
"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[List[PIL.Image.Image], np.ndarray]
       UpperCamelCase_		:					Optional[List[bool]]


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_cycle_diffusion import CycleDiffusionPipeline
      from .pipeline_stable_diffusion import StableDiffusionPipeline
      from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
      from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
      from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
      from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
      from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
      from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
      from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
      from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
      from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
      from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
      from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
      from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
      from .pipeline_stable_unclip import StableUnCLIPPipeline
      from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
      from .safety_checker import StableDiffusionSafetyChecker
      from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
      from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          StableDiffusionDepthaImgPipeline,
          StableDiffusionDiffEditPipeline,
          StableDiffusionPixaPixZeroPipeline,
      )
else:
      from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
      from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
      from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
      if not (
          is_torch_available()
          and is_transformers_available()
          and is_k_diffusion_available()
          and is_k_diffusion_version(""">=""", """0.0.12""")
      ):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
      from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
      if not (is_transformers_available() and is_onnx_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_onnx_objects import *  # noqa F403
else:
      from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
      from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
      from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
      from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
      from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
      import flax



      @flax.struct.dataclass
      class   __a  (      _lowerCAmelCase	):
             UpperCamelCase_		:					np.ndarray
             UpperCamelCase_		:					List[bool]





      from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
      from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
      from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
      from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
      from .safety_checker_flax import FlaxStableDiffusionSafetyChecker







"""simple docstring"""


from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE      =     TypeVar("""T""")



class   __a  (      Generic[T]	):






       def __init__(  self :							List[Any]       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              UpperCamelCase					 =		data
              UpperCamelCase					 =		self
              UpperCamelCase					 =		0





class   __a  (      Generic[T]	):






       def __init__(  self :							int			)->  None:







              """simple docstring"""
              # map from node name to the node object
              UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # create a new set with x as its member
              UpperCamelCase					 =		DisjointSetTreeNode(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							T			)->  DisjointSetTreeNode[T]:







              """simple docstring"""
              # find the set x belongs to (with path-compression)
              UpperCamelCase					 =		self.map[data]
              if elem_ref != elem_ref.parent:
                     UpperCamelCase					 =		self.find_set(elem_ref.parent.data			)
              return elem_ref.parent






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							DisjointSetTreeNode[T]       ,      UpperCAmelCase_ :							DisjointSetTreeNode[T]			)->  None:







              """simple docstring"""
              # helper function for union operation
              if nodea.rank > nodea.rank:
                     UpperCamelCase					 =		nodea
              else:
                     UpperCamelCase					 =		nodea
                     if nodea.rank == nodea.rank:
                            nodea.rank += 1






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # merge 2 disjoint sets
              self.link(self.find_set(UpperCAmelCase_			)       ,      self.find_set(UpperCAmelCase_			)			)





class   __a  (      Generic[T]	):






       def __init__(  self :							Dict			)->  None:







              """simple docstring"""
              # connections: map from the node to the neighbouring nodes (with weights)
              UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # add a node ONLY if its not present in the graph
              if node not in self.connections:
                     UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							int			)->  None:







              """simple docstring"""
              # add an edge with the given weight
              self.add_node(UpperCAmelCase_			)
              self.add_node(UpperCAmelCase_			)
              UpperCamelCase					 =		weight
              UpperCamelCase					 =		weight






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  GraphUndirectedWeighted[T]:







              """simple docstring"""
              UpperCamelCase					 =		[]
              UpperCamelCase					 =		set()
              for start in self.connections:
                     for end in self.connections[start]:
                            if (start, end) not in seen:
                                   seen.add((end, start)			)
                                   edges.append((start, end, self.connections[start][end])			)
              edges.sort(key=lambda UpperCAmelCase_			: x[2]			)

              # creating the disjoint set
              UpperCamelCase					 =		DisjointSetTree[T]()
              for node in self.connections:
                     disjoint_set.make_set(UpperCAmelCase_			)

              # MST generation
              UpperCamelCase					 =		0
              UpperCamelCase					 =		0
              UpperCamelCase					 =		GraphUndirectedWeighted[T]()
              while num_edges < len(self.connections			) - 1:
                     UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		edges[index]
                     index += 1
                     UpperCamelCase					 =		disjoint_set.find_set(UpperCAmelCase_			)
                     UpperCamelCase					 =		disjoint_set.find_set(UpperCAmelCase_			)
                     if parent_u != parent_v:
                            num_edges += 1
                            graph.add_edge(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                            disjoint_set.union(UpperCAmelCase_       ,      UpperCAmelCase_			)
              return graph





