
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      _SCREAMING_SNAKE_CASE				(		_UpperCAmelCase,     unittest.TestCase				):
				lowerCamelCase_	  =  KandinskyVaaInpaintPipeline
				lowerCamelCase_	  =  ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
				lowerCamelCase_	  =  [
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				    'mask_image',
				]
				lowerCamelCase_	  =  [
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_	  =  False





				@property
				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								return 32





				@property
				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								return 32





				@property
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								return self.time_input_dim





				@property
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								return self.time_input_dim * 4





				@property
				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								return 100





				@property
				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								torch.manual_seed(0				)

								A  :	str				=					{
								    '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  :	Union[str, Any]				=					UNetaDConditionModel(**SCREAMING_SNAKE_CASE_				)
								return model





				@property
				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								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 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								torch.manual_seed(0				)
								A  :	List[Any]				=					VQModel(**self.dummy_movq_kwargs				)
								return model





				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								A  :	List[str]				=					self.dummy_unet
								A  :	str				=					self.dummy_movq

								A  :	List[str]				=					DDIMScheduler(
								    num_train_timesteps=1000       ,   beta_schedule='''linear'''       ,   beta_start=0.0_00_85       ,   beta_end=0.0_12       ,   clip_sample=SCREAMING_SNAKE_CASE_       ,   set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,   steps_offset=1       ,   prediction_type='''epsilon'''       ,   thresholding=SCREAMING_SNAKE_CASE_       ,   )

								A  :	Optional[Any]				=					{
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components





				def 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		Any       ,   snake_case_			:		Dict=0				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(SCREAMING_SNAKE_CASE_				)				).to(SCREAMING_SNAKE_CASE_				)
								A  :	str				=					floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(seed + 1				)				).to(
								    SCREAMING_SNAKE_CASE_				)
								# create init_image
								A  :	Optional[Any]				=					floats_tensor((1, 3, 64, 64)       ,   rng=random.Random(SCREAMING_SNAKE_CASE_				)				).to(SCREAMING_SNAKE_CASE_				)
								A  :	Union[str, Any]				=					image.cpu().permute(0       ,   2       ,   3       ,   1				)[0]
								A  :	Optional[int]				=					Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_				)				).convert('''RGB'''				).resize((256, 256)				)
								# create mask
								A  :	Optional[Any]				=					np.ones((64, 64)       ,   dtype=np.floataa				)
								A  :	Dict				=					0

								if str(SCREAMING_SNAKE_CASE_				).startswith('''mps'''				):
												A  :	Any				=					torch.manual_seed(SCREAMING_SNAKE_CASE_				)
								else:
												A  :	Optional[int]				=					torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(SCREAMING_SNAKE_CASE_				)
								A  :	Optional[int]				=					{
								    'image': init_image,
								    'mask_image': mask,
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 64,
								    'width': 64,
								    'num_inference_steps': 2,
								    'guidance_scale': 4.0,
								    'output_type': 'np',
								}
								return inputs





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	List[str]				=					'cpu'

								A  :	int				=					self.get_dummy_components()

								A  :	Optional[int]				=					self.pipeline_class(**SCREAMING_SNAKE_CASE_				)
								A  :	Union[str, Any]				=					pipe.to(SCREAMING_SNAKE_CASE_				)

								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

								A  :	str				=					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_				)				)
								A  :	List[str]				=					output.images

								A  :	Dict				=					pipe(
								    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_				)       ,   return_dict=SCREAMING_SNAKE_CASE_       ,   )[0]

								A  :	int				=					image[0, -3:, -3:, -1]
								A  :	Optional[Any]				=					image_from_tuple[0, -3:, -3:, -1]

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

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

								A  :	Dict				=					np.array(
								    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]				)

								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 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



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





				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

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






				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								A  :	str				=					load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''				)

								A  :	Optional[Any]				=					load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
								A  :	str				=					np.ones((768, 768)       ,   dtype=np.floataa				)
								A  :	str				=					0

								A  :	Tuple				=					'a hat'

								A  :	Union[str, Any]				=					KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''       ,   torch_dtype=torch.floataa				)
								pipe_prior.to(SCREAMING_SNAKE_CASE_				)

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

								A  :	Dict				=					torch.Generator(device='''cpu'''				).manual_seed(0				)
								A  :	str				=					pipe_prior(
								    SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_       ,   num_inference_steps=5       ,   negative_prompt=''''''       ,   ).to_tuple()

								A  :	Optional[Any]				=					pipeline(
								    image=SCREAMING_SNAKE_CASE_       ,   mask_image=SCREAMING_SNAKE_CASE_       ,   image_embeds=SCREAMING_SNAKE_CASE_       ,   negative_image_embeds=SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_       ,   num_inference_steps=100       ,   height=768       ,   width=768       ,   output_type='''np'''       ,   )

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

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_				)



'''simple docstring'''



from collections import defaultdict
from math import gcd



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_50_00_00				)	->						int:
     __lowerCamelCase  :      defaultdict														=						defaultdict(UpperCAmelCase_				)
     __lowerCamelCase  :      Any														=						2
     while 2 * euclid_m * (euclid_m + 1) <= limit:
          for euclid_n in range((euclid_m % 2) + 1 ,       UpperCAmelCase_ ,       2				):
               if gcd(UpperCAmelCase_ ,       UpperCAmelCase_				) > 1:
                    continue
               __lowerCamelCase  :      Any														=						2 * euclid_m * (euclid_m + euclid_n)
               for perimeter in range(UpperCAmelCase_ ,       limit + 1 ,       UpperCAmelCase_				):
                    frequencies[perimeter] += 1
          euclid_m += 1
     return sum(1 for frequency in frequencies.values() if frequency == 1				)


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






