


'''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 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    KandinskyImgaImgPipeline
	A_				=    ["prompt", "image_embeds", "negative_image_embeds", "image"]
	A_				=    [
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	]
	A_				=    [
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	A_				=    False




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 32




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 32




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.time_input_dim




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.time_input_dim * 4




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 100




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'							)
						return tokenizer




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       Tuple					    =  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						,				)

						__a			:       Any					    =  MultilingualCLIP(__a							)
						__a			:       int					    =  text_encoder.eval()

						return text_encoder




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)

						__a			:       Dict					    =  {
						    '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			:       str					    =  UNetaDConditionModel(**__a							)
						return model




	@property
	def       __UpperCAmelCase							(   self							):

						'''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							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  VQModel(**self.dummy_movq_kwargs							)
						return model




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.dummy_text_encoder
						__a			:       Any					    =  self.dummy_tokenizer
						__a			:       Union[str, Any]					    =  self.dummy_unet
						__a			:       List[Any]					    =  self.dummy_movq

						__a			:       Dict					    =  {
						    '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,
						}

						__a			:       Dict					    =  DDIMScheduler(**__a							)

						__a			:       Optional[int]					    =  {
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





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

						if str(__a							).startswith('mps'							):
											__a			:       str					    =  torch.manual_seed(__a							)
						else:
											__a			:       Union[str, Any]					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Optional[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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  'cpu'

						__a			:       Any					    =  self.get_dummy_components()

						__a			:       List[Any]					    =  self.pipeline_class(**__a							)
						__a			:       Optional[int]					    =  pipe.to(__a							)

						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  pipe(**self.get_dummy_inputs(__a							)							)
						__a			:       int					    =  output.images

						__a			:       int					    =  pipe(
						    **self.get_dummy_inputs(__a							)						,				return_dict=__a						,				)[0]

						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]
						__a			:       List[Any]					    =  image_from_tuple[0, -3:, -3:, -1]

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

						__a			:       int					    =  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 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinsky/kandinsky_img2img_frog.npy'							)

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

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

						__a			:       Union[str, Any]					    =  KandinskyImgaImgPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-1'						,				torch_dtype=torch.floataa							)
						__a			:       Optional[int]					    =  pipeline.to(__a							)

						pipeline.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  torch.Generator(device='cpu'							).manual_seed(0							)
						__a    ,				__a			:       List[str]					    =  pipe_prior(
						    __a						,				generator=__a						,				num_inference_steps=5						,				negative_prompt=''						,				).to_tuple()

						__a			:       Union[str, Any]					    =  pipeline(
						    __a						,				image=__a						,				image_embeds=__a						,				negative_image_embeds=__a						,				generator=__a						,				num_inference_steps=100						,				height=768						,				width=768						,				strength=0.2						,				output_type='np'						,				)

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

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

						assert_mean_pixel_difference(__a						,				__a							)







'''simple docstring'''



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  int(number**0.5   )
					return number == sq * sq






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
					__a			:       int					    =  x_den * y_den * z_den
					__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
					top //= hcf
					bottom //= hcf
					return top, bottom






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int = 35   ):
					__a			:       set					    =  set()
					__a			:       int
					__a			:       Fraction					    =  Fraction(0   )
					__a			:       tuple[int, int]

					for x_num in range(1						,      order + 1   ):
										for x_den in range(x_num + 1						,      order + 1   ):
															for y_num in range(1						,      order + 1   ):
																				for y_den in range(y_num + 1						,      order + 1   ):
																									# n=1
																									__a			:       Union[str, Any]					    =  x_num * y_den + x_den * y_num
																									__a			:       Optional[Any]					    =  x_den * y_den
																									__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														__a			:       Any					    =  add_three(
																														    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														unique_s.add(_SCREAMING_SNAKE_CASE   )

																									# n=2
																									__a			:       Optional[int]					    =  (
																									    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																									)
																									__a			:       Union[str, Any]					    =  x_den * x_den * y_den * y_den
																									if is_sq(_SCREAMING_SNAKE_CASE   ) and is_sq(_SCREAMING_SNAKE_CASE   ):
																														__a			:       List[Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Any					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Optional[int]					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			__a			:       List[Any]					    =  add_three(
																																			    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																																			unique_s.add(_SCREAMING_SNAKE_CASE   )

                    # n=-1
																									__a			:       int					    =  x_num * y_num
																									__a			:       Optional[Any]					    =  x_den * y_num + x_num * y_den
																									__a			:       Tuple					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														__a			:       Any					    =  add_three(
																														    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														unique_s.add(_SCREAMING_SNAKE_CASE   )

																									# n=2
																									__a			:       List[Any]					    =  x_num * x_num * y_num * y_num
																									__a			:       List[Any]					    =  (
																									    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																									)
																									if is_sq(_SCREAMING_SNAKE_CASE   ) and is_sq(_SCREAMING_SNAKE_CASE   ):
																														__a			:       Optional[Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Union[str, Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			__a			:       List[str]					    =  add_three(
																																			    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																																			unique_s.add(_SCREAMING_SNAKE_CASE   )

					for num, den in unique_s:
										total += Fraction(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					return total.denominator + total.numerator


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



