






"""simple docstring"""



from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		UpperCamelCase		):
							snake_case_     =							'pixel_values'
							snake_case_     =							False
							snake_case_     =							TimmBackboneConfig


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						List[str]			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													requires_backends(self			,    """timm"""							)
													super().__init__(snake_case							)
													A__						:  Optional[int]		  =  config

													if config.backbone is None:
																			raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""							)

													if config.backbone not in timm.list_models():
																			raise ValueError(F'backbone {config.backbone} is not supported by timm.'							)

													if hasattr(snake_case			,    """out_features"""							) and config.out_features is not None:
																			raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""							)

													A__						:  Optional[Any]		  =  getattr(snake_case			,    """use_pretrained_backbone"""			,    snake_case							)
													if pretrained is None:
																			raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""							)

													# We just take the final layer by default. This matches the default for the transformers models.
													A__						:  str		  =  config.out_indices if getattr(snake_case			,    """out_indices"""			,    snake_case							) is not None else (-1,)

													A__						:  int		  =  timm.create_model(
													    config.backbone			,    pretrained=snake_case			,    features_only=config.features_only			,    in_chans=config.num_channels			,    out_indices=snake_case			,    **snake_case			,    )
													# These are used to control the output of the model when called. If output_hidden_states is True, then
													# return_layers is modified to include all layers.
													A__						:  Optional[int]		  =  self._backbone.return_layers
													A__						:  int		  =  {layer["""module"""]: str(snake_case							) for i, layer in enumerate(self._backbone.feature_info.info							)}
													super()._init_backbone(snake_case							)


							@classmethod
							def 						_UpperCamelCase       (			cls      :						Any			,    snake_case      :						Union[str, Any]			,    *snake_case      :						Tuple			,    **snake_case      :						Optional[int]							):
													'''simple docstring'''





													requires_backends(cls			,    ["""vision""", """timm"""]							)
													from ...models.timm_backbone import TimmBackboneConfig

													A__						:  Optional[Any]		  =  kwargs.pop("""config"""			,    TimmBackboneConfig()							)

													A__						:  Dict		  =  kwargs.pop("""use_timm_backbone"""			,    snake_case							)
													if not use_timm:
																			raise ValueError("""use_timm_backbone must be True for timm backbones"""							)

													A__						:  Union[str, Any]		  =  kwargs.pop("""num_channels"""			,    config.num_channels							)
													A__						:  Optional[int]		  =  kwargs.pop("""features_only"""			,    config.features_only							)
													A__						:  int		  =  kwargs.pop("""use_pretrained_backbone"""			,    config.use_pretrained_backbone							)
													A__						:  int		  =  kwargs.pop("""out_indices"""			,    config.out_indices							)
													A__						:  List[Any]		  =  TimmBackboneConfig(
													    backbone=snake_case			,    num_channels=snake_case			,    features_only=snake_case			,    use_pretrained_backbone=snake_case			,    out_indices=snake_case			,    )
													return super()._from_config(snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						Dict=None			,    snake_case      :						str=None			,    snake_case      :						Union[str, Any]=None			,    **snake_case      :						str							):
													'''simple docstring'''





													A__						:  Dict		  =  return_dict if return_dict is not None else self.config.use_return_dict
													A__						:  Optional[int]		  =  (
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													A__						:  List[Any]		  =  output_attentions if output_attentions is not None else self.config.output_attentions

													if output_attentions:
																			raise ValueError("""Cannot output attentions for timm backbones at the moment"""							)

													if output_hidden_states:
																			# We modify the return layers to include all the stages of the backbone
																			A__						:  int		  =  self._all_layers
																			A__						:  Dict		  =  self._backbone(snake_case			,    **snake_case							)
																			A__						:  int		  =  self._return_layers
																			A__						:  Optional[Any]		  =  tuple(hidden_states[i] for i in self.out_indices							)
													else:
																			A__						:  Optional[int]		  =  self._backbone(snake_case			,    **snake_case							)
																			A__						:  Union[str, Any]		  =  None

													A__						:  int		  =  tuple(snake_case							)
													A__						:  int		  =  tuple(snake_case							) if hidden_states is not None else None

													if not return_dict:
																			A__						:  Optional[Any]		  =  (feature_maps,)
																			if output_hidden_states:
																									A__						:  int		  =  output + (hidden_states,)
																			return output

													return BackboneOutput(feature_maps=snake_case			,    hidden_states=snake_case			,    attentions=snake_case							)














"""simple docstring"""



from __future__ import annotations

A_    =							[]




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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int      )		->bool:
						if row >= len(UpperCAmelCase__      ):
												solution.append(UpperCAmelCase__      )
												printboard(UpperCAmelCase__      )
												print()
												return True
						for i in range(len(UpperCAmelCase__      )      ):
												if is_safe(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      ):
																		A__						:  Tuple		  =  1
																		solve(UpperCAmelCase__,						row + 1      )
																		A__						:  Optional[int]		  =  0
						return False




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]]      )		->None:
						for i in range(len(UpperCAmelCase__      )      ):
												for j in range(len(UpperCAmelCase__      )      ):
																		if board[i][j] == 1:
																								print("""Q""",						end=""" """      )
																		else:
																								print(""".""",						end=""" """      )
												print()


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

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






