
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()







class      A__							(     unittest.TestCase	):






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






							'''simple docstring'''







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






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		1
							lowerCAmelCase__      :      Tuple         =		3
							lowerCAmelCase__      :      List[Any]         =		(32, 32)

							lowerCAmelCase__      :      int         =		floats_tensor((batch_size, num_channels) + sizes    ,     rng=random.Random(0   )   ).to(__a   )
							return image






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      Union[str, Any]         =		UNetaDConditionModel(
							    block_out_channels=(32, 32, 64)    ,     layers_per_block=2    ,     sample_size=32    ,     in_channels=7    ,     out_channels=4    ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')    ,     up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')    ,     cross_attention_dim=32    ,     attention_head_dim=8    ,     use_linear_projection=__a    ,     only_cross_attention=(True, True, False)    ,     num_class_embeds=100    ,     )
							return model






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      int         =		AutoencoderKL(
							    block_out_channels=[32, 32, 64]    ,     in_channels=3    ,     out_channels=3    ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']    ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']    ,     latent_channels=4    ,     )
							return model






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      str         =		CLIPTextConfig(
							    bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=32    ,     intermediate_size=37    ,     layer_norm_eps=1E-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=1_000    ,     hidden_act='gelu'    ,     projection_dim=512    ,     )
							return CLIPTextModel(__a   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__      :      List[str]         =		self.dummy_cond_unet_upscale
							lowerCAmelCase__      :      Union[str, Any]         =		DDPMScheduler()
							lowerCAmelCase__      :      List[str]         =		DDIMScheduler(prediction_type='v_prediction'   )
							lowerCAmelCase__      :      List[str]         =		self.dummy_vae
							lowerCAmelCase__      :      Dict         =		self.dummy_text_encoder
							lowerCAmelCase__      :      Optional[Any]         =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

							lowerCAmelCase__      :      int         =		self.dummy_image.cpu().permute(0    ,     2    ,     3    ,     1   )[0]
							lowerCAmelCase__      :      Optional[Any]         =		Image.fromarray(np.uinta(__a   )   ).convert('RGB'   ).resize((64, 64)   )

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__      :      Optional[int]         =		StableDiffusionUpscalePipeline(
							    unet=__a    ,     low_res_scheduler=__a    ,     scheduler=__a    ,     vae=__a    ,     text_encoder=__a    ,     tokenizer=__a    ,     max_noise_level=350    ,     )
							lowerCAmelCase__      :      Union[str, Any]         =		sd_pipe.to(__a   )
							sd_pipe.set_progress_bar_config(disable=__a   )

							lowerCAmelCase__      :      Dict         =		"""A painting of a squirrel eating a burger"""
							lowerCAmelCase__      :      Optional[Any]         =		torch.Generator(device=__a   ).manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		sd_pipe(
							    [prompt]    ,     image=__a    ,     generator=__a    ,     guidance_scale=6.0    ,     noise_level=20    ,     num_inference_steps=2    ,     output_type='np'    ,     )

							lowerCAmelCase__      :      Optional[int]         =		output.images

							lowerCAmelCase__      :      Union[str, Any]         =		torch.Generator(device=__a   ).manual_seed(0   )
							lowerCAmelCase__      :      str         =		sd_pipe(
							    [prompt]    ,     image=__a    ,     generator=__a    ,     guidance_scale=6.0    ,     noise_level=20    ,     num_inference_steps=2    ,     output_type='np'    ,     return_dict=__a    ,     )[0]

							lowerCAmelCase__      :      int         =		image[0, -3:, -3:, -1]
							lowerCAmelCase__      :      Union[str, Any]         =		image_from_tuple[0, -3:, -3:, -1]

							lowerCAmelCase__      :      List[str]         =		low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							lowerCAmelCase__      :      str         =		np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__      :      List[Any]         =		self.dummy_cond_unet_upscale
							lowerCAmelCase__      :      Tuple         =		DDPMScheduler()
							lowerCAmelCase__      :      int         =		DDIMScheduler(prediction_type='v_prediction'   )
							lowerCAmelCase__      :      Optional[int]         =		self.dummy_vae
							lowerCAmelCase__      :      str         =		self.dummy_text_encoder
							lowerCAmelCase__      :      Tuple         =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

							lowerCAmelCase__      :      Optional[Any]         =		self.dummy_image.cpu().permute(0    ,     2    ,     3    ,     1   )[0]
							lowerCAmelCase__      :      Dict         =		Image.fromarray(np.uinta(__a   )   ).convert('RGB'   ).resize((64, 64)   )

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__      :      Union[str, Any]         =		StableDiffusionUpscalePipeline(
							    unet=__a    ,     low_res_scheduler=__a    ,     scheduler=__a    ,     vae=__a    ,     text_encoder=__a    ,     tokenizer=__a    ,     max_noise_level=350    ,     )
							lowerCAmelCase__      :      Optional[Any]         =		sd_pipe.to(__a   )
							sd_pipe.set_progress_bar_config(disable=__a   )

							lowerCAmelCase__      :      List[str]         =		"""A painting of a squirrel eating a burger"""
							lowerCAmelCase__      :      List[Any]         =		sd_pipe(
							    2 * [prompt]    ,     image=2 * [low_res_image]    ,     guidance_scale=6.0    ,     noise_level=20    ,     num_inference_steps=2    ,     output_type='np'    ,     )
							lowerCAmelCase__      :      str         =		output.images
							assert image.shape[0] == 2

							lowerCAmelCase__      :      Union[str, Any]         =		torch.Generator(device=__a   ).manual_seed(0   )
							lowerCAmelCase__      :      Optional[int]         =		sd_pipe(
							    [prompt]    ,     image=__a    ,     generator=__a    ,     num_images_per_prompt=2    ,     guidance_scale=6.0    ,     noise_level=20    ,     num_inference_steps=2    ,     output_type='np'    ,     )
							lowerCAmelCase__      :      Optional[Any]         =		output.images
							assert image.shape[0] == 2






				@unittest.skipIf(torch_device != 'cuda'    ,     'This test requires a GPU'   )
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		self.dummy_cond_unet_upscale
							lowerCAmelCase__      :      Any         =		DDPMScheduler()
							lowerCAmelCase__      :      Dict         =		DDIMScheduler(prediction_type='v_prediction'   )
							lowerCAmelCase__      :      List[Any]         =		self.dummy_vae
							lowerCAmelCase__      :      int         =		self.dummy_text_encoder
							lowerCAmelCase__      :      Optional[Any]         =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

							lowerCAmelCase__      :      List[str]         =		self.dummy_image.cpu().permute(0    ,     2    ,     3    ,     1   )[0]
							lowerCAmelCase__      :      str         =		Image.fromarray(np.uinta(__a   )   ).convert('RGB'   ).resize((64, 64)   )

							# put models in fp16, except vae as it overflows in fp16
							lowerCAmelCase__      :      Tuple         =		unet.half()
							lowerCAmelCase__      :      List[str]         =		text_encoder.half()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__      :      Union[str, Any]         =		StableDiffusionUpscalePipeline(
							    unet=__a    ,     low_res_scheduler=__a    ,     scheduler=__a    ,     vae=__a    ,     text_encoder=__a    ,     tokenizer=__a    ,     max_noise_level=350    ,     )
							lowerCAmelCase__      :      Union[str, Any]         =		sd_pipe.to(__a   )
							sd_pipe.set_progress_bar_config(disable=__a   )

							lowerCAmelCase__      :      Dict         =		"""A painting of a squirrel eating a burger"""
							lowerCAmelCase__      :      int         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[str]         =		sd_pipe(
							    [prompt]    ,     image=__a    ,     generator=__a    ,     num_inference_steps=2    ,     output_type='np'    ,     ).images

							lowerCAmelCase__      :      int         =		low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)







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






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-upscale/low_res_cat.png'   )
							lowerCAmelCase__      :      Union[str, Any]         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
							    '/upsampled_cat.npy'   )

							lowerCAmelCase__      :      Any         =		"""stabilityai/stable-diffusion-x4-upscaler"""
							lowerCAmelCase__      :      Dict         =		StableDiffusionUpscalePipeline.from_pretrained(__a   )
							pipe.to(__a   )
							pipe.set_progress_bar_config(disable=__a   )
							pipe.enable_attention_slicing()

							lowerCAmelCase__      :      List[str]         =		"""a cat sitting on a park bench"""

							lowerCAmelCase__      :      Optional[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      Tuple         =		pipe(
							    prompt=__a    ,     image=__a    ,     generator=__a    ,     output_type='np'    ,     )
							lowerCAmelCase__      :      Optional[int]         =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image   ).max() < 1E-3






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-upscale/low_res_cat.png'   )
							lowerCAmelCase__      :      Optional[int]         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
							    '/upsampled_cat_fp16.npy'   )

							lowerCAmelCase__      :      Optional[int]         =		"""stabilityai/stable-diffusion-x4-upscaler"""
							lowerCAmelCase__      :      int         =		StableDiffusionUpscalePipeline.from_pretrained(
							    __a    ,     torch_dtype=torch.floataa    ,     )
							pipe.to(__a   )
							pipe.set_progress_bar_config(disable=__a   )
							pipe.enable_attention_slicing()

							lowerCAmelCase__      :      List[str]         =		"""a cat sitting on a park bench"""

							lowerCAmelCase__      :      List[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		pipe(
							    prompt=__a    ,     image=__a    ,     generator=__a    ,     output_type='np'    ,     )
							lowerCAmelCase__      :      Optional[Any]         =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image   ).max() < 5E-1






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase__      :      Tuple         =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-upscale/low_res_cat.png'   )

							lowerCAmelCase__      :      List[str]         =		"""stabilityai/stable-diffusion-x4-upscaler"""
							lowerCAmelCase__      :      Any         =		StableDiffusionUpscalePipeline.from_pretrained(
							    __a    ,     torch_dtype=torch.floataa    ,     )
							pipe.to(__a   )
							pipe.set_progress_bar_config(disable=__a   )
							pipe.enable_attention_slicing(1   )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase__      :      Optional[int]         =		"""a cat sitting on a park bench"""

							lowerCAmelCase__      :      Dict         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      Union[str, Any]         =		pipe(
							    prompt=__a    ,     image=__a    ,     generator=__a    ,     num_inference_steps=5    ,     output_type='np'    ,     )

							lowerCAmelCase__      :      str         =		torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9







import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				Graph(5)
	graph.add_edge(0, 2, 9)
	graph.add_edge(0, 4, 10)
	graph.add_edge(1, 3, 5)
	graph.add_edge(2, 3, 7)
	graph.add_edge(3, 0, 10)
	graph.add_edge(3, 1, 2)
	graph.add_edge(3, 2, 1)
	graph.add_edge(3, 4, 6)
	graph.add_edge(4, 1, 3)
	graph.add_edge(4, 2, 4)
	graph.add_edge(4, 3, 9)
	graph.floyd_warshall()
	graph.show_min(1, 4)
	graph.show_min(0, 3)