"""simple docstring"""




def 	UpperCAmelCase       (						UpperCamelCase__ = 1_000					):






							"""simple docstring"""





							A__						  =				0
							A__						  =				(n - 1) // 3
							total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
							A__						  =				(n - 1) // 5
							total += ((terms) * (10 + (terms - 1) * 5)) // 2
							A__						  =				(n - 1) // 15
							total -= ((terms) * (30 + (terms - 1) * 15)) // 2
							return total


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






"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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__(      __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Tuple    				=		KandinskyPipeline
						lowerCAmelCase__	:    int    				=		[
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						lowerCAmelCase__	:    str    				=		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						lowerCAmelCase__	:    Any    				=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__	:    Dict    				=		False





						@property
						def 					snake_case__						(							self  )       ->					List[Any]:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Any:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Dict:
													return self.time_input_dim





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





						@property
						def 					snake_case__						(							self  )       ->					List[str]:
													return 1_00





						@property
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
													return tokenizer





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													torch.manual_seed(0  )
													A__						  =				MCLIPConfig(
													    numDims=self.cross_attention_dim		,transformerDimensions=self.text_embedder_hidden_size		,hidden_size=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_hidden_layers=5		,vocab_size=10_05		,)

													A__						  =				MultilingualCLIP(__UpperCAmelCase  )
													A__						  =				text_encoder.eval()

													return text_encoder





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													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': 'text_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': 'text_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(**__UpperCAmelCase  )
													return model





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													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 					snake_case__						(							self  )       ->					Any:
													torch.manual_seed(0  )
													A__						  =				VQModel(**self.dummy_movq_kwargs  )
													return model





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.dummy_text_encoder
													A__						  =				self.dummy_tokenizer
													A__						  =				self.dummy_unet
													A__						  =				self.dummy_movq

													A__						  =				DDIMScheduler(
													    num_train_timesteps=10_00		,beta_schedule='linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,steps_offset=1		,prediction_type='epsilon'		,thresholding=__UpperCAmelCase		,)

													A__						  =				{
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=0  )       ->					Optional[Any]:
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(__UpperCAmelCase  )  ).to(__UpperCAmelCase  )
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1  )  ).to(__UpperCAmelCase  )
													if str(__UpperCAmelCase  ).startswith('mps'  ):
																				A__						  =				torch.manual_seed(__UpperCAmelCase  )
													else:
																				A__						  =				torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
													A__						  =				{
													    'prompt': 'horse',
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				'cpu'

													A__						  =				self.get_dummy_components()

													A__						  =				self.pipeline_class(**__UpperCAmelCase  )
													A__						  =				pipe.to(__UpperCAmelCase  )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
													A__						  =				output.images

													A__						  =				pipe(
													    **self.get_dummy_inputs(__UpperCAmelCase  )		,return_dict=__UpperCAmelCase		,)[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(
													    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]  )

													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      UpperCamelCase__(      unittest.TestCase			):





						def 					snake_case__						(							self  )       ->					Tuple:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

													A__						  =				KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'		,torch_dtype=torch.floataa  )
													pipe_prior.to(__UpperCAmelCase  )

													A__						  =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,torch_dtype=torch.floataa  )
													A__						  =				pipeline.to(__UpperCAmelCase  )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				'red cat, 4k photo'

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__  ,	A__						  =				pipe_prior(
													    __UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=5		,negative_prompt=''		,).to_tuple()

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__						  =				pipeline(
													    __UpperCAmelCase		,image_embeds=__UpperCAmelCase		,negative_image_embeds=__UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=1_00		,output_type='np'		,)

													A__						  =				output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(__UpperCAmelCase		,__UpperCAmelCase  )





