


'''simple docstring'''



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin





@dataclass
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   torch.FloatTensor







class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase    ):





    """simple docstring"""






    @register_to_config
    def __init__(				self     ,	SCREAMING_SNAKE_CASE_ = 16     ,	SCREAMING_SNAKE_CASE_ = 88     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 32     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "geglu"     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Optional[Any]:
         super().__init__()
         __lowerCamelCase  :      int														=						num_attention_heads
         __lowerCamelCase  :      str														=						attention_head_dim
         __lowerCamelCase  :      Optional[int]														=						num_attention_heads * attention_head_dim

         __lowerCamelCase  :      Tuple														=						in_channels

         __lowerCamelCase  :      List[str]														=						torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE_     ,	num_channels=SCREAMING_SNAKE_CASE_     ,	eps=1E-6     ,	affine=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						nn.Linear(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         # 3. Define transformers blocks
         __lowerCamelCase  :      List[str]														=						nn.ModuleList(
             [
                 BasicTransformerBlock(
                     SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	dropout=SCREAMING_SNAKE_CASE_     ,	cross_attention_dim=SCREAMING_SNAKE_CASE_     ,	activation_fn=SCREAMING_SNAKE_CASE_     ,	attention_bias=SCREAMING_SNAKE_CASE_     ,	double_self_attention=SCREAMING_SNAKE_CASE_     ,	norm_elementwise_affine=SCREAMING_SNAKE_CASE_     ,	)
                 for d in range(SCREAMING_SNAKE_CASE_      )
             ]      )

         __lowerCamelCase  :      Union[str, Any]														=						nn.Linear(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Union[str, Any]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Dict														=						hidden_states.shape
         __lowerCamelCase  :      Dict														=						batch_frames // num_frames

         __lowerCamelCase  :      Tuple														=						hidden_states

         __lowerCamelCase  :      Any														=						hidden_states[None, :].reshape(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						hidden_states.permute(0     ,	2     ,	1     ,	3     ,	4      )

         __lowerCamelCase  :      List[str]														=						self.norm(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						hidden_states.permute(0     ,	3     ,	4     ,	2     ,	1      ).reshape(batch_size * height * width     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						self.proj_in(SCREAMING_SNAKE_CASE_      )

         # 2. Blocks
         for block in self.transformer_blocks:
              __lowerCamelCase  :      List[Any]														=						block(
                  SCREAMING_SNAKE_CASE_     ,	encoder_hidden_states=SCREAMING_SNAKE_CASE_     ,	timestep=SCREAMING_SNAKE_CASE_     ,	cross_attention_kwargs=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_     ,	)

         # 3. Output
         __lowerCamelCase  :      Dict														=						self.proj_out(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						(
             hidden_states[None, None, :]
             .reshape(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
             .permute(0     ,	3     ,	4     ,	1     ,	2      )
             .contiguous()
         )
         __lowerCamelCase  :      List[Any]														=						hidden_states.reshape(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						hidden_states + residual

         if not return_dict:
              return (output,)

         return TransformerTemporalModelOutput(sample=SCREAMING_SNAKE_CASE_      )














'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






