


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_snake_case					:		Tuple      =    logging.get_logger(__name__)


if is_vision_available():
			import PIL

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  ["pixel_values"]

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  PILImageResampling = PILImageResampling.BICUBIC   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[int, float] = 1 / 255   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  bool = True   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   None:
														super().__init__(**lowerCamelCase    )
														__snake_case		:      Optional[int]										=       size if size is not None else {"shortest_edge": 224}
														__snake_case		:      Dict										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )
														__snake_case		:      Tuple										=       crop_size if crop_size is not None else {"height": 224, "width": 224}
														__snake_case		:      int										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase   ,     param_name="crop_size"    )

														__snake_case		:      Tuple										=       do_resize
														__snake_case		:      Union[str, Any]										=       size
														__snake_case		:      str										=       resample
														__snake_case		:      List[Any]										=       do_center_crop
														__snake_case		:      str										=       crop_size
														__snake_case		:      Optional[int]										=       do_rescale
														__snake_case		:      List[Any]										=       rescale_factor
														__snake_case		:      List[str]										=       do_normalize
														__snake_case		:      Optional[Any]										=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														__snake_case		:      Tuple										=       image_std if image_std is not None else OPENAI_CLIP_STD
														__snake_case		:      List[Any]										=       do_convert_rgb

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Dict[str, int]   ,     lowerCamelCase	:  PILImageResampling = PILImageResampling.BICUBIC   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Dict   ,     )     ->   np.ndarray:
														__snake_case		:      Dict										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'    )
														__snake_case		:      Optional[int]										=       get_resize_output_image_size(lowerCamelCase   ,     size=size["shortest_edge"]   ,     default_to_square=lowerCamelCase    )
														return resize(lowerCamelCase   ,     size=lowerCamelCase   ,     resample=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Dict[str, int]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Optional[int]   ,     )     ->   np.ndarray:
														__snake_case		:      str										=       get_size_dict(lowerCamelCase    )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
														return center_crop(lowerCamelCase   ,     size=(size["height"], size["width"])   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Union[int, float]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Tuple   ,     )     ->   Optional[Any]:
														return rescale(lowerCamelCase   ,     scale=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Union[float, List[float]]   ,     lowerCamelCase	:  Union[float, List[float]]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  List[str]   ,     )     ->   np.ndarray:
														return normalize(lowerCamelCase   ,     mean=lowerCamelCase   ,     std=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )






							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  ImageInput   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  PILImageResampling = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  int = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  float = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     lowerCamelCase	:  Optional[ChannelDimension] = ChannelDimension.FIRST   ,     **lowerCamelCase	:  List[str]   ,     )     ->   PIL.Image.Image:
														__snake_case		:      Any										=       do_resize if do_resize is not None else self.do_resize
														__snake_case		:      Optional[int]										=       size if size is not None else self.size
														__snake_case		:      List[Any]										=       get_size_dict(lowerCamelCase   ,     param_name="size"   ,     default_to_square=lowerCamelCase    )
														__snake_case		:      str										=       resample if resample is not None else self.resample
														__snake_case		:      List[Any]										=       do_center_crop if do_center_crop is not None else self.do_center_crop
														__snake_case		:      Tuple										=       crop_size if crop_size is not None else self.crop_size
														__snake_case		:      List[str]										=       get_size_dict(lowerCamelCase   ,     param_name="crop_size"   ,     default_to_square=lowerCamelCase    )
														__snake_case		:      List[Any]										=       do_rescale if do_rescale is not None else self.do_rescale
														__snake_case		:      Union[str, Any]										=       rescale_factor if rescale_factor is not None else self.rescale_factor
														__snake_case		:      List[str]										=       do_normalize if do_normalize is not None else self.do_normalize
														__snake_case		:      Tuple										=       image_mean if image_mean is not None else self.image_mean
														__snake_case		:      Dict										=       image_std if image_std is not None else self.image_std
														__snake_case		:      Tuple										=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														__snake_case		:      Any										=       make_list_of_images(lowerCamelCase    )

														if not valid_images(lowerCamelCase    ):
																					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:
																					raise ValueError("Size must be specified if do_resize is True."    )

														if do_center_crop and crop_size is None:
																					raise ValueError("Crop size must be specified if do_center_crop is True."    )

														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."    )

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					__snake_case		:      Any										=       [convert_to_rgb(lowerCamelCase    ) for image in images]

														# All transformations expect numpy arrays.
														__snake_case		:      Optional[int]										=       [to_numpy_array(lowerCamelCase    ) for image in images]

														if do_resize:
																					__snake_case		:      str										=       [self.resize(image=lowerCamelCase   ,     size=lowerCamelCase   ,     resample=lowerCamelCase    ) for image in images]

														if do_center_crop:
																					__snake_case		:      List[Any]										=       [self.center_crop(image=lowerCamelCase   ,     size=lowerCamelCase    ) for image in images]

														if do_rescale:
																					__snake_case		:      str										=       [self.rescale(image=lowerCamelCase   ,     scale=lowerCamelCase    ) for image in images]

														if do_normalize:
																					__snake_case		:      List[Any]										=       [self.normalize(image=lowerCamelCase   ,     mean=lowerCamelCase   ,     std=lowerCamelCase    ) for image in images]

														__snake_case		:      int										=       [to_channel_dimension_format(lowerCamelCase   ,     lowerCamelCase    ) for image in images]

														__snake_case		:      Tuple										=       {"pixel_values": images}
														return BatchFeature(data=lowerCamelCase   ,     tensor_type=lowerCamelCase    )





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







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


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

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

