


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class     a		(nn.Module				):




							"""simple docstring"""


							def __init__(							self	:  int   ,     lowerCamelCase	:  int = 16   ,     lowerCamelCase	:  int = 88   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  int = 1   ,     lowerCamelCase	:  float = 0.0   ,     lowerCamelCase	:  int = 32   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  str = "geglu"   ,     lowerCamelCase	:  Optional[int] = None   ,     )     ->   int:
														super().__init__()
														__snake_case		:      Any										=       nn.ModuleList(
														    [
														        TransformeraDModel(
														            num_attention_heads=lowerCamelCase   ,     attention_head_dim=lowerCamelCase   ,     in_channels=lowerCamelCase   ,     num_layers=lowerCamelCase   ,     dropout=lowerCamelCase   ,     norm_num_groups=lowerCamelCase   ,     cross_attention_dim=lowerCamelCase   ,     attention_bias=lowerCamelCase   ,     sample_size=lowerCamelCase   ,     num_vector_embeds=lowerCamelCase   ,     activation_fn=lowerCamelCase   ,     num_embeds_ada_norm=lowerCamelCase   ,     )
														        for _ in range(2    )
														    ]    )

														# Variables that can be set by a pipeline:

														# The ratio of transformer1 to transformer2's output states to be combined during inference
														__snake_case		:      List[Any]										=       0.5

														# The shape of `encoder_hidden_states` is expected to be
														# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
														__snake_case		:      Optional[int]										=       [77, 257]

														# Which transformer to use to encode which condition.
														# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
														__snake_case		:      Optional[Any]										=       [1, 0]






							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  bool = True   ,     )     ->   List[str]:
														__snake_case		:      Union[str, Any]										=       hidden_states

														__snake_case		:      Dict										=       []
														__snake_case		:      Union[str, Any]										=       0
														# attention_mask is not used yet
														for i in range(2    ):
																					# for each of the two transformers, pass the corresponding condition tokens
																					__snake_case		:      Optional[int]										=       encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																					__snake_case		:      str										=       self.transformer_index_for_condition[i]
																					__snake_case		:      str										=       self.transformers[transformer_index](
																					    lowerCamelCase   ,     encoder_hidden_states=lowerCamelCase   ,     timestep=lowerCamelCase   ,     cross_attention_kwargs=lowerCamelCase   ,     return_dict=lowerCamelCase   ,     )[0]
																					encoded_states.append(encoded_state - input_states    )
																					tokens_start += self.condition_lengths[i]

														__snake_case		:      int										=       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
														__snake_case		:      List[str]										=       output_states + input_states

														if not return_dict:
																					return (output_states,)

														return TransformeraDModelOutput(sample=lowerCamelCase    )





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







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


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

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

