

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       KandinskyVaaInpaintPipeline
							UpperCAmelCase__			:		List[str]             =       ["image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		List[str]             =       [
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		int             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Dict             =       False


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												torch.manual_seed(0			)

												_a      :       Optional[int]   =						{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''image''',
												    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
												    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
												    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
												    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    '''layers_per_block''': 1,
												    '''encoder_hid_dim''': self.text_embedder_hidden_size,
												    '''encoder_hid_dim_type''': '''image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												_a      :       Optional[Any]   =						UNetaDConditionModel(**_a			)
												return model


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												torch.manual_seed(0			)
												_a      :       Optional[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						self.dummy_unet
												_a      :       List[Any]   =						self.dummy_movq

												_a      :       Union[str, Any]   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       int   =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       str:
												_a      :       List[str]   =						floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       int   =						floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(seed + 1			)			).to(
												    _a			)
												# create init_image
												_a      :       Union[str, Any]   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Union[str, Any]   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Any   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Optional[int]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       Dict   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       List[str]   =						torch.manual_seed(_a			)
												else:
																	_a      :       Optional[int]   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       List[str]   =						{
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						'''cpu'''

												_a      :       Union[str, Any]   =						self.get_dummy_components()

												_a      :       Optional[int]   =						self.pipeline_class(**_a			)
												_a      :       Optional[Any]   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Optional[Any]   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       Optional[Any]   =						output.images

												_a      :       Any   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Dict   =						image[0, -3:, -3:, -1]
												_a      :       Optional[int]   =						image_from_tuple[0, -3:, -3:, -1]

												print(F"""image.shape {image.shape}"""			)

												assert image.shape == (1, 6_4, 6_4, 3)

												_a      :       int   =						np.array(
												    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]			)

												assert (
												    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       List[str]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       List[str]   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       Tuple   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Dict   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       int   =						0

												_a      :       Dict   =						'''a hat'''

												_a      :       Dict   =						KandinskyVaaPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Union[str, Any]   =						KandinskyVaaInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       str   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Union[str, Any]   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Union[str, Any]   =						pipeline(
												    image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Tuple   =						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a			,				_a			)






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			a__												=						int(sys.argv[1])
			# main working structure of this module.
			a__												=						create_canvas(canvas_size)
			seed(c)
			a__						,     a__												=						plt.subplots()
			fig.show()
			a__												=						ListedColormap(['''w''', '''k'''])
			try:
						while True:
									a__												=						run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass



