




'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 				_snake_case	(			unittest.TestCase							):




			@property
			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										torch.manual_seed(0							)
										snake_case_			      =   UNetaDModel(
										    block_out_channels=(32, 64)       ,							layers_per_block=2       ,							sample_size=32       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=("DownBlock2D", "AttnDownBlock2D")       ,							up_block_types=("AttnUpBlock2D", "UpBlock2D")       ,							)
										return model




			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   "cpu"
										snake_case_			      =   self.dummy_uncond_unet
										snake_case_			      =   DDPMScheduler()

										snake_case_			      =   DDPMPipeline(unet=a__       ,							scheduler=a__							)
										ddpm.to(a__							)
										ddpm.set_progress_bar_config(disable=a__							)

										snake_case_			      =   torch.Generator(device=a__							).manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"							).images

										snake_case_			      =   torch.Generator(device=a__							).manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"       ,							return_dict=a__							)[0]

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

										assert image.shape == (1, 32, 32, 3)
										snake_case_			      =   np.array(
										    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_0_0, 1.199e-03, 2.648e-04, 5.101e-04]							)

										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							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   self.dummy_uncond_unet
										snake_case_			      =   DDPMScheduler(prediction_type="sample"							)

										snake_case_			      =   DDPMPipeline(unet=a__       ,							scheduler=a__							)
										ddpm.to(a__							)
										ddpm.set_progress_bar_config(disable=a__							)

										snake_case_			      =   torch.manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"							).images

										snake_case_			      =   torch.manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"							)[0]

										snake_case_			      =   image[0, -3:, -3:, -1]
										snake_case_			      =   image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_			      =   1e-2 if torch_device != "mps" else 3e-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance





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




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   "google/ddpm-cifar10-32"

										snake_case_			      =   UNetaDModel.from_pretrained(a__							)
										snake_case_			      =   DDPMScheduler.from_pretrained(a__							)

										snake_case_			      =   DDPMPipeline(unet=a__       ,							scheduler=a__							)
										ddpm.to(a__							)
										ddpm.set_progress_bar_config(disable=a__							)

										snake_case_			      =   torch.manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							output_type="numpy"							).images

										snake_case_			      =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_			      =   np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]							)
										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2










'''simple docstring'''





import heapq


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: List[str]							,       snake_case							: Dict  ):

							'''simple docstring'''






							snake_case_			      =   [(0, start)]  # cost from start node,end node
							snake_case_			      =   set()
							while heap:
														((snake_case_)      ,   (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
																					snake_case_			      =   cost + c
																					heapq.heappush(snake_case							,       (next_item, v)  )
							return -1


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "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]],
}
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_SCREAMING_SNAKE_CASE							:     Any   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

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

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

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

if __name__ == "__main__":
		import doctest

		doctest.testmod()




