"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BILINEAR     ,__UpperCamelCase = True     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	size if size is not None else {'shortest_edge': 384}
        lowercase_	:				Optional[int]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)

        lowercase_	:				Tuple													=	do_resize
        lowercase_	:				int													=	size
        # Default value set here for backwards compatibility where the value in config is None
        lowercase_	:				Optional[Any]													=	crop_pct if crop_pct is not None else 224 / 256
        lowercase_	:				Optional[int]													=	resample
        lowercase_	:				str													=	do_rescale
        lowercase_	:				Optional[int]													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Optional[Any]													=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowercase_	:				List[str]													=	image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        if "shortest_edge" not in size:
             raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''			)
        lowercase_	:				Union[str, Any]													=	size['shortest_edge']

        if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             lowercase_	:				Union[str, Any]													=	int(shortest_edge / crop_pct			)
             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
             lowercase_	:				Dict													=	resize(image=__UpperCamelCase     ,size=__UpperCamelCase     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=__UpperCamelCase     ,size=(shortest_edge, shortest_edge)     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
        else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 __UpperCamelCase     ,size=(shortest_edge, shortest_edge)     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      int:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        return normalize(__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = ChannelDimension.FIRST     ,**__UpperCamelCase     ,)	->      PIL.Image.Image:


        '''simple docstring'''


        lowercase_	:				Dict													=	do_resize if do_resize is not None else self.do_resize
        lowercase_	:				int													=	crop_pct if crop_pct is not None else self.crop_pct
        lowercase_	:				Tuple													=	resample if resample is not None else self.resample
        lowercase_	:				Optional[Any]													=	do_rescale if do_rescale is not None else self.do_rescale
        lowercase_	:				Union[str, Any]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_	:				Any													=	do_normalize if do_normalize is not None else self.do_normalize
        lowercase_	:				int													=	image_mean if image_mean is not None else self.image_mean
        lowercase_	:				Optional[Any]													=	image_std if image_std is not None else self.image_std

        lowercase_	:				Any													=	size if size is not None else self.size
        lowercase_	:				int													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)

        lowercase_	:				str													=	make_list_of_images(__UpperCamelCase			)

        if not valid_images(__UpperCamelCase			):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'			)

        if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'			)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('crop_pct must be specified if size < 384.'			)

        if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

        if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

        # All transformations expect numpy arrays.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

        if do_resize:
             lowercase_	:				Union[str, Any]													=	[self.resize(image=__UpperCamelCase     ,size=__UpperCamelCase     ,crop_pct=__UpperCamelCase     ,resample=__UpperCamelCase			) for image in images]

        if do_rescale:
             lowercase_	:				Dict													=	[self.rescale(image=__UpperCamelCase     ,scale=__UpperCamelCase			) for image in images]

        if do_normalize:
             lowercase_	:				Any													=	[self.normalize(image=__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase			) for image in images]

        lowercase_	:				Union[str, Any]													=	[to_channel_dimension_format(__UpperCamelCase     ,__UpperCamelCase			) for image in images]

        lowercase_	:				Any													=	{'pixel_values': images}
        return BatchFeature(data=__UpperCamelCase     ,tensor_type=__UpperCamelCase			)


"""simple docstring"""







from __future__ import annotations

__SCREAMING_SNAKE_CASE    	=[]



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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     if row >= len(__SCREAMING_SNAKE_CASE      ):
          solution.append(__SCREAMING_SNAKE_CASE      )
          printboard(__SCREAMING_SNAKE_CASE      )
          print()
          return True
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if is_safe(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				int													=	1
               solve(__SCREAMING_SNAKE_CASE     ,			row + 1      )
               lowercase_	:				Dict													=	0
     return False



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]      ):
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          for j in range(len(__SCREAMING_SNAKE_CASE      )      ):
               if board[i][j] == 1:
                    print('Q'     ,			end=' '      )
               else:
                    print('.'     ,			end=' '      )
          print()


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







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

