''    ,     model='''naver-clova-ix/donut-base-finetuned-docvqa'''    ,     tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' )    ,     feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa'''    ,     )

     lowerCAmelCase_			=      INVOICE_URL
     lowerCAmelCase_			=      '''What is the invoice number?'''
     lowerCAmelCase_			=      dqa_pipeline(image=_lowerCamelCase    ,     question=_lowerCamelCase    ,     top_k=2 )
     self.assertEqual(nested_simplify(_lowerCamelCase    ,     decimals=4 )    ,     [{'''answer''': '''us-001'''}] )







  @require_tf
  @unittest.skip('''Document question answering not implemented in TF''' )
  def 						UpperCAmelCase_	(	self ):
     pass





