




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

UpperCAmelCase_									=	logging.get_logger(__name__)




def 					lowerCamelCase__       (						A__       :					List[Any]	):





       '''simple docstring'''



       if isinstance(A__  ,			(list, tuple)	) and isinstance(videos[0]  ,			(list, tuple)	) and is_valid_image(videos[0][0]	):
              return videos

       elif isinstance(A__  ,			(list, tuple)	) and is_valid_image(videos[0]	):
              return [videos]

       elif is_valid_image(A__	):
              return [[videos]]

       raise ValueError(f'Could not make batched video from {videos}'	)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Optional[Any]    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       **UpperCamelCase_: Dict    ,       ):
             super().__init__(**UpperCamelCase_   )
             __lowerCamelCase   =						size if size is not None else {"""shortest_edge""": 2_56}
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       default_to_square=UpperCamelCase_   )
             __lowerCamelCase   =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       param_name="""crop_size"""   )

             __lowerCamelCase   =						do_resize
             __lowerCamelCase   =						size
             __lowerCamelCase   =						do_center_crop
             __lowerCamelCase   =						crop_size
             __lowerCamelCase   =						resample
             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						offset
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             __lowerCamelCase   =						image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Dict[str, int]    ,       UpperCamelCase_: PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: str    ,       ):
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       default_to_square=UpperCamelCase_   )
             if "shortest_edge" in size:
                    __lowerCamelCase   =						get_resize_output_image_size(UpperCamelCase_    ,       size["""shortest_edge"""]    ,       default_to_square=UpperCamelCase_   )
             elif "height" in size and "width" in size:
                    __lowerCamelCase   =						(size["""height"""], size["""width"""])
             else:
                    raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'   )
             return resize(UpperCamelCase_    ,       size=UpperCamelCase_    ,       resample=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Dict[str, int]    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: List[Any]    ,       ):
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_   )
             if "height" not in size or "width" not in size:
                    raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'   )
             return center_crop(UpperCamelCase_    ,       size=(size["""height"""], size["""width"""])    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Union[int, float]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Union[str, Any]    ,       ):
             __lowerCamelCase   =						image.astype(np.floataa   )
             if offset:
                    __lowerCamelCase   =						image - (scale / 2)
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Union[float, List[float]]    ,       UpperCamelCase_: Union[float, List[float]]    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Optional[int]    ,       ):
             return normalize(UpperCamelCase_    ,       mean=UpperCamelCase_    ,       std=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: PILImageResampling = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: float = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[ChannelDimension] = ChannelDimension.FIRST    ,       ):
             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_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."""   )

             if offset and not do_rescale:
                    raise ValueError("""For offset, do_rescale must also be set to True."""   )

             # All transformations expect numpy arrays.
             __lowerCamelCase   =						to_numpy_array(UpperCamelCase_   )

             if do_resize:
                    __lowerCamelCase   =						self.resize(image=UpperCamelCase_    ,       size=UpperCamelCase_    ,       resample=UpperCamelCase_   )

             if do_center_crop:
                    __lowerCamelCase   =						self.center_crop(UpperCamelCase_    ,       size=UpperCamelCase_   )

             if do_rescale:
                    __lowerCamelCase   =						self.rescale(image=UpperCamelCase_    ,       scale=UpperCamelCase_    ,       offset=UpperCamelCase_   )

             if do_normalize:
                    __lowerCamelCase   =						self.normalize(image=UpperCamelCase_    ,       mean=UpperCamelCase_    ,       std=UpperCamelCase_   )

             __lowerCamelCase   =						to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   )
             return image




      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: PILImageResampling = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: float = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: ChannelDimension = ChannelDimension.FIRST    ,       **UpperCamelCase_: str    ,       ):
             __lowerCamelCase   =						do_resize if do_resize is not None else self.do_resize
             __lowerCamelCase   =						resample if resample is not None else self.resample
             __lowerCamelCase   =						do_center_crop if do_center_crop is not None else self.do_center_crop
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						offset if offset is not None else self.offset
             __lowerCamelCase   =						do_normalize if do_normalize is not None else self.do_normalize
             __lowerCamelCase   =						image_mean if image_mean is not None else self.image_mean
             __lowerCamelCase   =						image_std if image_std is not None else self.image_std

             __lowerCamelCase   =						size if size is not None else self.size
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       default_to_square=UpperCamelCase_   )
             __lowerCamelCase   =						crop_size if crop_size is not None else self.crop_size
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       param_name="""crop_size"""   )

             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."""   )

             __lowerCamelCase   =						make_batched(UpperCamelCase_   )

             __lowerCamelCase   =						[
                 [
                     self._preprocess_image(
                         image=UpperCamelCase_    ,       do_resize=UpperCamelCase_    ,       size=UpperCamelCase_    ,       resample=UpperCamelCase_    ,       do_center_crop=UpperCamelCase_    ,       crop_size=UpperCamelCase_    ,       do_rescale=UpperCamelCase_    ,       rescale_factor=UpperCamelCase_    ,       offset=UpperCamelCase_    ,       do_normalize=UpperCamelCase_    ,       image_mean=UpperCamelCase_    ,       image_std=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       )
                     for img in video
                 ]
                 for video in videos
             ]

             __lowerCamelCase   =						{"""pixel_values""": videos}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )







from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[list[str]]  ,			A__       :					int  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)

       # If row is equal to the size of the board it means there are a queen in each row in
       # the current board (possible_board)
       if row == n:
              # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
              # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
              boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]	)
              return

       # We iterate each column in the row to find all possible results in each row
       for col in range(A__	):
              # We apply that we learned previously. First we check that in the current board
              # (possible_board) there are not other same value because if there is it means
              # that there are a collision in vertical. Then we apply the two formulas we
              # learned before:
              #
              # 45º: y - x = b or 45: row - col = b
              # 135º: y + x = b or row + col = b.
              #
              # And we verify if the results of this two formulas not exist in their variables
              # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
              #
              # If any or these are True it means there is a collision so we continue to the
              # next value in the for loop.
              if (
                  col in possible_board
                  or row - col in diagonal_right_collisions
                  or row + col in diagonal_left_collisions
              ):
                     continue

              # If it is False we call dfs function again and we update the inputs
              depth_first_search(
                  [*possible_board, col]  ,			[*diagonal_right_collisions, row - col]  ,			[*diagonal_left_collisions, row + col]  ,			A__  ,			A__  ,			)




def 					lowerCamelCase__       (						A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       depth_first_search([]  ,			[]  ,			[]  ,			A__  ,			A__	)

       # Print all the boards
       for board in boards:
              for column in board:
                     print(A__	)
              print(""""""	)

       print(len(A__	)  ,			"""solutions were found."""	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       n_queens_solution(4)

