


"""simple docstring"""



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class _lowerCAmelCase							( snake_case_       ,				snake_case_       ,				snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			Optional[Any]				    =    StableDiffusionControlNetImgaImgPipeline
   __UpperCAmelCase					:			Dict				    =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
   __UpperCAmelCase					:			Dict				    =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __UpperCAmelCase					:			int				    =    IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}      )
   __UpperCAmelCase					:			Optional[Any]				    =    IMAGE_TO_IMAGE_IMAGE_PARAMS







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case       : List[Any]    =       UNetaDConditionModel(
           block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
       torch.manual_seed(0		)
       snake_case       : Any    =       ControlNetModel(
           block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
       torch.manual_seed(0		)
       snake_case       : Any    =       DDIMScheduler(
           beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=UpperCamelCase__		,      set_alpha_to_one=UpperCamelCase__		,      )
       torch.manual_seed(0		)
       snake_case       : List[str]    =       AutoencoderKL(
           block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=4		,      )
       torch.manual_seed(0		)
       snake_case       : str    =       CLIPTextConfig(
           bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
       snake_case       : str    =       CLIPTextModel(UpperCamelCase__		)
       snake_case       : Optional[Any]    =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

       snake_case       : Any    =       {
           "unet": unet,
           "controlnet": controlnet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=0		)      ->						Optional[int]:

       '''simple docstring'''




       if str(UpperCamelCase__		).startswith("mps"		):
           snake_case       : Optional[Any]    =       torch.manual_seed(UpperCamelCase__		)
       else:
           snake_case       : str    =       torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)

       snake_case       : Dict    =       2
       snake_case       : Dict    =       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=UpperCamelCase__		,      device=torch.device(UpperCamelCase__		)		,      )
       snake_case       : Dict    =       floats_tensor(control_image.shape		,      rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
       snake_case       : int    =       image.cpu().permute(0		,      2		,      3		,      1		)[0]
       snake_case       : List[Any]    =       Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("RGB"		).resize((64, 64)		)
       snake_case       : Optional[Any]    =       {
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
           "image": image,
           "control_image": control_image,
       }

       return inputs







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3		)







   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()		,      reason="XFormers attention is only available with CUDA and `xformers` installed"		,      )
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       self._test_inference_batch_single_identical(expected_max_diff=2e-3		)







class _lowerCAmelCase							( snake_case_       ,				snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			Optional[int]				    =    StableDiffusionControlNetImgaImgPipeline
   __UpperCAmelCase					:			List[Any]				    =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
   __UpperCAmelCase					:			Dict				    =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __UpperCAmelCase					:			Union[str, Any]				    =    frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







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

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case       : str    =       UNetaDConditionModel(
           block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
       torch.manual_seed(0		)

       def init_weights(UpperCamelCase__		):
           if isinstance(UpperCamelCase__		,      torch.nn.Convad		):
               torch.nn.init.normal(m.weight		)
               m.bias.data.fill_(1.0		)

       snake_case       : List[Any]    =       ControlNetModel(
           block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
       controlneta.controlnet_down_blocks.apply(UpperCamelCase__		)

       torch.manual_seed(0		)
       snake_case       : List[Any]    =       ControlNetModel(
           block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
       controlneta.controlnet_down_blocks.apply(UpperCamelCase__		)

       torch.manual_seed(0		)
       snake_case       : Tuple    =       DDIMScheduler(
           beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=UpperCamelCase__		,      set_alpha_to_one=UpperCamelCase__		,      )
       torch.manual_seed(0		)
       snake_case       : int    =       AutoencoderKL(
           block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=4		,      )
       torch.manual_seed(0		)
       snake_case       : str    =       CLIPTextConfig(
           bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
       snake_case       : Dict    =       CLIPTextModel(UpperCamelCase__		)
       snake_case       : str    =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

       snake_case       : Union[str, Any]    =       MultiControlNetModel([controlneta, controlneta]		)

       snake_case       : str    =       {
           "unet": unet,
           "controlnet": controlnet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=0		)      ->						Any:

       '''simple docstring'''




       if str(UpperCamelCase__		).startswith("mps"		):
           snake_case       : Any    =       torch.manual_seed(UpperCamelCase__		)
       else:
           snake_case       : List[str]    =       torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)

       snake_case       : int    =       2

       snake_case       : List[Any]    =       [
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=UpperCamelCase__		,      device=torch.device(UpperCamelCase__		)		,      ),
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=UpperCamelCase__		,      device=torch.device(UpperCamelCase__		)		,      ),
       ]

       snake_case       : List[Any]    =       floats_tensor(control_image[0].shape		,      rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		)[0]
       snake_case       : Dict    =       Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("RGB"		).resize((64, 64)		)
       snake_case       : str    =       {
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
           "image": image,
           "control_image": control_image,
       }

       return inputs







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Dict    =       self.get_dummy_components()
       snake_case       : List[str]    =       self.pipeline_class(**UpperCamelCase__		)
       pipe.to(UpperCamelCase__		)

       snake_case       : List[str]    =       10.0
       snake_case       : Dict    =       4

       snake_case       : Tuple    =       self.get_dummy_inputs(UpperCamelCase__		)
       snake_case       : List[Any]    =       steps
       snake_case       : List[Any]    =       scale
       snake_case       : Any    =       pipe(**UpperCamelCase__		)[0]

       snake_case       : Dict    =       self.get_dummy_inputs(UpperCamelCase__		)
       snake_case       : str    =       steps
       snake_case       : Union[str, Any]    =       scale
       snake_case       : List[Any]    =       pipe(**UpperCamelCase__		,      control_guidance_start=0.1		,      control_guidance_end=0.2		)[0]

       snake_case       : Dict    =       self.get_dummy_inputs(UpperCamelCase__		)
       snake_case       : int    =       steps
       snake_case       : List[Any]    =       scale
       snake_case       : Tuple    =       pipe(**UpperCamelCase__		,      control_guidance_start=[0.1, 0.3]		,      control_guidance_end=[0.2, 0.7]		)[0]

       snake_case       : Dict    =       self.get_dummy_inputs(UpperCamelCase__		)
       snake_case       : Union[str, Any]    =       steps
       snake_case       : int    =       scale
       snake_case       : Union[str, Any]    =       pipe(**UpperCamelCase__		,      control_guidance_start=0.4		,      control_guidance_end=[0.5, 0.8]		)[0]

       # make sure that all outputs are different
       assert np.sum(np.abs(output_a - output_a		)		) > 1e-3
       assert np.sum(np.abs(output_a - output_a		)		) > 1e-3
       assert np.sum(np.abs(output_a - output_a		)		) > 1e-3







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3		)







   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()		,      reason="XFormers attention is only available with CUDA and `xformers` installed"		,      )
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       self._test_inference_batch_single_identical(expected_max_diff=2e-3		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Tuple    =       self.get_dummy_components()
       snake_case       : Optional[int]    =       self.pipeline_class(**UpperCamelCase__		)
       pipe.to(UpperCamelCase__		)
       pipe.set_progress_bar_config(disable=UpperCamelCase__		)
       with tempfile.TemporaryDirectory() as tmpdir:
           try:
               # save_pretrained is not implemented for Multi-ControlNet
               pipe.save_pretrained(UpperCamelCase__		)
           except NotImplementedError:
               pass







@slow
@require_torch_gpu
class _lowerCAmelCase							( unittest.TestCase      ):







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"		)

       snake_case       : List[Any]    =       StableDiffusionControlNetImgaImgPipeline.from_pretrained(
           "runwayml/stable-diffusion-v1-5"		,      safety_checker=UpperCamelCase__		,      controlnet=UpperCamelCase__		)
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case       : Optional[Any]    =       torch.Generator(device="cpu"		).manual_seed(0		)
       snake_case       : Tuple    =       "evil space-punk bird"
       snake_case       : Optional[Any]    =       load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		).resize((512, 512)		)
       snake_case       : Optional[int]    =       load_image(
           "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"		).resize((512, 512)		)

       snake_case       : List[Any]    =       pipe(
           UpperCamelCase__		,      UpperCamelCase__		,      control_image=UpperCamelCase__		,      generator=UpperCamelCase__		,      output_type="np"		,      num_inference_steps=50		,      strength=0.6		,      )

       snake_case       : Optional[int]    =       output.images[0]

       assert image.shape == (512, 512, 3)

       snake_case       : Any    =       load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"		)

       assert np.abs(expected_image - image		).max() < 9e-2








"""simple docstring"""

from __future__ import annotations

_UpperCamelCase		     =    []



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						bool:
 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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						bool:
 if row >= len(lowercase__   ):
  solution.append(lowercase__   )
  printboard(lowercase__   )
  print()
  return True
 for i in range(len(lowercase__   )   ):
  if is_safe(lowercase__					,					lowercase__					,					lowercase__   ):
   lowerCAmelCase__   :				List[Any] 			=				1
   solve(lowercase__					,					row + 1   )
   lowerCAmelCase__   :				Dict 			=				0
 return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						None:
 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"))
_UpperCamelCase		     =    8
_UpperCamelCase		     =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






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






