


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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					(     a__       ,   unittest.TestCase	):
					lowerCAmelCase      :Optional[Any]   =					KandinskyImgaImgPipeline
					lowerCAmelCase      :Union[str, Any]   =					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
					lowerCAmelCase      :Tuple   =					[
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					lowerCAmelCase      :List[Any]   =					[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase      :Union[str, Any]   =					False




					@property
					def 				snake_case__						(      self):
								return 32




					@property
					def 				snake_case__						(      self):
								return 32




					@property
					def 				snake_case__						(      self):
								return self.time_input_dim




					@property
					def 				snake_case__						(      self):
								return self.time_input_dim * 4




					@property
					def 				snake_case__						(      self):
								return 100




					@property
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
								return tokenizer




					@property
					def 				snake_case__						(      self):
								torch.manual_seed(0)
								UpperCAmelCase__  :					List[Any]        =     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=1005							,							)

								UpperCAmelCase__  :					Any        =     MultilingualCLIP(_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     text_encoder.eval()

								return text_encoder




					@property
					def 				snake_case__						(      self):
								torch.manual_seed(0)

								UpperCAmelCase__  :					Optional[Any]        =     {
								    """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,
								}

								UpperCAmelCase__  :					str        =     UNetaDConditionModel(**_lowerCamelCase)
								return model




					@property
					def 				snake_case__						(      self):
								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):
								torch.manual_seed(0)
								UpperCAmelCase__  :					Optional[Any]        =     VQModel(**self.dummy_movq_kwargs)
								return model




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     self.dummy_text_encoder
								UpperCAmelCase__  :					Tuple        =     self.dummy_tokenizer
								UpperCAmelCase__  :					str        =     self.dummy_unet
								UpperCAmelCase__  :					Union[str, Any]        =     self.dummy_movq

								UpperCAmelCase__  :					List[Any]        =     {
								    """num_train_timesteps""": 1000,
								    """beta_schedule""": """linear""",
								    """beta_start""": 0.00085,
								    """beta_end""": 0.012,
								    """clip_sample""": False,
								    """set_alpha_to_one""": False,
								    """steps_offset""": 0,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								}

								UpperCAmelCase__  :					Optional[int]        =     DDIMScheduler(**_lowerCamelCase)

								UpperCAmelCase__  :					Optional[int]        =     {
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Optional[int]        =     floats_tensor((1, self.cross_attention_dim)							,							rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
								UpperCAmelCase__  :					int        =     floats_tensor((1, self.cross_attention_dim)							,							rng=random.Random(seed + 1)).to(_lowerCamelCase)
								# create init_image
								UpperCAmelCase__  :					str        =     floats_tensor((1, 3, 64, 64)							,							rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     image.cpu().permute(0							,							2							,							3							,							1)[0]
								UpperCAmelCase__  :					List[Any]        =     Image.fromarray(np.uinta(_lowerCamelCase)).convert("""RGB""").resize((256, 256))

								if str(_lowerCamelCase).startswith("""mps"""):
											UpperCAmelCase__  :					List[str]        =     torch.manual_seed(_lowerCamelCase)
								else:
											UpperCAmelCase__  :					Union[str, Any]        =     torch.Generator(device=_lowerCamelCase).manual_seed(_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     {
								    """prompt""": """horse""",
								    """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 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """cpu"""

								UpperCAmelCase__  :					Optional[Any]        =     self.get_dummy_components()

								UpperCAmelCase__  :					str        =     self.pipeline_class(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     pipe.to(_lowerCamelCase)

								pipe.set_progress_bar_config(disable=_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     pipe(**self.get_dummy_inputs(_lowerCamelCase))
								UpperCAmelCase__  :					Tuple        =     output.images

								UpperCAmelCase__  :					Union[str, Any]        =     pipe(
								    **self.get_dummy_inputs(_lowerCamelCase)							,							return_dict=_lowerCamelCase							,							)[0]

								UpperCAmelCase__  :					int        =     image[0, -3:, -3:, -1]
								UpperCAmelCase__  :					List[str]        =     image_from_tuple[0, -3:, -3:, -1]

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

								UpperCAmelCase__  :					Any        =     np.array(
								    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
								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 				snake_case__						(      self):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinsky/kandinsky_img2img_frog.npy""")

								UpperCAmelCase__  :					List[Any]        =     load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
								UpperCAmelCase__  :					List[Any]        =     """A red cartoon frog, 4k"""

								UpperCAmelCase__  :					List[Any]        =     KandinskyPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1-prior"""							,							torch_dtype=torch.floataa)
								pipe_prior.to(_lowerCamelCase)

								UpperCAmelCase__  :					Optional[Any]        =     KandinskyImgaImgPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1"""							,							torch_dtype=torch.floataa)
								UpperCAmelCase__  :					str        =     pipeline.to(_lowerCamelCase)

								pipeline.set_progress_bar_config(disable=_lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     torch.Generator(device="""cpu""").manual_seed(0)
								UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     pipe_prior(
								    _lowerCamelCase							,							generator=_lowerCamelCase							,							num_inference_steps=5							,							negative_prompt=""""""							,							).to_tuple()

								UpperCAmelCase__  :					Any        =     pipeline(
								    _lowerCamelCase							,							image=_lowerCamelCase							,							image_embeds=_lowerCamelCase							,							negative_image_embeds=_lowerCamelCase							,							generator=_lowerCamelCase							,							num_inference_steps=100							,							height=768							,							width=768							,							strength=0.2							,							output_type="""np"""							,							)

								UpperCAmelCase__  :					Dict        =     output.images[0]

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

								assert_mean_pixel_difference(_lowerCamelCase							,							_lowerCamelCase)



'''simple docstring'''



from itertools import permutations







def     _UpperCamelCase      (		UpperCamelCase__					):
			if num[3] % 2 != 0:
						return False

			if (num[2] + num[3] + num[4]) % 3 != 0:
						return False

			if num[5] % 5 != 0:
						return False

			UpperCAmelCase__  :					Optional[int]        =     [7, 1_1, 1_3, 1_7]
			for i, test in enumerate(UpperCamelCase__					):
						if (num[i + 4] * 1_0_0 + num[i + 5] * 1_0 + num[i + 6]) % test != 0:
									return False
			return True







def     _UpperCamelCase      (		UpperCamelCase__ = 1_0					):
			return sum(
			    int("""""".join(map(UpperCamelCase__ ,      UpperCamelCase__					)					)					)
			    for num in permutations(range(UpperCamelCase__					)					)
			    if is_substring_divisible(UpperCamelCase__					)					)


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