

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   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Dict										=     KandinskyImgaImgPipeline
			_UpperCAmelCase   :							Dict										=     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			_UpperCAmelCase   :							Union[str, Any]										=     [
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			_UpperCAmelCase   :							Dict										=     [
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			_UpperCAmelCase   :							Tuple										=     False



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									return 32



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return 32



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									return self.time_input_dim



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



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return 100



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Any  		=       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
									return tokenizer



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: int  		=       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   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       MultilingualCLIP(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       text_encoder.eval()

									return text_encoder



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_: List[str]  		=       {
									    "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,
									}

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       UNetaDConditionModel(**lowerCAmelCase__)
									return model



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									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        _SCREAMING_SNAKE_CASE			( self  :     Any):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: Any  		=       VQModel(**self.dummy_movq_kwargs)
									return model



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.dummy_text_encoder
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.dummy_tokenizer
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.dummy_unet
									SCREAMING_SNAKE_CASE_: Any  		=       self.dummy_movq

									SCREAMING_SNAKE_CASE_: str  		=       {
									    "num_train_timesteps": 1000,
									    "beta_schedule": "linear",
									    "beta_start": 0.0_0085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									SCREAMING_SNAKE_CASE_: str  		=       DDIMScheduler(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components



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

									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_: List[str]  		=       torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       {
									    "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        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "cpu"

									SCREAMING_SNAKE_CASE_: Any  		=       self.get_dummy_components()

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.pipeline_class(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       pipe.to(lowerCAmelCase__)

									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

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

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

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

									SCREAMING_SNAKE_CASE_: str  		=       np.array(
									    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233])
									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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_img2img_frog.npy")

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

									SCREAMING_SNAKE_CASE_: int  		=       KandinskyPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-prior"   ,						torch_dtype=torch.floataa)
									pipe_prior.to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       KandinskyImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1"   ,						torch_dtype=torch.floataa)
									SCREAMING_SNAKE_CASE_: int  		=       pipeline.to(lowerCAmelCase__)

									pipeline.set_progress_bar_config(disable=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: 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"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

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

									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)









import math
import sys







def 			A_      (    _UpperCAmelCase    ):
						if number != int(_UpperCAmelCase    ):
												raise ValueError("the value of input must be a natural number"    )
						if number < 0:
												raise ValueError("the value of input must not be a negative number"    )
						if number == 0:
												return 1
						SCREAMING_SNAKE_CASE_: List[str]  		=       [-1] * (number + 1)
						SCREAMING_SNAKE_CASE_: str  		=       0
						for i in range(1				,	number + 1    ):
												SCREAMING_SNAKE_CASE_: str  		=       sys.maxsize
												SCREAMING_SNAKE_CASE_: List[Any]  		=       int(math.sqrt(_UpperCAmelCase    )    )
												for j in range(1				,	root + 1    ):
																		SCREAMING_SNAKE_CASE_: List[str]  		=       1 + answers[i - (j**2)]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       min(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Dict  		=       answer
						return answers[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






