




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Union[str, Any]    		=   StableDiffusionInpaintPipeline
      UpperCAmelCase__      :	Dict    		=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase__      :	str    		=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase__      :	int    		=   frozenset(
          [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCAmelCase__      :	Union[str, Any]    		=   frozenset([])
      def 			lowerCAmelCase__						(				self: str   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						UNetaDConditionModel(
                 block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       attention_head_dim=(2, 4)    ,       use_linear_projection=UpperCamelCase_    ,       )
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             torch.manual_seed(0   )
             __lowerCamelCase   =						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    ,       sample_size=1_28    ,       )
             torch.manual_seed(0   )
             __lowerCamelCase   =						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=10_00    ,       hidden_act="""gelu"""    ,       projection_dim=5_12    ,       )
             __lowerCamelCase   =						CLIPTextModel(UpperCamelCase_   )
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             __lowerCamelCase   =						{
                 """unet""": unet,
                 """scheduler""": scheduler,
                 """vae""": vae,
                 """text_encoder""": text_encoder,
                 """tokenizer""": tokenizer,
                 """safety_checker""": None,
                 """feature_extractor""": None,
             }
             return components
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]=0   ):
             # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
             __lowerCamelCase   =						floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(UpperCamelCase_   )   ).to(UpperCamelCase_   )
             __lowerCamelCase   =						image.cpu().permute(0    ,       2    ,       3    ,       1   )[0]
             __lowerCamelCase   =						Image.fromarray(np.uinta(UpperCamelCase_   )   ).convert("""RGB"""   ).resize((64, 64)   )
             __lowerCamelCase   =						Image.fromarray(np.uinta(image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )
             if str(UpperCamelCase_   ).startswith("""mps"""   ):
                    __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						{
                 """prompt""": """A painting of a squirrel eating a burger""",
                 """image""": init_image,
                 """mask_image""": mask_image,
                 """generator""": generator,
                 """num_inference_steps""": 2,
                 """guidance_scale""": 6.0,
                 """output_type""": """numpy""",
             }
             return inputs
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.get_dummy_components()
             __lowerCamelCase   =						StableDiffusionInpaintPipeline(**UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						self.get_dummy_inputs(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(**UpperCamelCase_   ).images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __lowerCamelCase   =						np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]   )

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




      def 			lowerCAmelCase__						(				self: int   ):
             super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





@slow
@require_torch_gpu
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: str   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
                 """/yellow_cat_sitting_on_a_park_bench.npy"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_    ,       safety_checker=UpperCamelCase_   )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       output_type="""np"""    ,       )
             __lowerCamelCase   =						output.images[0]

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 9E-3
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
                 """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(
                 UpperCamelCase_    ,       torch_dtype=torch.floataa    ,       safety_checker=UpperCamelCase_    ,       )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       output_type="""np"""    ,       )
             __lowerCamelCase   =						output.images[0]

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 5E-1




      def 			lowerCAmelCase__						(				self: int   ):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						PNDMScheduler.from_pretrained(UpperCamelCase_    ,       subfolder="""scheduler"""   )
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(
                 UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       torch_dtype=torch.floataa    ,       )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing(1   )
             pipe.enable_sequential_cpu_offload()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=2    ,       output_type="""np"""    ,       )

             __lowerCamelCase   =						torch.cuda.max_memory_allocated()
             # make sure that less than 2.65 GB is allocated
             assert mem_bytes < 2.65 * 10**9







from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[list[str]]  ,			A__       :					int  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						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 					lowerCamelCase__       (						A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       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)

