'''simple docstring'''





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE :	List[Any]							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 def constraint_to_multiple_of(_A							,				_A							,				_A=0							,				_A=None					):
  __a       =  round(val / multiple					) * multiple

  if max_val is not None and x > max_val:
   __a       =  math.floor(val / multiple					) * multiple

  if x < min_val:
   __a       =  math.ceil(val / multiple					) * multiple

  return x

 __a       =  (output_size, output_size) if isinstance(_A							,				_A					) else output_size

 __a       ,     __a       =  get_image_size(_A					)
 __a       ,     __a       =  output_size

 # determine new height and width
 __a       =  output_height / input_height
 __a       =  output_width / input_width

 if keep_aspect_ratio:
  # scale as little as possible
  if abs(1 - scale_width					) < abs(1 - scale_height					):
   # fit width
   __a       =  scale_width
  else:
   # fit height
   __a       =  scale_height

 __a       =  constraint_to_multiple_of(scale_height * input_height							,				multiple=_A					)
 __a       =  constraint_to_multiple_of(scale_width * input_width							,				multiple=_A					)

 return (new_height, new_width)






class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""pixel_values"""]

  def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BILINEAR     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Union[int, float] = 1 / 2_55     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							**__SCREAMING_SNAKE_CASE :      List[Any]     ,							):
   super().__init__(**__SCREAMING_SNAKE_CASE       )
   __a       =  size if size is not None else {"height": 3_84, "width": 3_84}
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   __a       =  do_resize
   __a       =  size
   __a       =  keep_aspect_ratio
   __a       =  ensure_multiple_of
   __a       =  resample
   __a       =  do_rescale
   __a       =  rescale_factor
   __a       =  do_normalize
   __a       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __a       =  image_std if image_std is not None else IMAGENET_STANDARD_STD

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int]     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BICUBIC     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]     ,							):
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""       )
   __a       =  get_resize_output_image_size(
       __SCREAMING_SNAKE_CASE     ,							output_size=(size["height"], size["width"])     ,							keep_aspect_ratio=__SCREAMING_SNAKE_CASE     ,							multiple=__SCREAMING_SNAKE_CASE     ,							)
   return resize(__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							resample=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

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

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

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      ImageInput     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      float = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							__SCREAMING_SNAKE_CASE :      ChannelDimension = ChannelDimension.FIRST     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
   __a       =  do_resize if do_resize is not None else self.do_resize
   __a       =  size if size is not None else self.size
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   __a       =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
   __a       =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
   __a       =  resample if resample is not None else self.resample
   __a       =  do_rescale if do_rescale is not None else self.do_rescale
   __a       =  rescale_factor if rescale_factor is not None else self.rescale_factor
   __a       =  do_normalize if do_normalize is not None else self.do_normalize
   __a       =  image_mean if image_mean is not None else self.image_mean
   __a       =  image_std if image_std is not None else self.image_std

   __a       =  make_list_of_images(__SCREAMING_SNAKE_CASE       )

   if not valid_images(__SCREAMING_SNAKE_CASE       ):
    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_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.
   __a       =  [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_resize:
    __a       =  [self.resize(image=__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							resample=__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_rescale:
    __a       =  [self.rescale(image=__SCREAMING_SNAKE_CASE     ,							scale=__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_normalize:
    __a       =  [self.normalize(image=__SCREAMING_SNAKE_CASE     ,							mean=__SCREAMING_SNAKE_CASE     ,							std=__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  {"pixel_values": images}
   return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,							tensor_type=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Tuple] = None       ):
   __a       =  outputs.logits

   # Resize logits and compute semantic segmentation maps
   if target_sizes is not None:
    if len(__SCREAMING_SNAKE_CASE       ) != len(__SCREAMING_SNAKE_CASE       ):
     raise ValueError(
         "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

    if is_torch_tensor(__SCREAMING_SNAKE_CASE       ):
     __a       =  target_sizes.numpy()

    __a       =  []

    for idx in range(len(__SCREAMING_SNAKE_CASE       )       ):
     __a       =  torch.nn.functional.interpolate(
         logits[idx].unsqueeze(dim=0       )     ,							size=target_sizes[idx]     ,							mode="bilinear"     ,							align_corners=__SCREAMING_SNAKE_CASE       )
     __a       =  resized_logits[0].argmax(dim=0       )
     semantic_segmentation.append(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  logits.argmax(dim=1       )
    __a       =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

   return semantic_segmentation



from __future__ import annotations

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[]



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A					):



 """simple docstring"""


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


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






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


