






"""simple docstring"""



from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		UpperCamelCase		):


							@register_to_config
							def __init__(			self      :						int			,    snake_case      :						int = 32			,    snake_case      :						int = 64			,    snake_case      :						int = 20			,    snake_case      :						int = 768			,    snake_case      :						int=77			,    snake_case      :						Tuple=4			,    snake_case      :						float = 0.0			,    snake_case      :						str = "silu"			,    snake_case      :						Optional[str] = None			,    snake_case      :						Optional[str] = None			,    snake_case      :						Optional[str] = "linear"			,    snake_case      :						Optional[str] = "prd"			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[int] = None			,    ):
													'''simple docstring'''





													super().__init__()
													A__						:  Tuple		  =  num_attention_heads
													A__						:  Dict		  =  attention_head_dim
													A__						:  str		  =  num_attention_heads * attention_head_dim
													A__						:  Optional[int]		  =  additional_embeddings

													A__						:  Union[str, Any]		  =  time_embed_dim or inner_dim
													A__						:  str		  =  embedding_proj_dim or embedding_dim
													A__						:  Tuple		  =  clip_embed_dim or embedding_dim

													A__						:  int		  =  Timesteps(snake_case			,    snake_case			,    0							)
													A__						:  str		  =  TimestepEmbedding(snake_case			,    snake_case			,    out_dim=snake_case			,    act_fn=snake_case							)

													A__						:  List[str]		  =  nn.Linear(snake_case			,    snake_case							)

													if embedding_proj_norm_type is None:
																			A__						:  Optional[Any]		  =  None
													elif embedding_proj_norm_type == "layer":
																			A__						:  Tuple		  =  nn.LayerNorm(snake_case							)
													else:
																			raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'							)

													A__						:  List[Any]		  =  nn.Linear(snake_case			,    snake_case							)

													if encoder_hid_proj_type is None:
																			A__						:  int		  =  None
													elif encoder_hid_proj_type == "linear":
																			A__						:  str		  =  nn.Linear(snake_case			,    snake_case							)
													else:
																			raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'							)

													A__						:  Tuple		  =  nn.Parameter(torch.zeros(1			,    num_embeddings + additional_embeddings			,    snake_case							)							)

													if added_emb_type == "prd":
																			A__						:  List[str]		  =  nn.Parameter(torch.zeros(1			,    1			,    snake_case							)							)
													elif added_emb_type is None:
																			A__						:  int		  =  None
													else:
																			raise ValueError(
																			    F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'							)

													A__						:  Union[str, Any]		  =  nn.ModuleList(
													    [
													        BasicTransformerBlock(
													            snake_case			,    snake_case			,    snake_case			,    dropout=snake_case			,    activation_fn="""gelu"""			,    attention_bias=snake_case			,    )
													        for d in range(snake_case							)
													    ]							)

													if norm_in_type == "layer":
																			A__						:  Optional[Any]		  =  nn.LayerNorm(snake_case							)
													elif norm_in_type is None:
																			A__						:  Union[str, Any]		  =  None
													else:
																			raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'							)

													A__						:  Optional[Any]		  =  nn.LayerNorm(snake_case							)

													A__						:  str		  =  nn.Linear(snake_case			,    snake_case							)

													A__						:  str		  =  torch.full(
													    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			,    -10000.0							)
													causal_attention_mask.triu_(1							)
													A__						:  Union[str, Any]		  =  causal_attention_mask[None, ...]
													self.register_buffer("""causal_attention_mask"""			,    snake_case			,    persistent=snake_case							)

													A__						:  Any		  =  nn.Parameter(torch.zeros(1			,    snake_case							)							)
													A__						:  Optional[int]		  =  nn.Parameter(torch.zeros(1			,    snake_case							)							)


							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  str		  =  {}

													def fn_recursive_add_processors(snake_case      :						str			,    snake_case      :						torch.nn.Module			,    snake_case      :						Dict[str, AttentionProcessor]							):
																			if hasattr(snake_case			,    """set_processor"""							):
																									A__						:  Union[str, Any]		  =  module.processor

																			for sub_name, child in module.named_children():
																									fn_recursive_add_processors(F'{name}.{sub_name}'			,    snake_case			,    snake_case							)

																			return processors

													for name, module in self.named_children():
																			fn_recursive_add_processors(snake_case			,    snake_case			,    snake_case							)

													return processors


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





													A__						:  str		  =  len(self.attn_processors.keys()							)

													if isinstance(snake_case			,    snake_case							) and len(snake_case							) != count:
																			raise ValueError(
																			    F'A dict of processors was passed, but the number of processors {len(snake_case							)} does not match the'
																			    F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'							)

													def fn_recursive_attn_processor(snake_case      :						str			,    snake_case      :						torch.nn.Module			,    snake_case      :						List[str]							):
																			if hasattr(snake_case			,    """set_processor"""							):
																									if not isinstance(snake_case			,    snake_case							):
																															module.set_processor(snake_case							)
																									else:
																															module.set_processor(processor.pop(F'{name}.processor'							)							)

																			for sub_name, child in module.named_children():
																									fn_recursive_attn_processor(F'{name}.{sub_name}'			,    snake_case			,    snake_case							)

													for name, module in self.named_children():
																			fn_recursive_attn_processor(snake_case			,    snake_case			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													self.set_attn_processor(AttnProcessor()							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						Union[torch.Tensor, float, int]			,    snake_case      :						torch.FloatTensor			,    snake_case      :						Optional[torch.FloatTensor] = None			,    snake_case      :						Optional[torch.BoolTensor] = None			,    snake_case      :						bool = True			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  hidden_states.shape[0]

													A__						:  List[Any]		  =  timestep
													if not torch.is_tensor(snake_case							):
																			A__						:  Dict		  =  torch.tensor([timesteps]			,    dtype=torch.long			,    device=hidden_states.device							)
													elif torch.is_tensor(snake_case							) and len(timesteps.shape							) == 0:
																			A__						:  List[str]		  =  timesteps[None].to(hidden_states.device							)

													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													A__						:  Any		  =  timesteps * torch.ones(snake_case			,    dtype=timesteps.dtype			,    device=timesteps.device							)

													A__						:  Dict		  =  self.time_proj(snake_case							)

													# timesteps does not contain any weights and will always return f32 tensors
													# but time_embedding might be fp16, so we need to cast here.
													A__						:  Any		  =  timesteps_projected.to(dtype=self.dtype							)
													A__						:  Union[str, Any]		  =  self.time_embedding(snake_case							)

													if self.embedding_proj_norm is not None:
																			A__						:  Any		  =  self.embedding_proj_norm(snake_case							)

													A__						:  Dict		  =  self.embedding_proj(snake_case							)
													if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																			A__						:  List[Any]		  =  self.encoder_hidden_states_proj(snake_case							)
													elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																			raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"""							)

													A__						:  Optional[Any]		  =  self.proj_in(snake_case							)

													A__						:  Union[str, Any]		  =  self.positional_embedding.to(hidden_states.dtype							)

													A__						:  Tuple		  =  []
													A__						:  Tuple		  =  0

													if encoder_hidden_states is not None:
																			additional_embeds.append(snake_case							)
																			additional_embeddings_len += encoder_hidden_states.shape[1]

													if len(proj_embeddings.shape							) == 2:
																			A__						:  Optional[Any]		  =  proj_embeddings[:, None, :]

													if len(hidden_states.shape							) == 2:
																			A__						:  List[str]		  =  hidden_states[:, None, :]

													A__						:  Optional[Any]		  =  additional_embeds + [
													    proj_embeddings,
													    time_embeddings[:, None, :],
													    hidden_states,
													]

													if self.prd_embedding is not None:
																			A__						:  Dict		  =  self.prd_embedding.to(hidden_states.dtype							).expand(snake_case			,    -1			,    -1							)
																			additional_embeds.append(snake_case							)

													A__						:  Dict		  =  torch.cat(
													    snake_case			,    dim=1			,    )

													# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
													A__						:  List[str]		  =  additional_embeddings_len + proj_embeddings.shape[1] + 1
													if positional_embeddings.shape[1] < hidden_states.shape[1]:
																			A__						:  Any		  =  F.pad(
																			    snake_case			,    (
																			        0,
																			        0,
																			        additional_embeddings_len,
																			        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																			    )			,    value=0.0			,    )

													A__						:  List[str]		  =  hidden_states + positional_embeddings

													if attention_mask is not None:
																			A__						:  List[str]		  =  (1 - attention_mask.to(hidden_states.dtype							)) * -10000.0
																			A__						:  Optional[Any]		  =  F.pad(snake_case			,    (0, self.additional_embeddings)			,    value=0.0							)
																			A__						:  Union[str, Any]		  =  (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
																			A__						:  Optional[int]		  =  attention_mask.repeat_interleave(self.config.num_attention_heads			,    dim=0							)

													if self.norm_in is not None:
																			A__						:  Optional[Any]		  =  self.norm_in(snake_case							)

													for block in self.transformer_blocks:
																			A__						:  Any		  =  block(snake_case			,    attention_mask=snake_case							)

													A__						:  List[Any]		  =  self.norm_out(snake_case							)

													if self.prd_embedding is not None:
																			A__						:  Union[str, Any]		  =  hidden_states[:, -1]
													else:
																			A__						:  Tuple		  =  hidden_states[:, additional_embeddings_len:]

													A__						:  List[str]		  =  self.proj_to_clip_embeddings(snake_case							)

													if not return_dict:
																			return (predicted_image_embedding,)

													return PriorTransformerOutput(predicted_image_embedding=snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						List[str]							):
													'''simple docstring'''





													A__						:  List[str]		  =  (prior_latents * self.clip_std) + self.clip_mean
													return prior_latents














"""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))






