

"""simple docstring"""




import heapq

def 		A    (       snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [(0, start)]  # cost from start node,end node
			SCREAMING_SNAKE_CASE__									= set()
			while heap:
						((SCREAMING_SNAKE_CASE__)							,						(SCREAMING_SNAKE_CASE__))									= heapq.heappop(snake_case__      )
						if u in visited:
									continue
						visited.add(snake_case__      )
						if u == end:
									return cost
						for v, c in graph[u]:
									if v in visited:
												continue
									SCREAMING_SNAKE_CASE__									= cost + c
									heapq.heappush(snake_case__						,			(next_item, v)      )
			return -1


A_							:     Optional[Any]     =	{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
A_							:     Union[str, Any]     =	{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
A_							:     int     =	{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

A_							:     Tuple     =	dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A_							:     int     =	dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

A_							:     Union[str, Any]     =	dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""




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 				lowerCamelCase  (A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						Optional[Any]   						=							KandinskyPipeline
			lowerCamelCase__				:						Union[str, Any]   						=							[
			    'prompt',
			    'image_embeds',
			    'negative_image_embeds',
			]
			lowerCamelCase__				:						Optional[Any]   						=							['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
			lowerCamelCase__				:						Union[str, Any]   						=							[
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'negative_prompt',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			lowerCamelCase__				:						Optional[Any]   						=							False

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      List[str]:
						return 3_2

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						return 3_2

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Any:
						return self.time_input_dim

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Any:
						return self.time_input_dim * 4

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[Any]:
						return 1_0_0

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""    )
						return tokenizer

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Dict:
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= MCLIPConfig(
						    numDims=self.cross_attention_dim      ,			transformerDimensions=self.text_embedder_hidden_size      ,			hidden_size=self.text_embedder_hidden_size      ,			intermediate_size=3_7      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			vocab_size=1_0_0_5      ,			)

						SCREAMING_SNAKE_CASE__									= MultilingualCLIP(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= text_encoder.eval()

						return text_encoder

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						torch.manual_seed(0    )

						SCREAMING_SNAKE_CASE__									= {
						    """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__									= UNetaDConditionModel(**__UpperCAmelCase    )
						return model

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Union[str, Any]:
						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      List[str]:
						torch.manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= VQModel(**self.dummy_movq_kwargs    )
						return model

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.dummy_text_encoder
						SCREAMING_SNAKE_CASE__									= self.dummy_tokenizer
						SCREAMING_SNAKE_CASE__									= self.dummy_unet
						SCREAMING_SNAKE_CASE__									= self.dummy_movq

						SCREAMING_SNAKE_CASE__									= DDIMScheduler(
						    num_train_timesteps=1_0_0_0      ,			beta_schedule="""linear"""      ,			beta_start=0.00_085      ,			beta_end=0.012      ,			clip_sample=__UpperCAmelCase      ,			set_alpha_to_one=__UpperCAmelCase      ,			steps_offset=1      ,			prediction_type="""epsilon"""      ,			thresholding=__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= {
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}
						return components

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]=0    )				->      str:
						SCREAMING_SNAKE_CASE__									= floats_tensor((1, self.cross_attention_dim)      ,			rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= floats_tensor((1, self.cross_attention_dim)      ,			rng=random.Random(seed + 1    )    ).to(__UpperCAmelCase    )
						if str(__UpperCAmelCase    ).startswith("""mps"""    ):
									SCREAMING_SNAKE_CASE__									= torch.manual_seed(__UpperCAmelCase    )
						else:
									SCREAMING_SNAKE_CASE__									= torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= {
						    """prompt""": """horse""",
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """guidance_scale""": 4.0,
						    """num_inference_steps""": 2,
						    """output_type""": """np""",
						}
						return inputs





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      int:
						SCREAMING_SNAKE_CASE__									= """cpu"""

						SCREAMING_SNAKE_CASE__									= self.get_dummy_components()

						SCREAMING_SNAKE_CASE__									= self.pipeline_class(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= pipe.to(__UpperCAmelCase    )

						pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
						SCREAMING_SNAKE_CASE__									= output.images

						SCREAMING_SNAKE_CASE__									= pipe(
						    **self.get_dummy_inputs(__UpperCAmelCase    )      ,			return_dict=__UpperCAmelCase      ,			)[0]

						SCREAMING_SNAKE_CASE__									= image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__									= image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__									= np.array(
						    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]    )

						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 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      List[str]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





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

						SCREAMING_SNAKE_CASE__									= KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""      ,			torch_dtype=torch.floataa    )
						pipe_prior.to(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""      ,			torch_dtype=torch.floataa    )
						SCREAMING_SNAKE_CASE__									= pipeline.to(__UpperCAmelCase    )
						pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= """red cat, 4k photo"""

						SCREAMING_SNAKE_CASE__									= torch.Generator(device="""cuda"""    ).manual_seed(0    )
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= pipe_prior(
						    __UpperCAmelCase      ,			generator=__UpperCAmelCase      ,			num_inference_steps=5      ,			negative_prompt=""""""      ,			).to_tuple()

						SCREAMING_SNAKE_CASE__									= torch.Generator(device="""cuda"""    ).manual_seed(0    )
						SCREAMING_SNAKE_CASE__									= pipeline(
						    __UpperCAmelCase      ,			image_embeds=__UpperCAmelCase      ,			negative_image_embeds=__UpperCAmelCase      ,			generator=__UpperCAmelCase      ,			num_inference_steps=1_0_0      ,			output_type="""np"""      ,			)

						SCREAMING_SNAKE_CASE__									= output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)

						assert_mean_pixel_difference(__UpperCAmelCase      ,			__UpperCAmelCase    )


