



'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Tuple									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   str									=		"ViTImageProcessor"
						UpperCAmelCase							:   List[str]									=		("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__( self     ,	A_=None     ,	A_=None     ,	**A_				)     ->     Any:
							lowerCAmelCase   =     None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead."""     ,	A_     ,	)
								lowerCAmelCase   =     kwargs.pop("""feature_extractor"""				)

							lowerCAmelCase   =     image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError("""You need to specify an `image_processor`."""				)
							if tokenizer is None:
								raise ValueError("""You need to specify a `tokenizer`."""				)

							super().__init__(A_     ,	A_				)


						def __call__( self     ,	A_=None     ,	A_=None     ,	A_=None     ,	A_=None     ,	**A_				)     ->     Union[str, Any]:
							if text is None and visual_prompt is None and images is None:
								raise ValueError("""You have to specify either text, visual prompt or images."""				)

							if text is not None and visual_prompt is not None:
								raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(A_     ,	return_tensors=A_     ,	**A_				)

							if visual_prompt is not None:
								lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_     ,	**A_				)

							if images is not None:
								lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_     ,	**A_				)

							if visual_prompt is not None and images is not None:
								lowerCAmelCase   =     {
								    """pixel_values""": image_features.pixel_values,
								    """conditional_pixel_values""": prompt_features.pixel_values,
								}
								return encoding
							elif text is not None and images is not None:
								lowerCAmelCase   =     image_features.pixel_values
								return encoding
							elif text is not None:
								return encoding
							elif visual_prompt is not None:
								lowerCAmelCase   =     {
								    """conditional_pixel_values""": prompt_features.pixel_values,
								}
								return encoding
							else:
								return BatchEncoding(data=dict(**A_				)     ,	tensor_type=A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Optional[int]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Dict:
							return self.tokenizer.decode(*A_     ,	**A_				)


						@property
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,	A_     ,	)
							return self.image_processor_class




						@property
						def     __snake_case   ( self				)     ->     int:
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,	A_     ,	)
							return self.image_processor




'''simple docstring'''

from __future__ import annotations

UpperCAmelCase     		=   []






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







	"""simple docstring"""
	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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







	"""simple docstring"""
	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							):
			lowerCAmelCase   =     1
			solve(_SCREAMING_SNAKE_CASE							,		row + 1							)
			lowerCAmelCase   =     0
	return False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							)      ->						None:







	"""simple docstring"""
	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"))
UpperCAmelCase     		=   8
UpperCAmelCase     		=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)





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