





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    __A  (    _UpperCAmelCase     ,       _UpperCAmelCase     ,       _UpperCAmelCase     ,       unittest.TestCase     ):
				__A  =					StableDiffusionImageVariationPipeline
				__A  =					IMAGE_VARIATION_PARAMS
				__A  =					IMAGE_VARIATION_BATCH_PARAMS
				__A  =					frozenset([]     )
				# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__A  =					frozenset([]     )


				def _snake_case							(		self				):
								torch.manual_seed(0				)
								lowerCamelCase									=UNetaDConditionModel(
								    block_out_channels=(32, 64) ,			layers_per_block=2 ,			sample_size=32 ,			in_channels=4 ,			out_channels=4 ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,			cross_attention_dim=32 ,			)
								lowerCamelCase									=PNDMScheduler(skip_prk_steps=UpperCAmelCase_				)
								torch.manual_seed(0				)
								lowerCamelCase									=AutoencoderKL(
								    block_out_channels=[32, 64] ,			in_channels=3 ,			out_channels=3 ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,			latent_channels=4 ,			)
								torch.manual_seed(0				)
								lowerCamelCase									=CLIPVisionConfig(
								    hidden_size=32 ,			projection_dim=32 ,			intermediate_size=37 ,			layer_norm_eps=1E-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			image_size=32 ,			patch_size=4 ,			)
								lowerCamelCase									=CLIPVisionModelWithProjection(UpperCAmelCase_				)
								lowerCamelCase									=CLIPImageProcessor(crop_size=32 ,			size=32				)

								lowerCamelCase									={
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """image_encoder""": image_encoder,
								    """feature_extractor""": feature_extractor,
								    """safety_checker""": None,
								}
								return components


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=0				):
								lowerCamelCase									=floats_tensor((1, 3, 32, 32) ,			rng=random.Random(UpperCAmelCase_				)				)
								lowerCamelCase									=image.cpu().permute(0 ,			2 ,			3 ,			1				)[0]
								lowerCamelCase									=Image.fromarray(np.uinta(UpperCAmelCase_				)				).convert("""RGB"""				).resize((32, 32)				)
								if str(UpperCAmelCase_				).startswith("""mps"""				):
												lowerCamelCase									=torch.manual_seed(UpperCAmelCase_				)
								else:
												lowerCamelCase									=torch.Generator(device=UpperCAmelCase_				).manual_seed(UpperCAmelCase_				)
								lowerCamelCase									={
								    """image""": image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs


				def _snake_case							(		self				):
								lowerCamelCase									="""cpu"""  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase									=self.get_dummy_components()
								lowerCamelCase									=StableDiffusionImageVariationPipeline(**UpperCAmelCase_				)
								lowerCamelCase									=sd_pipe.to(UpperCAmelCase_				)
								sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

								lowerCamelCase									=self.get_dummy_inputs(UpperCAmelCase_				)
								lowerCamelCase									=sd_pipe(**UpperCAmelCase_				).images
								lowerCamelCase									=image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase									=np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3


				def _snake_case							(		self				):
								lowerCamelCase									="""cpu"""  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase									=self.get_dummy_components()
								lowerCamelCase									=StableDiffusionImageVariationPipeline(**UpperCAmelCase_				)
								lowerCamelCase									=sd_pipe.to(UpperCAmelCase_				)
								sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

								lowerCamelCase									=self.get_dummy_inputs(UpperCAmelCase_				)
								lowerCamelCase									=2 * [inputs["""image"""]]
								lowerCamelCase									=sd_pipe(**UpperCAmelCase_				)

								lowerCamelCase									=output.images

								lowerCamelCase									=image[-1, -3:, -3:, -1]

								assert image.shape == (2, 64, 64, 3)
								lowerCamelCase									=np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3


				def _snake_case							(		self				):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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


				def _snake_case							(		self				):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_="cpu" ,			UpperCAmelCase_=torch.floataa ,			UpperCAmelCase_=0				):
								lowerCamelCase									=torch.Generator(device=UpperCAmelCase_				).manual_seed(UpperCAmelCase_				)
								lowerCamelCase									=load_image(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/input_image_vermeer.png"""				)
								lowerCamelCase									=np.random.RandomState(UpperCAmelCase_				).standard_normal((1, 4, 64, 64)				)
								lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				).to(device=UpperCAmelCase_ ,			dtype=UpperCAmelCase_				)
								lowerCamelCase									={
								    """image""": init_image,
								    """latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 3,
								    """guidance_scale""": 7.5,
								    """output_type""": """numpy""",
								}
								return inputs


				def _snake_case							(		self				):
								lowerCamelCase									=StableDiffusionImageVariationPipeline.from_pretrained(
								    """lambdalabs/sd-image-variations-diffusers""" ,			safety_checker=UpperCAmelCase_				)
								lowerCamelCase									=sd_pipe.to(UpperCAmelCase_				)
								sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

								lowerCamelCase									=self.get_inputs(UpperCAmelCase_				)
								lowerCamelCase									=sd_pipe(**UpperCAmelCase_				).images
								lowerCamelCase									=image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase									=np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]				)
								assert np.abs(image_slice - expected_slice				).max() < 6E-3


				def _snake_case							(		self				):
								lowerCamelCase									=0

								def callback_fn(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				) -> None:
												lowerCamelCase									=True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																lowerCamelCase									=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase									=latents[0, -3:, -3:, -1]
																lowerCamelCase									=np.array(
																    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]				)

																assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2
												elif step == 2:
																lowerCamelCase									=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase									=latents[0, -3:, -3:, -1]
																lowerCamelCase									=np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]				)

																assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2

								lowerCamelCase									=False

								lowerCamelCase									=StableDiffusionImageVariationPipeline.from_pretrained(
								    """fusing/sd-image-variations-diffusers""" ,			safety_checker=UpperCAmelCase_ ,			torch_dtype=torch.floataa ,			)
								pipe.to(UpperCAmelCase_				)
								pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
								pipe.enable_attention_slicing()

								lowerCamelCase									=self.get_inputs(UpperCAmelCase_ ,			dtype=torch.floataa				)
								pipe(**UpperCAmelCase_ ,			callback=UpperCAmelCase_ ,			callback_steps=1				)
								assert callback_fn.has_been_called
								assert number_of_steps == inputs["num_inference_steps"]


				def _snake_case							(		self				):
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								lowerCamelCase									="""fusing/sd-image-variations-diffusers"""
								lowerCamelCase									=StableDiffusionImageVariationPipeline.from_pretrained(
								    UpperCAmelCase_ ,			safety_checker=UpperCAmelCase_ ,			torch_dtype=torch.floataa				)
								lowerCamelCase									=pipe.to(UpperCAmelCase_				)
								pipe.set_progress_bar_config(disable=UpperCAmelCase_				)
								pipe.enable_attention_slicing(1				)
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase									=self.get_inputs(UpperCAmelCase_ ,			dtype=torch.floataa				)
								lowerCamelCase									=pipe(**UpperCAmelCase_				)

								lowerCamelCase									=torch.cuda.max_memory_allocated()
								# make sure that less than 2.6 GB is allocated
								assert mem_bytes < 2.6 * 10**9




@nightly
@require_torch_gpu
class    __A  (    unittest.TestCase     ):


				def _snake_case							(		self				):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_="cpu" ,			UpperCAmelCase_=torch.floataa ,			UpperCAmelCase_=0				):
								lowerCamelCase									=torch.Generator(device=UpperCAmelCase_				).manual_seed(UpperCAmelCase_				)
								lowerCamelCase									=load_image(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/input_image_vermeer.png"""				)
								lowerCamelCase									=np.random.RandomState(UpperCAmelCase_				).standard_normal((1, 4, 64, 64)				)
								lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				).to(device=UpperCAmelCase_ ,			dtype=UpperCAmelCase_				)
								lowerCamelCase									={
								    """image""": init_image,
								    """latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 50,
								    """guidance_scale""": 7.5,
								    """output_type""": """numpy""",
								}
								return inputs


				def _snake_case							(		self				):
								lowerCamelCase									=StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""				)
								sd_pipe.to(UpperCAmelCase_				)
								sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

								lowerCamelCase									=self.get_inputs(UpperCAmelCase_				)
								lowerCamelCase									=sd_pipe(**UpperCAmelCase_				).images[0]

								lowerCamelCase									=load_numpy(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""				)
								lowerCamelCase									=np.abs(expected_image - image				).max()
								assert max_diff < 1E-3


				def _snake_case							(		self				):
								lowerCamelCase									=StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""				)
								lowerCamelCase									=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config				)
								sd_pipe.to(UpperCAmelCase_				)
								sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_				)

								lowerCamelCase									=self.get_inputs(UpperCAmelCase_				)
								lowerCamelCase									=25
								lowerCamelCase									=sd_pipe(**UpperCAmelCase_				).images[0]

								lowerCamelCase									=load_numpy(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""				)
								lowerCamelCase									=np.abs(expected_image - image				).max()
								assert max_diff < 1E-3













import math
import sys



class  __UpperCAmelCase						:







			def __init__(      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    0
									_SCREAMING_SNAKE_CASE       =    []
									_SCREAMING_SNAKE_CASE       =    {}  # To store the pos of node in array







			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									return self.cur_size == 0







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.left(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.right(UpperCAmelCase_	)
									if lc < self.cur_size and self.array(UpperCAmelCase_	)[0] < self.array(UpperCAmelCase_	)[0]:
															_SCREAMING_SNAKE_CASE       =    lc
									else:
															_SCREAMING_SNAKE_CASE       =    idx
									if rc < self.cur_size and self.array(UpperCAmelCase_	)[0] < self.array(UpperCAmelCase_	)[0]:
															_SCREAMING_SNAKE_CASE       =    rc
									if smallest != idx:
															self.swap(UpperCAmelCase_ ,      UpperCAmelCase_	)
															self.min_heapify(UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.cur_size
									self.cur_size += 1
									self.array.append((sys.maxsize, tup[1])	)
									self.decrease_key((sys.maxsize, tup[1]) ,      tup[0]	)







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







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.array[0][1]
									_SCREAMING_SNAKE_CASE       =    self.array[self.cur_size - 1]
									self.cur_size -= 1
									self.min_heapify(1	)
									del self.pos[min_node]
									return min_node







			def 				UpperCamelCase (      self:    Dict ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									return 2 * i + 1







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									return 2 * i + 2







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    int	):







									'''simple docstring'''




									return math.floor(i / 2	)







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    j
									_SCREAMING_SNAKE_CASE       =    i
									_SCREAMING_SNAKE_CASE       =    self.array[i]
									_SCREAMING_SNAKE_CASE       =    self.array[j]
									_SCREAMING_SNAKE_CASE       =    temp







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Dict ,      UpperCAmelCase_:    Dict	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.pos[tup[1]]
									# assuming the new_d is atmost old_d
									_SCREAMING_SNAKE_CASE       =    (new_d, tup[1])
									while idx > 0 and self.array[self.par(UpperCAmelCase_	)][0] > self.array[idx][0]:
															self.swap(UpperCAmelCase_ ,      self.par(UpperCAmelCase_	)	)
															_SCREAMING_SNAKE_CASE       =    self.par(UpperCAmelCase_	)



class  __UpperCAmelCase						:







			def __init__(      self:    Tuple ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    {}  # To store graph: u -> (v,w)
									_SCREAMING_SNAKE_CASE       =    num  # Number of nodes in graph
									# To store the distance from source vertex
									_SCREAMING_SNAKE_CASE       =    [0] * self.num_nodes
									_SCREAMING_SNAKE_CASE       =    [-1] * self.num_nodes  # To store the path







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




									if u in self.adjList:
															self.adjList[u].append((v, w)	)
									else:
															_SCREAMING_SNAKE_CASE       =    [(v, w)]

									# Assuming undirected graph
									if v in self.adjList:
															self.adjList[v].append((u, w)	)
									else:
															_SCREAMING_SNAKE_CASE       =    [(u, w)]







			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									for u in self.adjList:
															print(UpperCAmelCase_ ,      """->""" ,      """ -> """.join(str(F'{v}({w})'	) for v, w in self.adjList[u]	)	)







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    [-1] * self.num_nodes
									# src is the source node
									_SCREAMING_SNAKE_CASE       =    0
									_SCREAMING_SNAKE_CASE       =    PriorityQueue()
									q.insert((0, src)	)  # (dist from src, node)
									for u in self.adjList:
															if u != src:
																					_SCREAMING_SNAKE_CASE       =    sys.maxsize  # Infinity
																					_SCREAMING_SNAKE_CASE       =    -1

									while not q.is_empty():
															_SCREAMING_SNAKE_CASE       =    q.extract_min()  # Returns node with the min dist from source
															# Update the distance of all the neighbours of u and
															# if their prev dist was INFINITY then push them in Q
															for v, w in self.adjList[u]:
																					_SCREAMING_SNAKE_CASE       =    self.dist[u] + w
																					if self.dist[v] > new_dist:
																											if self.dist[v] == sys.maxsize:
																																	q.insert((new_dist, v)	)
																											else:
																																	q.decrease_key((self.dist[v], v) ,      UpperCAmelCase_	)
																											_SCREAMING_SNAKE_CASE       =    new_dist
																											_SCREAMING_SNAKE_CASE       =    u

        # Show the shortest distances from src
									self.show_distances(UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    int	):







									'''simple docstring'''




									print(F'Distance from node: {src}'	)
									for u in range(self.num_nodes	):
															print(F'Node {u} has distance: {self.dist[u]}'	)







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    []
									_SCREAMING_SNAKE_CASE       =    0
									_SCREAMING_SNAKE_CASE       =    dest
									# Backtracking from dest to src
									while self.par[temp] != -1:
															path.append(UpperCAmelCase_	)
															if temp != src:
																					for v, w in self.adjList[temp]:
																											if v == self.par[temp]:
																																	cost += w
																																	break
															_SCREAMING_SNAKE_CASE       =    self.par[temp]
									path.append(UpperCAmelCase_	)
									path.reverse()

									print(F'----Path to reach {dest} from {src}----'	)
									for u in path:
															print(F'{u}' ,      end=""" """	)
															if u != dest:
																					print("""-> """ ,      end=""""""	)

									print("""\nTotal cost of path: """ ,      UpperCAmelCase_	)


if __name__ == "__main__":
		UpperCamelCase												=      Graph(9)
		graph.add_edge(0, 1, 4)
		graph.add_edge(0, 7, 8)
		graph.add_edge(1, 2, 8)
		graph.add_edge(1, 7, 11)
		graph.add_edge(2, 3, 7)
		graph.add_edge(2, 8, 2)
		graph.add_edge(2, 5, 4)
		graph.add_edge(3, 4, 9)
		graph.add_edge(3, 5, 14)
		graph.add_edge(4, 5, 10)
		graph.add_edge(5, 6, 2)
		graph.add_edge(6, 7, 1)
		graph.add_edge(6, 8, 6)
		graph.add_edge(7, 8, 7)
		graph.show_graph()
		graph.dijkstra(0)
		graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21

