



"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__a           =							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							lowerCamelCase    (     a__							):






					'''simple docstring'''





					_A						:     List[Any]		=	["""safety_checker""", """feature_extractor"""]






					def __init__(			self:				Any		,	snake_case:				Tuple		,	snake_case:				str		,	snake_case:				Optional[int]		,	snake_case:				Tuple		,	snake_case:				Any		,	snake_case:				List[Any]		,	snake_case:				Optional[int]		,	snake_case:				int = True		,	)  ->      List[Any]:
											super().__init__()
											if hasattr(scheduler.config		,	"""steps_offset"""		) and scheduler.config.steps_offset != 1:
																	snake_case_					:str		=     (
																	    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																	    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																	    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
																	    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
																	    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
																	    " file"
																	)
																	deprecate("""steps_offset!=1"""		,	"""1.0.0"""		,	__A		,	standard_warn=__A		)
																	snake_case_					:Union[str, Any]		=     dict(scheduler.config		)
																	snake_case_					:Tuple		=     1
																	snake_case_					:List[str]		=     FrozenDict(__A		)

											self.register_modules(
											    vae=__A		,	text_encoder=__A		,	tokenizer=__A		,	unet=__A		,	scheduler=__A		,	safety_checker=__A		,	feature_extractor=__A		,	)
											self.register_to_config(requires_safety_checker=__A		)






					@property
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Dict[str, Any]:
											return {k: getattr(self		,	__A		) for k in self.config.keys() if not k.startswith("""_"""		)}






					def   lowerCAmelCase_							(			self:				List[Any]		,	snake_case:				Dict = "auto"		)  ->      Tuple:







											if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	snake_case_					:Tuple		=     self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(__A		)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      int:
											self.enable_attention_slicing(__A		)






					@torch.no_grad()
					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Tuple		,	snake_case:				Any		,	snake_case:				Dict		,	snake_case:				Dict = 0.8		,	snake_case:				int = 50		,	snake_case:				int = 7.5		,	snake_case:				Optional[Any] = None		,	snake_case:				List[str] = 1		,	snake_case:				Optional[Any] = 0.0		,	snake_case:				Tuple = None		,	snake_case:				Optional[Any] = "pil"		,	snake_case:				Optional[int] = True		,	snake_case:				Union[str, Any] = None		,	snake_case:				Optional[int] = 1		,	)  ->      int:
											return StableDiffusionInpaintPipelineLegacy(**self.components		)(
											    prompt=__A		,	image=__A		,	mask_image=__A		,	strength=__A		,	num_inference_steps=__A		,	guidance_scale=__A		,	negative_prompt=__A		,	num_images_per_prompt=__A		,	eta=__A		,	generator=__A		,	output_type=__A		,	return_dict=__A		,	callback=__A		,	)






					@torch.no_grad()
					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				Union[str, Any]		,	snake_case:				Optional[int]		,	snake_case:				Union[str, Any] = 0.8		,	snake_case:				Tuple = 50		,	snake_case:				Union[str, Any] = 7.5		,	snake_case:				Dict = None		,	snake_case:				List[str] = 1		,	snake_case:				Any = 0.0		,	snake_case:				Dict = None		,	snake_case:				List[Any] = "pil"		,	snake_case:				int = True		,	snake_case:				int = None		,	snake_case:				str = 1		,	**snake_case:				Tuple		,	)  ->      Optional[int]:
											return StableDiffusionImgaImgPipeline(**self.components		)(
											    prompt=__A		,	image=__A		,	strength=__A		,	num_inference_steps=__A		,	guidance_scale=__A		,	negative_prompt=__A		,	num_images_per_prompt=__A		,	eta=__A		,	generator=__A		,	output_type=__A		,	return_dict=__A		,	callback=__A		,	callback_steps=__A		,	)






					@torch.no_grad()
					def   lowerCAmelCase_							(			self:				str		,	snake_case:				Optional[int]		,	snake_case:				Union[str, Any] = 512		,	snake_case:				Optional[int] = 512		,	snake_case:				Any = 50		,	snake_case:				List[Any] = 7.5		,	snake_case:				Any = None		,	snake_case:				Any = 1		,	snake_case:				Optional[int] = 0.0		,	snake_case:				Any = None		,	snake_case:				int = None		,	snake_case:				Optional[int] = "pil"		,	snake_case:				str = True		,	snake_case:				List[Any] = None		,	snake_case:				List[Any] = 1		,	)  ->      List[str]:
											return StableDiffusionPipeline(**self.components		)(
											    prompt=__A		,	height=__A		,	width=__A		,	num_inference_steps=__A		,	guidance_scale=__A		,	negative_prompt=__A		,	num_images_per_prompt=__A		,	eta=__A		,	generator=__A		,	latents=__A		,	output_type=__A		,	return_dict=__A		,	callback=__A		,	callback_steps=__A		,	)




'''simple docstring'''




import math
import sys





class _snake_case :
							"""simple docstring"""





							def __init__( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   int           =       0
											__lowerCamelCase		:   str           =       []
											__lowerCamelCase		:   int           =       {}  # To store the pos of node in array







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



											"""simple docstring"""







											return self.cur_size == 0







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



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       self.left(__A						)
											__lowerCamelCase		:   Any           =       self.right(__A						)
											if lc < self.cur_size and self.array(__A						)[0] < self.array(__A						)[0]:
															__lowerCamelCase		:   Dict           =       lc
											else:
															__lowerCamelCase		:   List[Any]           =       idx
											if rc < self.cur_size and self.array(__A						)[0] < self.array(__A						)[0]:
															__lowerCamelCase		:   List[str]           =       rc
											if smallest != idx:
															self.swap(__A      ,					__A						)
															self.min_heapify(__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[Any]           =       self.cur_size
											self.cur_size += 1
											self.array.append((sys.maxsize, tup[1])						)
											self.decrease_key((sys.maxsize, tup[1])      ,					tup[0]						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       self.array[0][1]
											__lowerCamelCase		:   str           =       self.array[self.cur_size - 1]
											self.cur_size -= 1
											self.min_heapify(1						)
											del self.pos[min_node]
											return min_node







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->int:



											"""simple docstring"""







											return 2 * i + 1







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







											return 2 * i + 2







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Tuple:



											"""simple docstring"""







											return math.floor(i / 2						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       j
											__lowerCamelCase		:   str           =       i
											__lowerCamelCase		:   Dict           =       self.array[i]
											__lowerCamelCase		:   Union[str, Any]           =       self.array[j]
											__lowerCamelCase		:   str           =       temp







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   str           =       self.pos[tup[1]]
											# assuming the new_d is atmost old_d
											__lowerCamelCase		:   Union[str, Any]           =       (new_d, tup[1])
											while idx > 0 and self.array[self.par(__A						)][0] > self.array[idx][0]:
															self.swap(__A      ,					self.par(__A						)						)
															__lowerCamelCase		:   List[Any]           =       self.par(__A						)





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->Any:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







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

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







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Tuple:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->int:



											"""simple docstring"""







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

											while not q.is_empty():
															__lowerCamelCase		:   int           =       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]:
																			__lowerCamelCase		:   str           =       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)      ,					__A						)
																							__lowerCamelCase		:   str           =       new_dist
																							__lowerCamelCase		:   Tuple           =       u

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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->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 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       []
											__lowerCamelCase		:   Tuple           =       0
											__lowerCamelCase		:   List[str]           =       dest
											# Backtracking from dest to src
											while self.par[temp] != -1:
															path.append(__A						)
															if temp != src:
																			for v, w in self.adjList[temp]:
																							if v == self.par[temp]:
																											cost += w
																											break
															__lowerCamelCase		:   int           =       self.par[temp]
											path.append(__A						)
											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: "      ,					__A						)


if __name__ == "__main__":
				__lowercase	:   Optional[Any]   		=					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





