


'''simple docstring'''
UpperCAmelCase_			:  Optional[int]										=   '0.18.2'

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from .utils.dummy_pt_objects import *  # noqa F403
else:
 from .models import (
     AutoencoderKL,
     ControlNetModel,
     ModelMixin,
     PriorTransformer,
     TaFilmDecoder,
     TransformeraDModel,
     UNetaDModel,
     UNetaDConditionModel,
     UNetaDModel,
     UNetaDConditionModel,
     VQModel,
 )
 from .optimization import (
     get_constant_schedule,
     get_constant_schedule_with_warmup,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
     get_scheduler,
 )
 from .pipelines import (
     AudioPipelineOutput,
     ConsistencyModelPipeline,
     DanceDiffusionPipeline,
     DDIMPipeline,
     DDPMPipeline,
     DiffusionPipeline,
     DiTPipeline,
     ImagePipelineOutput,
     KarrasVePipeline,
     LDMPipeline,
     LDMSuperResolutionPipeline,
     PNDMPipeline,
     RePaintPipeline,
     ScoreSdeVePipeline,
 )
 from .schedulers import (
     CMStochasticIterativeScheduler,
     DDIMInverseScheduler,
     DDIMParallelScheduler,
     DDIMScheduler,
     DDPMParallelScheduler,
     DDPMScheduler,
     DEISMultistepScheduler,
     DPMSolverMultistepInverseScheduler,
     DPMSolverMultistepScheduler,
     DPMSolverSinglestepScheduler,
     EulerAncestralDiscreteScheduler,
     EulerDiscreteScheduler,
     HeunDiscreteScheduler,
     IPNDMScheduler,
     KarrasVeScheduler,
     KDPMaAncestralDiscreteScheduler,
     KDPMaDiscreteScheduler,
     PNDMScheduler,
     RePaintScheduler,
     SchedulerMixin,
     ScoreSdeVeScheduler,
     UnCLIPScheduler,
     UniPCMultistepScheduler,
     VQDiffusionScheduler,
 )
 from .training_utils import EMAModel

try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .schedulers import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .schedulers import DPMSolverSDEScheduler

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 .pipelines import (
     AltDiffusionImgaImgPipeline,
     AltDiffusionPipeline,
     AudioLDMPipeline,
     CycleDiffusionPipeline,
     IFImgaImgPipeline,
     IFImgaImgSuperResolutionPipeline,
     IFInpaintingPipeline,
     IFInpaintingSuperResolutionPipeline,
     IFPipeline,
     IFSuperResolutionPipeline,
     ImageTextPipelineOutput,
     KandinskyImgaImgPipeline,
     KandinskyInpaintPipeline,
     KandinskyPipeline,
     KandinskyPriorPipeline,
     KandinskyVaaControlnetImgaImgPipeline,
     KandinskyVaaControlnetPipeline,
     KandinskyVaaImgaImgPipeline,
     KandinskyVaaInpaintPipeline,
     KandinskyVaaPipeline,
     KandinskyVaaPriorEmbaEmbPipeline,
     KandinskyVaaPriorPipeline,
     LDMTextToImagePipeline,
     PaintByExamplePipeline,
     SemanticStableDiffusionPipeline,
     ShapEImgaImgPipeline,
     ShapEPipeline,
     StableDiffusionAttendAndExcitePipeline,
     StableDiffusionControlNetImgaImgPipeline,
     StableDiffusionControlNetInpaintPipeline,
     StableDiffusionControlNetPipeline,
     StableDiffusionDepthaImgPipeline,
     StableDiffusionDiffEditPipeline,
     StableDiffusionImageVariationPipeline,
     StableDiffusionImgaImgPipeline,
     StableDiffusionInpaintPipeline,
     StableDiffusionInpaintPipelineLegacy,
     StableDiffusionInstructPixaPixPipeline,
     StableDiffusionLatentUpscalePipeline,
     StableDiffusionLDMaDPipeline,
     StableDiffusionModelEditingPipeline,
     StableDiffusionPanoramaPipeline,
     StableDiffusionParadigmsPipeline,
     StableDiffusionPipeline,
     StableDiffusionPipelineSafe,
     StableDiffusionPixaPixZeroPipeline,
     StableDiffusionSAGPipeline,
     StableDiffusionUpscalePipeline,
     StableUnCLIPImgaImgPipeline,
     StableUnCLIPPipeline,
     TextToVideoSDPipeline,
     TextToVideoZeroPipeline,
     UnCLIPImageVariationPipeline,
     UnCLIPPipeline,
     UniDiffuserModel,
     UniDiffuserPipeline,
     UniDiffuserTextDecoder,
     VersatileDiffusionDualGuidedPipeline,
     VersatileDiffusionImageVariationPipeline,
     VersatileDiffusionPipeline,
     VersatileDiffusionTextToImagePipeline,
     VideoToVideoSDPipeline,
     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 .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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 .pipelines import StableDiffusionKDiffusionPipeline

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 .pipelines import (
     OnnxStableDiffusionImgaImgPipeline,
     OnnxStableDiffusionInpaintPipeline,
     OnnxStableDiffusionInpaintPipelineLegacy,
     OnnxStableDiffusionPipeline,
     OnnxStableDiffusionUpscalePipeline,
     StableDiffusionOnnxPipeline,
 )

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 .pipelines import AudioDiffusionPipeline, Mel

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 .pipelines import SpectrogramDiffusionPipeline

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from .utils.dummy_flax_objects import *  # noqa F403
else:
 from .models.controlnet_flax import FlaxControlNetModel
 from .models.modeling_flax_utils import FlaxModelMixin
 from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .models.vae_flax import FlaxAutoencoderKL
 from .pipelines import FlaxDiffusionPipeline
 from .schedulers import (
     FlaxDDIMScheduler,
     FlaxDDPMScheduler,
     FlaxDPMSolverMultistepScheduler,
     FlaxKarrasVeScheduler,
     FlaxLMSDiscreteScheduler,
     FlaxPNDMScheduler,
     FlaxSchedulerMixin,
     FlaxScoreSdeVeScheduler,
 )


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 .pipelines import (
     FlaxStableDiffusionControlNetPipeline,
     FlaxStableDiffusionImgaImgPipeline,
     FlaxStableDiffusionInpaintPipeline,
     FlaxStableDiffusionPipeline,
 )

try:
 if not (is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from .utils.dummy_note_seq_objects import *  # noqa F403
else:
 from .pipelines import MidiProcessor










'''simple docstring'''
from __future__ import annotations

import math
import random
from typing import Any

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		list[Any]	  =      []
  __A	:		int	  =      0
  __A	:		int	  =      0


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.head == self.tail


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.data.append(__lowercase    )
  __A	:		Any	  =      self.tail + 1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.data[self.head]
  __A	:		Union[str, Any]	  =      self.head + 1
  return ret


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.tail - self.head







 def 			snake_case__ (   self    ):

  """simple docstring"""







  print(self.data    )
  print('**************'    )
  print(self.data[self.head : self.tail]    )


class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      data
  __A	:		MyNode | None	  =      None
  __A	:		MyNode | None	  =      None
  __A	:		int	  =      1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.data


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.left


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.right


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.height


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      data


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      node


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      node







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      height


def       _lowercase							(					UpperCamelCase__							:       MyNode | None      ):
 if node is None:
  return 0
 return node.get_height()


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 if a > b:
  return a
 return b


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('left rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_left()
 assert ret is not None
 node.set_left(ret.get_right()      )
 ret.set_right(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Optional[Any]	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('right rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_right()
 assert ret is not None
 node.set_right(ret.get_left()      )
 ret.set_left(UpperCamelCase__      )
 __A	:		List[str]	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Any	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Dict	  =      node.get_left()
 assert left_child is not None
 node.set_left(left_rotation(UpperCamelCase__      )      )
 return right_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Tuple	  =      node.get_right()
 assert right_child is not None
 node.set_right(right_rotation(UpperCamelCase__      )      )
 return left_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode | None,					UpperCamelCase__							:       Any      ):
 if node is None:
  return MyNode(UpperCamelCase__      )
 if data < node.get_data():
  node.set_left(insert_node(node.get_left(),					UpperCamelCase__      )      )
  if (
      get_height(node.get_left()      ) - get_height(node.get_right()      ) == 2
  ):  # an unbalance detected
   __A	:		Union[str, Any]	  =      node.get_left()
   assert left_child is not None
   if (
       data < left_child.get_data()
   ):  # new node is the left child of the left child
    __A	:		Optional[Any]	  =      right_rotation(UpperCamelCase__      )
   else:
    __A	:		Any	  =      lr_rotation(UpperCamelCase__      )
 else:
  node.set_right(insert_node(node.get_right(),					UpperCamelCase__      )      )
  if get_height(node.get_right()      ) - get_height(node.get_left()      ) == 2:
   __A	:		Union[str, Any]	  =      node.get_right()
   assert right_child is not None
   if data < right_child.get_data():
    __A	:		int	  =      rl_rotation(UpperCamelCase__      )
   else:
    __A	:		Optional[Any]	  =      left_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 return node


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		str	  =      root.get_right()
  if right_child is None:
   break
  __A	:		Union[str, Any]	  =      right_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		Union[str, Any]	  =      root.get_left()
  if left_child is None:
   break
  __A	:		int	  =      left_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode,					UpperCamelCase__							:       Any      ):
 __A	:		List[Any]	  =      root.get_left()
 __A	:		int	  =      root.get_right()
 if root.get_data() == data:
  if left_child is not None and right_child is not None:
   __A	:		List[Any]	  =      get_left_most(UpperCamelCase__      )
   root.set_data(UpperCamelCase__      )
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
  elif left_child is not None:
   __A	:		Tuple	  =      left_child
  elif right_child is not None:
   __A	:		str	  =      right_child
  else:
   return None
 elif root.get_data() > data:
  if left_child is None:
   print('No such data'      )
   return root
  else:
   root.set_left(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
 else:  # root.get_data() < data
  if right_child is None:
   return root
  else:
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )

 if get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == 2:
  assert right_child is not None
  if get_height(right_child.get_right()      ) > get_height(right_child.get_left()      ):
   __A	:		List[str]	  =      left_rotation(UpperCamelCase__      )
  else:
   __A	:		Union[str, Any]	  =      rl_rotation(UpperCamelCase__      )
 elif get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == -2:
  assert left_child is not None
  if get_height(left_child.get_left()      ) > get_height(left_child.get_right()      ):
   __A	:		str	  =      right_rotation(UpperCamelCase__      )
  else:
   __A	:		List[Any]	  =      lr_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(root.get_right()      ),					get_height(root.get_left()      )      ) + 1
 root.set_height(UpperCamelCase__      )
 return root

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		MyNode | None	  =      None


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return get_height(self.root    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('insert:' + str(__lowercase    )    )
  __A	:		Optional[int]	  =      insert_node(self.root   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('delete:' + str(__lowercase    )    )
  if self.root is None:
   print('Tree is empty!'    )
   return
  __A	:		List[Any]	  =      del_node(self.root   ,   __lowercase    )







 def __str__(   self   ,   ):  # a level traversale, gives a more intuitive look on the tree

  """simple docstring"""







  __A	:		Union[str, Any]	  =      ''
  __A	:		Optional[Any]	  =      MyQueue()
  q.push(self.root    )
  __A	:		Tuple	  =      self.get_height()
  if layer == 0:
   return output
  __A	:		List[Any]	  =      0
  while not q.is_empty():
   __A	:		Union[str, Any]	  =      q.pop()
   __A	:		Any	  =      ' ' * int(math.pow(2   ,   layer - 1    )    )
   output += space
   if node is None:
    output += "*"
    q.push(__lowercase    )
    q.push(__lowercase    )
   else:
    output += str(node.get_data()    )
    q.push(node.get_left()    )
    q.push(node.get_right()    )
   output += space
   __A	:		Optional[int]	  =      cnt + 1
   for i in range(100    ):
    if cnt == math.pow(2   ,   __lowercase    ) - 1:
     __A	:		Optional[int]	  =      layer - 1
     if layer == 0:
      output += "\n*************************************"
      return output
     output += "\n"
     break
  output += "\n*************************************"
  return output


def       _lowercase							(					):
 import doctest

 doctest.testmod()


if __name__ == "__main__":
 _test()
 UpperCAmelCase_			:  Optional[Any]										=   AVLtree()
 UpperCAmelCase_			:  Dict										=   list(range(1_0))
 random.shuffle(lst)
 for i in lst:
  t.insert(i)
  print(str(t))
 random.shuffle(lst)
 for i in lst:
  t.del_node(i)
  print(str(t))






