



'''simple docstring'''


import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor







class 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = 3_2							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = 1 / 2_5_5							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,  lowerCAmelCase_ = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,  lowerCAmelCase_ = True							,  lowerCAmelCase_=7							,  lowerCAmelCase_=3_0							,  lowerCAmelCase_=4_0_0							,  lowerCAmelCase_=3							,  ) ->      str:



      """simple docstring"""







      a_       		=parent
      a_       		=do_resize
      a_       		=size if size is not None else {"shortest_edge": 2_8_8}
      a_       		=size_divisor
      a_       		=do_rescale
      a_       		=rescale_factor
      a_       		=do_normalize
      a_       		=do_center_crop
      a_       		=image_mean
      a_       		=image_std
      a_       		=do_pad
      a_       		=batch_size
      a_       		=num_channels
      a_       		=min_resolution
      a_       		=max_resolution




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=False) ->      str:



      """simple docstring"""







      if not batched:
           a_       		=self.size["shortest_edge"]
           a_       		=image_inputs[0]
           if isinstance(lowerCAmelCase_							,  Image.Image):
                a_		,						a_       		=image.size
           else:
                a_		,						a_       		=image.shape[1], image.shape[2]
           a_       		=size / min(lowerCAmelCase_							,  lowerCAmelCase_)
           if h < w:
                a_		,						a_       		=size, scale * w
           else:
                a_		,						a_       		=scale * h, size

           a_       		=int((1_3_3_3 / 8_0_0) * size)
           if max(lowerCAmelCase_							,  lowerCAmelCase_) > max_size:
                a_       		=max_size / max(lowerCAmelCase_							,  lowerCAmelCase_)
                a_       		=newh * scale
                a_       		=neww * scale

           a_		,						a_       		=int(newh + 0.5), int(neww + 0.5)
           a_		,						a_       		=(
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

      else:
           a_       		=[]
           for image in image_inputs:
                a_		,						a_       		=self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
           a_       		=max(lowerCAmelCase_							,  key=lambda lowerCAmelCase_: item[0])[0]
           a_       		=max(lowerCAmelCase_							,  key=lambda lowerCAmelCase_: item[1])[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: Optional[Any]  =      BridgeTowerImageProcessor if is_vision_available() else None




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=BridgeTowerImageProcessingTester(self)




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      return self.image_processor_tester.prepare_image_processor_dict()




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		=self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowerCAmelCase_							,  "image_mean"))
      self.assertTrue(hasattr(lowerCAmelCase_							,  "image_std"))
      self.assertTrue(hasattr(lowerCAmelCase_							,  "do_normalize"))
      self.assertTrue(hasattr(lowerCAmelCase_							,  "do_resize"))
      self.assertTrue(hasattr(lowerCAmelCase_							,  "size"))
      self.assertTrue(hasattr(lowerCAmelCase_							,  "size_divisor"))




 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      pass




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      a_       		=prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowerCAmelCase_)
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_							,  Image.Image)

      # Test not batched input
      a_       		=image_processing(image_inputs[0]							,  return_tensors="pt").pixel_values

      a_		,						a_       		=self.image_processor_tester.get_expected_values(lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

      # Test batched
      a_       		=image_processing(lowerCAmelCase_							,  return_tensors="pt").pixel_values

      a_		,						a_       		=self.image_processor_tester.get_expected_values(lowerCAmelCase_							,  batched=lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape							,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,  )




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_       		=prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowerCAmelCase_							,  numpify=lowerCAmelCase_)
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_							,  np.ndarray)

      # Test not batched input
      a_       		=image_processing(image_inputs[0]							,  return_tensors="pt").pixel_values

      a_		,						a_       		=self.image_processor_tester.get_expected_values(lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

      # Test batched
      a_       		=image_processing(lowerCAmelCase_							,  return_tensors="pt").pixel_values

      a_		,						a_       		=self.image_processor_tester.get_expected_values(lowerCAmelCase_							,  batched=lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape							,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,  )




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_       		=prepare_image_inputs(self.image_processor_tester							,  equal_resolution=lowerCAmelCase_							,  torchify=lowerCAmelCase_)
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_							,  torch.Tensor)

      # Test not batched input
      a_       		=image_processing(image_inputs[0]							,  return_tensors="pt").pixel_values

      a_		,						a_       		=self.image_processor_tester.get_expected_values(lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

      # Test batched
      a_       		=image_processing(lowerCAmelCase_							,  return_tensors="pt").pixel_values

      a_		,						a_       		=self.image_processor_tester.get_expected_values(lowerCAmelCase_							,  batched=lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape							,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,  )







'''simple docstring'''


from __future__ import annotations

lowercase							       =			[]






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          if board[row][i] == 1:
               return False
     for i in range(len(lowercase__       )       ):
          if board[i][column] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	-1       ,	-1       )       ):
          if board[i][j] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	len(lowercase__       )       )       ):
          if board[i][j] == 1:
               return False
     return True






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if row >= len(lowercase__       ):
          solution.append(lowercase__       )
          printboard(lowercase__       )
          print()
          return True
     for i in range(len(lowercase__       )       ):
          if is_safe(lowercase__       ,	lowercase__       ,	lowercase__       ):
               a_       		=1
               solve(lowercase__       ,	row + 1       )
               a_       		=0
     return False






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          for j in range(len(lowercase__       )       ):
               if board[i][j] == 1:
                    print("Q"       ,	end=" "       )
               else:
                    print("."       ,	end=" "       )
          print()


# n=int(input("The no. of queens"))
lowercase							       =			8
lowercase							       =			[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))


