


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Any   				=      KandinskyVaaImgaImgPipeline
					UpperCAmelCase__ :   List[Any]   				=      ['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__ :   Any   				=      [
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__ :   Union[str, Any]   				=      [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__ :   int   				=      False
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return 32
					@property
					def 			lowerCamelCase__(				self       :str				) ->   str:
												return 32
					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[int]:
												return self.time_input_dim
					@property
					def 			lowerCamelCase__(				self       :int				) ->   str:
												return self.time_input_dim * 4
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Any:
												return 1_00
					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Tuple:
												torch.manual_seed(0				)

												a__					=					{
												    'in_channels': 4,
												    # 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__					=					UNetaDConditionModel(**__snake_case				)
												return model
					@property
					def 			lowerCamelCase__(				self       :str				) ->   List[Any]:
												return {
												    "block_out_channels": [32, 64],
												    "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": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}
					@property
					def 			lowerCamelCase__(				self       :str				) ->   Optional[Any]:
												torch.manual_seed(0				)
												a__					=					VQModel(**self.dummy_movq_kwargs				)
												return model
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Dict:
												a__					=					self.dummy_unet
												a__					=					self.dummy_movq

												a__					=					{
												    'num_train_timesteps': 10_00,
												    'beta_schedule': 'linear',
												    'beta_start': 0.0_00_85,
												    'beta_end': 0.0_12,
												    'clip_sample': False,
												    'set_alpha_to_one': False,
												    'steps_offset': 0,
												    'prediction_type': 'epsilon',
												    'thresholding': False,
												}

												a__					=					DDIMScheduler(**__snake_case				)

												a__					=					{
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Union[str, Any]      ,__snake_case       :Dict=0				) ->   Optional[Any]:
												a__					=					floats_tensor((1, self.text_embedder_hidden_size)      ,rng=random.Random(__snake_case				)				).to(__snake_case				)
												a__					=					floats_tensor((1, self.text_embedder_hidden_size)      ,rng=random.Random(seed + 1				)				).to(
												    __snake_case				)
												# create init_image
												a__					=					floats_tensor((1, 3, 64, 64)      ,rng=random.Random(__snake_case				)				).to(__snake_case				)
												a__					=					image.cpu().permute(0      ,2      ,3      ,1				)[0]
												a__					=					Image.fromarray(np.uinta(__snake_case				)				).convert('RGB'				).resize((2_56, 2_56)				)

												if str(__snake_case				).startswith('mps'				):
																			a__					=					torch.manual_seed(__snake_case				)
												else:
																			a__					=					torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
												a__					=					{
												    'image': init_image,
												    'image_embeds': image_embeds,
												    'negative_image_embeds': negative_image_embeds,
												    'generator': generator,
												    'height': 64,
												    'width': 64,
												    'num_inference_steps': 10,
												    'guidance_scale': 7.0,
												    'strength': 0.2,
												    'output_type': 'np',
												}
												return inputs



					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[int]:
												a__					=					'cpu'

												a__					=					self.get_dummy_components()

												a__					=					self.pipeline_class(**__snake_case				)
												a__					=					pipe.to(__snake_case				)

												pipe.set_progress_bar_config(disable=__snake_case				)

												a__					=					pipe(**self.get_dummy_inputs(__snake_case				)				)
												a__					=					output.images

												a__					=					pipe(
												    **self.get_dummy_inputs(__snake_case				)      ,return_dict=__snake_case      ,)[0]

												a__					=					image[0, -3:, -3:, -1]
												a__					=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												a__					=					np.array(
												    [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]				)
												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()}'







@slow
@require_torch_gpu
class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[str]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Dict:
												a__					=					load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinskyv22/kandinskyv22_img2img_frog.npy'				)

												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
												a__					=					'A red cartoon frog, 4k'

												a__					=					KandinskyVaaPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-prior'      ,torch_dtype=torch.floataa				)
												pipe_prior.to(__snake_case				)

												a__					=					KandinskyVaaImgaImgPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-decoder'      ,torch_dtype=torch.floataa				)
												a__					=					pipeline.to(__snake_case				)

												pipeline.set_progress_bar_config(disable=__snake_case				)

												a__					=					torch.Generator(device='cpu'				).manual_seed(0				)
												a__				, a__					=					pipe_prior(
												    __snake_case      ,generator=__snake_case      ,num_inference_steps=5      ,negative_prompt=''      ,).to_tuple()

												a__					=					pipeline(
												    image=__snake_case      ,image_embeds=__snake_case      ,negative_image_embeds=__snake_case      ,generator=__snake_case      ,num_inference_steps=1_00      ,height=7_68      ,width=7_68      ,strength=0.2      ,output_type='np'      ,)

												a__					=					output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(__snake_case      ,__snake_case				)





from collections import defaultdict
from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0 ,   __lowerCAmelCase							:    int = 1_0       ):
							a__					=					defaultdict(__lowerCAmelCase       )

							for outer_width in range(3 ,   (t_limit // 4) + 2       ):
														if outer_width * outer_width > t_limit:
																					a__					=					max(
																					    ceil(sqrt(outer_width * outer_width - t_limit       )       ) ,   1       )
														else:
																					a__					=					1

														hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

														for hole_width in range(__lowerCAmelCase ,   outer_width - 1 ,   2       ):
																					count[outer_width * outer_width - hole_width * hole_width] += 1

							return sum(1 for n in count.values() if 1 <= n <= 1_0       )


if __name__ == "__main__":
						print(f"""{solution() = }""")




