



'''simple docstring'''


import unittest

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 PoolFormerImageProcessor







class 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_=7							,  lowerCAmelCase_=3							,  lowerCAmelCase_=3_0							,  lowerCAmelCase_=4_0_0							,  lowerCAmelCase_=True							,  lowerCAmelCase_=None							,  lowerCAmelCase_=0.9							,  lowerCAmelCase_=None							,  lowerCAmelCase_=True							,  lowerCAmelCase_=[0.5, 0.5, 0.5]							,  lowerCAmelCase_=[0.5, 0.5, 0.5]							,  ) ->      Union[str, Any]:



      """simple docstring"""







      a_       		=size if size is not None else {"shortest_edge": 3_0}
      a_       		=crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
      a_       		=parent
      a_       		=batch_size
      a_       		=num_channels
      a_       		=min_resolution
      a_       		=max_resolution
      a_       		=do_resize_and_center_crop
      a_       		=size
      a_       		=crop_pct
      a_       		=crop_size
      a_       		=do_normalize
      a_       		=image_mean
      a_       		=image_std




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



      """simple docstring"""







      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }







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


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      PoolFormerImageProcessor if is_vision_available() else None




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      a_       		=PoolFormerImageProcessingTester(self)




 @property
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      return self.image_processor_tester.prepare_image_processor_dict()




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







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




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



      """simple docstring"""







      a_       		=self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size							,  {"shortest_edge": 3_0})
      self.assertEqual(image_processor.crop_size							,  {"height": 3_0, "width": 3_0})

      a_       		=self.image_processing_class.from_dict(self.image_processor_dict							,  size=4_2							,  crop_size=8_4)
      self.assertEqual(image_processor.size							,  {"shortest_edge": 4_2})
      self.assertEqual(image_processor.crop_size							,  {"height": 8_4, "width": 8_4})




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



      """simple docstring"""







      pass




 def       lowercase_  (   self) ->      str:



      """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

      self.assertEqual(
          encoded_images.shape							,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )							,  )

      # Test batched
      a_       		=image_processing(lowerCAmelCase_							,  return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape							,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )							,  )




 def       lowercase_  (   self) ->      str:



      """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
      self.assertEqual(
          encoded_images.shape							,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )							,  )

      # Test batched
      a_       		=image_processing(lowerCAmelCase_							,  return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape							,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )							,  )




 def       lowercase_  (   self) ->      str:



      """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
      self.assertEqual(
          encoded_images.shape							,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )							,  )

      # Test batched
      a_       		=image_processing(lowerCAmelCase_							,  return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_images.shape							,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["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))


