





'''simple docstring'''

import math
import sys
class        __lowerCAmelCase  :






       """simple docstring"""





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

            '''simple docstring'''




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





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

            '''simple docstring'''




            return self.cur_size == 0





       def snake_case__		(							self     :	int ,				lowerCAmelCase__     :	Optional[int]  )					->  Tuple:

            '''simple docstring'''




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





       def snake_case__		(							self     :	Any ,				lowerCAmelCase__     :	List[str]  )					->  int:

            '''simple docstring'''




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





       def snake_case__		(							self     :	List[str]  )					->  int:

            '''simple docstring'''




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





       def snake_case__		(							self     :	Dict ,				lowerCAmelCase__     :	Dict  )					->  Any:

            '''simple docstring'''




            return 2 * i + 1





       def snake_case__		(							self     :	Optional[int] ,				lowerCAmelCase__     :	Optional[Any]  )					->  Optional[Any]:

            '''simple docstring'''




            return 2 * i + 2





       def snake_case__		(							self     :	Dict ,				lowerCAmelCase__     :	List[str]  )					->  Any:

            '''simple docstring'''




            return math.floor(i / 2  )





       def snake_case__		(							self     :	Optional[Any] ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	List[Any]  )					->  Tuple:

            '''simple docstring'''




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







       def snake_case__		(							self     :	List[Any] ,				lowerCAmelCase__     :	List[Any] ,				lowerCAmelCase__     :	Optional[int]  )					->  Optional[int]:

            '''simple docstring'''




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

class        __lowerCAmelCase  :






       """simple docstring"""





       def __init__(							self     :	Optional[int] ,				lowerCAmelCase__     :	List[Any]  )					->  Dict:

            '''simple docstring'''




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





       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	List[str] ,				lowerCAmelCase__     :	Any ,				lowerCAmelCase__     :	int  )					->  Optional[Any]:

            '''simple docstring'''




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

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





       def snake_case__		(							self     :	str  )					->  str:

            '''simple docstring'''




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





       def snake_case__		(							self     :	Tuple ,				lowerCAmelCase__     :	str  )					->  List[str]:

            '''simple docstring'''




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

            while not q.is_empty():
                 _UpperCamelCase       					= 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]:
                      _UpperCamelCase       					= 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) ,				lowerCAmelCase__  )
                           _UpperCamelCase       					= new_dist
                           _UpperCamelCase       					= u

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





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

            '''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 snake_case__		(							self     :	int ,				lowerCAmelCase__     :	Dict ,				lowerCAmelCase__     :	Optional[int]  )					->  int:

            '''simple docstring'''




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


if __name__ == "__main__":
   lowercase__		:    Union[str, 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










'''simple docstring'''

from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .consistency_models import ConsistencyModelPipeline
   from .dance_diffusion import DanceDiffusionPipeline
   from .ddim import DDIMPipeline
   from .ddpm import DDPMPipeline
   from .dit import DiTPipeline
   from .latent_diffusion import LDMSuperResolutionPipeline
   from .latent_diffusion_uncond import LDMPipeline
   from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
   from .pndm import PNDMPipeline
   from .repaint import RePaintPipeline
   from .score_sde_ve import ScoreSdeVePipeline
   from .stochastic_karras_ve import KarrasVePipeline

try:
   if not (is_torch_available() and is_librosa_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
   from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
   if not (is_torch_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
   from .audioldm import AudioLDMPipeline
   from .controlnet import (
       StableDiffusionControlNetImgaImgPipeline,
       StableDiffusionControlNetInpaintPipeline,
       StableDiffusionControlNetPipeline,
   )
   from .deepfloyd_if import (
       IFImgaImgPipeline,
       IFImgaImgSuperResolutionPipeline,
       IFInpaintingPipeline,
       IFInpaintingSuperResolutionPipeline,
       IFPipeline,
       IFSuperResolutionPipeline,
   )
   from .kandinsky import (
       KandinskyImgaImgPipeline,
       KandinskyInpaintPipeline,
       KandinskyPipeline,
       KandinskyPriorPipeline,
   )
   from .kandinskya_a import (
       KandinskyVaaControlnetImgaImgPipeline,
       KandinskyVaaControlnetPipeline,
       KandinskyVaaImgaImgPipeline,
       KandinskyVaaInpaintPipeline,
       KandinskyVaaPipeline,
       KandinskyVaaPriorEmbaEmbPipeline,
       KandinskyVaaPriorPipeline,
   )
   from .latent_diffusion import LDMTextToImagePipeline
   from .paint_by_example import PaintByExamplePipeline
   from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
   from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
   from .stable_diffusion import (
       CycleDiffusionPipeline,
       StableDiffusionAttendAndExcitePipeline,
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionImageVariationPipeline,
       StableDiffusionImgaImgPipeline,
       StableDiffusionInpaintPipeline,
       StableDiffusionInpaintPipelineLegacy,
       StableDiffusionInstructPixaPixPipeline,
       StableDiffusionLatentUpscalePipeline,
       StableDiffusionLDMaDPipeline,
       StableDiffusionModelEditingPipeline,
       StableDiffusionPanoramaPipeline,
       StableDiffusionParadigmsPipeline,
       StableDiffusionPipeline,
       StableDiffusionPixaPixZeroPipeline,
       StableDiffusionSAGPipeline,
       StableDiffusionUpscalePipeline,
       StableUnCLIPImgaImgPipeline,
       StableUnCLIPPipeline,
   )
   from .stable_diffusion_safe import StableDiffusionPipelineSafe
   from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
   from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
   from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
   from .versatile_diffusion import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
   from .vq_diffusion import VQDiffusionPipeline


try:
   if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
   from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
   if not is_onnx_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_onnx_objects import *  # noqa F403
else:
   from .onnx_utils import OnnxRuntimeModel

try:
   if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
   from .stable_diffusion import (
       OnnxStableDiffusionImgaImgPipeline,
       OnnxStableDiffusionInpaintPipeline,
       OnnxStableDiffusionInpaintPipelineLegacy,
       OnnxStableDiffusionPipeline,
       OnnxStableDiffusionUpscalePipeline,
       StableDiffusionOnnxPipeline,
   )

try:
   if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
   if not (is_flax_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
   from .controlnet import FlaxStableDiffusionControlNetPipeline
   from .stable_diffusion import (
       FlaxStableDiffusionImgaImgPipeline,
       FlaxStableDiffusionInpaintPipeline,
       FlaxStableDiffusionPipeline,
   )
try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline



