



'''simple docstring'''


from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)







@flax.struct.dataclass
class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: jnp.ndarray
 __magic_name__					: jnp.ndarray







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 __magic_name__					: int
 __magic_name__					: Tuple[int]  =      (16, 32, 96, 256)
 __magic_name__					: jnp.dtype  =      jnp.floataa




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=nn.Conv(
          self.block_out_channels[0]							,  kernel_size=(3, 3)							,  padding=((1, 1), (1, 1))							,  dtype=self.dtype							,  )

      a_       		=[]
      for i in range(len(self.block_out_channels) - 1):
           a_       		=self.block_out_channels[i]
           a_       		=self.block_out_channels[i + 1]
           a_       		=nn.Conv(
               lowerCAmelCase_							,  kernel_size=(3, 3)							,  padding=((1, 1), (1, 1))							,  dtype=self.dtype							,  )
           blocks.append(lowerCAmelCase_)
           a_       		=nn.Conv(
               lowerCAmelCase_							,  kernel_size=(3, 3)							,  strides=(2, 2)							,  padding=((1, 1), (1, 1))							,  dtype=self.dtype							,  )
           blocks.append(lowerCAmelCase_)
      a_       		=blocks

      a_       		=nn.Conv(
          self.conditioning_embedding_channels							,  kernel_size=(3, 3)							,  padding=((1, 1), (1, 1))							,  kernel_init=nn.initializers.zeros_init()							,  bias_init=nn.initializers.zeros_init()							,  dtype=self.dtype							,  )




 def __call__(   self							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      a_       		=self.conv_in(lowerCAmelCase_)
      a_       		=nn.silu(lowerCAmelCase_)

      for block in self.blocks:
           a_       		=block(lowerCAmelCase_)
           a_       		=nn.silu(lowerCAmelCase_)

      a_       		=self.conv_out(lowerCAmelCase_)

      return embedding







@flax_register_to_config
class 	UpperCAmelCase	(		nn.Module					,	__a					,	__a):


 '''simple docstring'''




 __magic_name__					: int  =      32
 __magic_name__					: int  =      4
 __magic_name__					: Tuple[str]  =      (
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 __magic_name__					: Union[bool, Tuple[bool]]  =      False
 __magic_name__					: Tuple[int]  =      (320, 640, 1_280, 1_280)
 __magic_name__					: int  =      2
 __magic_name__					: Union[int, Tuple[int]]  =      8
 __magic_name__					: Optional[Union[int, Tuple[int]]]  =      None
 __magic_name__					: int  =      1_280
 __magic_name__					: float  =      0.0
 __magic_name__					: bool  =      False
 __magic_name__					: jnp.dtype  =      jnp.floataa
 __magic_name__					: bool  =      True
 __magic_name__					: int  =      0
 __magic_name__					: str  =      "rgb"
 __magic_name__					: Tuple[int]  =      (16, 32, 96, 256)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      FrozenDict:



      """simple docstring"""







      a_       		=(1, self.in_channels, self.sample_size, self.sample_size)
      a_       		=jnp.zeros(lowerCAmelCase_							,  dtype=jnp.floataa)
      a_       		=jnp.ones((1,)							,  dtype=jnp.intaa)
      a_       		=jnp.zeros((1, 1, self.cross_attention_dim)							,  dtype=jnp.floataa)
      a_       		=(1, 3, self.sample_size * 8, self.sample_size * 8)
      a_       		=jnp.zeros(lowerCAmelCase_							,  dtype=jnp.floataa)

      a_		,						a_       		=jax.random.split(lowerCAmelCase_)
      a_       		={"params": params_rng, "dropout": dropout_rng}

      return self.init(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_)["params"]




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=self.block_out_channels
      a_       		=block_out_channels[0] * 4

      # If `num_attention_heads` is not defined (which is the case for most models)
      # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
      # The reason for this behavior is to correct for incorrectly named variables that were introduced
      # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
      # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
      # which is why we correct for the naming here.
      a_       		=self.num_attention_heads or self.attention_head_dim

      # input
      a_       		=nn.Conv(
          block_out_channels[0]							,  kernel_size=(3, 3)							,  strides=(1, 1)							,  padding=((1, 1), (1, 1))							,  dtype=self.dtype							,  )

      # time
      a_       		=FlaxTimesteps(
          block_out_channels[0]							,  flip_sin_to_cos=self.flip_sin_to_cos							,  freq_shift=self.config.freq_shift)
      a_       		=FlaxTimestepEmbedding(lowerCAmelCase_							,  dtype=self.dtype)

      a_       		=FlaxControlNetConditioningEmbedding(
          conditioning_embedding_channels=block_out_channels[0]							,  block_out_channels=self.conditioning_embedding_out_channels							,  )

      a_       		=self.only_cross_attention
      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=(only_cross_attention,) * len(self.down_block_types)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=(num_attention_heads,) * len(self.down_block_types)

      # down
      a_       		=[]
      a_       		=[]

      a_       		=block_out_channels[0]

      a_       		=nn.Conv(
          lowerCAmelCase_							,  kernel_size=(1, 1)							,  padding="VALID"							,  kernel_init=nn.initializers.zeros_init()							,  bias_init=nn.initializers.zeros_init()							,  dtype=self.dtype							,  )
      controlnet_down_blocks.append(lowerCAmelCase_)

      for i, down_block_type in enumerate(self.down_block_types):
           a_       		=output_channel
           a_       		=block_out_channels[i]
           a_       		=i == len(lowerCAmelCase_) - 1

           if down_block_type == "CrossAttnDownBlock2D":
                a_       		=FlaxCrossAttnDownBlockaD(
                    in_channels=lowerCAmelCase_							,  out_channels=lowerCAmelCase_							,  dropout=self.dropout							,  num_layers=self.layers_per_block							,  num_attention_heads=num_attention_heads[i]							,  add_downsample=not is_final_block							,  use_linear_projection=self.use_linear_projection							,  only_cross_attention=only_cross_attention[i]							,  dtype=self.dtype							,  )
           else:
                a_       		=FlaxDownBlockaD(
                    in_channels=lowerCAmelCase_							,  out_channels=lowerCAmelCase_							,  dropout=self.dropout							,  num_layers=self.layers_per_block							,  add_downsample=not is_final_block							,  dtype=self.dtype							,  )

           down_blocks.append(lowerCAmelCase_)

           for _ in range(self.layers_per_block):
                a_       		=nn.Conv(
                    lowerCAmelCase_							,  kernel_size=(1, 1)							,  padding="VALID"							,  kernel_init=nn.initializers.zeros_init()							,  bias_init=nn.initializers.zeros_init()							,  dtype=self.dtype							,  )
                controlnet_down_blocks.append(lowerCAmelCase_)

           if not is_final_block:
                a_       		=nn.Conv(
                    lowerCAmelCase_							,  kernel_size=(1, 1)							,  padding="VALID"							,  kernel_init=nn.initializers.zeros_init()							,  bias_init=nn.initializers.zeros_init()							,  dtype=self.dtype							,  )
                controlnet_down_blocks.append(lowerCAmelCase_)

      a_       		=down_blocks
      a_       		=controlnet_down_blocks

      # mid
      a_       		=block_out_channels[-1]
      a_       		=FlaxUNetMidBlockaDCrossAttn(
          in_channels=lowerCAmelCase_							,  dropout=self.dropout							,  num_attention_heads=num_attention_heads[-1]							,  use_linear_projection=self.use_linear_projection							,  dtype=self.dtype							,  )

      a_       		=nn.Conv(
          lowerCAmelCase_							,  kernel_size=(1, 1)							,  padding="VALID"							,  kernel_init=nn.initializers.zeros_init()							,  bias_init=nn.initializers.zeros_init()							,  dtype=self.dtype							,  )




 def __call__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = 1.0							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = False							,  ) ->      Union[FlaxControlNetOutput, Tuple]:



      """simple docstring"""







      a_       		=self.controlnet_conditioning_channel_order
      if channel_order == "bgr":
           a_       		=jnp.flip(lowerCAmelCase_							,  axis=1)

      # 1. time
      if not isinstance(lowerCAmelCase_							,  jnp.ndarray):
           a_       		=jnp.array([timesteps]							,  dtype=jnp.intaa)
      elif isinstance(lowerCAmelCase_							,  jnp.ndarray) and len(timesteps.shape) == 0:
           a_       		=timesteps.astype(dtype=jnp.floataa)
           a_       		=jnp.expand_dims(lowerCAmelCase_							,  0)

      a_       		=self.time_proj(lowerCAmelCase_)
      a_       		=self.time_embedding(lowerCAmelCase_)

      # 2. pre-process
      a_       		=jnp.transpose(lowerCAmelCase_							,  (0, 2, 3, 1))
      a_       		=self.conv_in(lowerCAmelCase_)

      a_       		=jnp.transpose(lowerCAmelCase_							,  (0, 2, 3, 1))
      a_       		=self.controlnet_cond_embedding(lowerCAmelCase_)
      sample += controlnet_cond

      # 3. down
      a_       		=(sample,)
      for down_block in self.down_blocks:
           if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
                a_		,						a_       		=down_block(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  deterministic=not train)
           else:
                a_		,						a_       		=down_block(lowerCAmelCase_							,  lowerCAmelCase_							,  deterministic=not train)
           down_block_res_samples += res_samples

      # 4. mid
      a_       		=self.mid_block(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  deterministic=not train)

      # 5. contronet blocks
      a_       		=()
      for down_block_res_sample, controlnet_block in zip(lowerCAmelCase_							,  self.controlnet_down_blocks):
           a_       		=controlnet_block(lowerCAmelCase_)
           controlnet_down_block_res_samples += (down_block_res_sample,)

      a_       		=controlnet_down_block_res_samples

      a_       		=self.controlnet_mid_block(lowerCAmelCase_)

      # 6. scaling
      a_       		=[sample * conditioning_scale for sample in down_block_res_samples]
      mid_block_res_sample *= conditioning_scale

      if not return_dict:
           return (down_block_res_samples, mid_block_res_sample)

      return FlaxControlNetOutput(
          down_block_res_samples=lowerCAmelCase_							,  mid_block_res_sample=lowerCAmelCase_)







'''simple docstring'''


from __future__ import annotations

lowercase							       =			[]






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if row >= len(lowercase__       ):
          solution.append(lowercase__       )
          printboard(lowercase__       )
          print()
          return True
     for i in range(len(lowercase__       )       ):
          if is_safe(lowercase__       ,	lowercase__       ,	lowercase__       ):
               a_       		=1
               solve(lowercase__       ,	row + 1       )
               a_       		=0
     return False






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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


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

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


