






from __future__ import annotations

class      UpperCamelCase_							:




						def __init__(    self		,	lowercase       )			->						Union[str, Any]:
													_a  :    Union[str, Any]			=						vertices
													_a  :    Dict			=						[[0] * vertices for _ in range(_lowerCamelCase       )]




						def      snake_case__(    self		,	lowercase       )			->						Optional[Any]:
													print('''Vertex \t Distance from Source'''       )
													for vertex in range(self.vertices       ):
																				print(_lowerCamelCase		,	'''\t\t'''		,	distances_from_source[vertex]       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						List[str]:
													_a  :    Union[str, Any]			=						1e7
													_a  :    int			=						0

													# Search not nearest vertex not in the shortest path tree
													for vertex in range(self.vertices       ):
																				if distances_from_source[vertex] < minimum and visited[vertex] is False:
																											_a  :    Union[str, Any]			=						distances_from_source[vertex]
																											_a  :    List[Any]			=						vertex
													return min_index







						def      snake_case__(    self		,	lowercase       )			->						str:
													_a  :    Dict			=						[int(1e7       )] * self.vertices  # distances from the source
													_a  :    Union[str, Any]			=						0
													_a  :    Optional[Any]			=						[False] * self.vertices

													for _ in range(self.vertices       ):
																				_a  :    List[Any]			=						self.minimum_distance(_lowerCamelCase		,	_lowerCamelCase       )
																				_a  :    Tuple			=						True

																				# Update dist value of the adjacent vertices
																				# of the picked vertex only if the current
																				# distance is greater than new distance and
																				# the vertex in not in the shortest path tree
																				for v in range(self.vertices       ):
																											if (
																											    self.graph[u][v] > 0
																											    and visited[v] is False
																											    and distances[v] > distances[u] + self.graph[u][v]
																											):
																																		_a  :    Optional[Any]			=						distances[u] + self.graph[u][v]

													self.print_solution(_lowerCamelCase       )


if __name__ == "__main__":
						__lowerCamelCase						= Graph(9)
						__lowerCamelCase						= [
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)

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(   SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Dict				   =      KandinskyPipeline
						__A:						List[Any]				   =      [
						    """prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						]
						__A:						Dict				   =      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
						__A:						List[str]				   =      [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						__A:						List[str]				   =      False

						@property
						def 						a__ (					self		:			List[Any]			):
													return 32

						@property
						def 						a__ (					self		:			Tuple			):
													return 32

						@property
						def 						a__ (					self		:			List[Any]			):
													return self.time_input_dim

						@property
						def 						a__ (					self		:			List[Any]			):
													return self.time_input_dim * 4

						@property
						def 						a__ (					self		:			Dict			):
													return 1_00

						@property
						def 						a__ (					self		:			Tuple			):
													_UpperCAmelCase  :			Optional[int]	       =		XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
													return tokenizer

						@property
						def 						a__ (					self		:			Tuple			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			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=10_05	,						)

													_UpperCAmelCase  :			Optional[Any]	       =		MultilingualCLIP(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		text_encoder.eval()

													return text_encoder

						@property
						def 						a__ (					self		:			Any			):
													torch.manual_seed(0			)

													_UpperCAmelCase  :			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  :			Optional[Any]	       =		UNetaDConditionModel(**_lowerCamelCase			)
													return model

						@property
						def 						a__ (					self		:			Optional[Any]			):
													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 						a__ (					self		:			Tuple			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Dict	       =		VQModel(**self.dummy_movq_kwargs			)
													return model

						def 						a__ (					self		:			List[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		self.dummy_text_encoder
													_UpperCAmelCase  :			Any	       =		self.dummy_tokenizer
													_UpperCAmelCase  :			List[Any]	       =		self.dummy_unet
													_UpperCAmelCase  :			Union[str, Any]	       =		self.dummy_movq

													_UpperCAmelCase  :			str	       =		DDIMScheduler(
													    num_train_timesteps=10_00	,						beta_schedule="linear"	,						beta_start=0.0_00_85	,						beta_end=0.0_12	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						steps_offset=1	,						prediction_type="epsilon"	,						thresholding=_lowerCamelCase	,						)

													_UpperCAmelCase  :			Optional[Any]	       =		{
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "movq": movq,
													}
													return components

						def 						a__ (					self		:			int	,						_lowerCamelCase		:			Any	,						_lowerCamelCase		:			List[str]=0			):
													_UpperCAmelCase  :			Tuple	       =		floats_tensor((1, self.cross_attention_dim)	,						rng=random.Random(_lowerCamelCase			)			).to(_lowerCamelCase			)
													_UpperCAmelCase  :			Tuple	       =		floats_tensor((1, self.cross_attention_dim)	,						rng=random.Random(seed + 1			)			).to(_lowerCamelCase			)
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Union[str, Any]	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		{
													    "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 						a__ (					self		:			List[Any]			):
													_UpperCAmelCase  :			Optional[Any]	       =		"cpu"

													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()

													_UpperCAmelCase  :			Any	       =		self.pipeline_class(**_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[int]	       =		pipe.to(_lowerCamelCase			)

													pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Dict	       =		pipe(**self.get_dummy_inputs(_lowerCamelCase			)			)
													_UpperCAmelCase  :			int	       =		output.images

													_UpperCAmelCase  :			Tuple	       =		pipe(
													    **self.get_dummy_inputs(_lowerCamelCase			)	,						return_dict=_lowerCamelCase	,						)[0]

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

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

													_UpperCAmelCase  :			Dict	       =		np.array(
													    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]			)

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






						def 						a__ (					self		:			str			):
													_UpperCAmelCase  :			List[str]	       =		load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/kandinsky/kandinsky_text2img_cat_fp16.npy"			)

													_UpperCAmelCase  :			Dict	       =		KandinskyPriorPipeline.from_pretrained(
													    "kandinsky-community/kandinsky-2-1-prior"	,						torch_dtype=torch.floataa			)
													pipe_prior.to(_lowerCamelCase			)

													_UpperCAmelCase  :			str	       =		KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"	,						torch_dtype=torch.floataa			)
													_UpperCAmelCase  :			Union[str, Any]	       =		pipeline.to(_lowerCamelCase			)
													pipeline.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Tuple	       =		"red cat, 4k photo"

													_UpperCAmelCase  :			List[Any]	       =		torch.Generator(device="cuda"			).manual_seed(0			)
													_UpperCAmelCase       ,_UpperCAmelCase  :			int	       =		pipe_prior(
													    _lowerCamelCase	,						generator=_lowerCamelCase	,						num_inference_steps=5	,						negative_prompt=""	,						).to_tuple()

													_UpperCAmelCase  :			Tuple	       =		torch.Generator(device="cuda"			).manual_seed(0			)
													_UpperCAmelCase  :			Dict	       =		pipeline(
													    _lowerCamelCase	,						image_embeds=_lowerCamelCase	,						negative_image_embeds=_lowerCamelCase	,						generator=_lowerCamelCase	,						num_inference_steps=1_00	,						output_type="np"	,						)

													_UpperCAmelCase  :			Tuple	       =		output.images[0]

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

													assert_mean_pixel_difference(_lowerCamelCase	,						_lowerCamelCase			)




