
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





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     key
							lowerCAmelCase_      =     freq




				def __str__( self					)    ->				Any:
							return f"""Node(key={self.key}, freq={self.freq})"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			if i > j or i < 0 or j > len(__lowerCAmelCase    ) - 1:
						return

			lowerCAmelCase_      =     root[i][j]
			if parent == -1:  # root does not have a parent
						print(F"""{key[node]} is the root of the binary search tree."""    )
			elif is_left:
						print(F"""{key[node]} is the left child of key {parent}."""    )
			else:
						print(F"""{key[node]} is the right child of key {parent}."""    )

			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							node - 1   ,							key[node]   ,							__lowerCAmelCase    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							node + 1   ,							__lowerCAmelCase   ,							key[node]   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			nodes.sort(key=lambda __lowerCAmelCase    : node.key    )

			lowerCAmelCase_      =     len(__lowerCAmelCase    )

			lowerCAmelCase_      =     [nodes[i].key for i in range(__lowerCAmelCase    )]
			lowerCAmelCase_      =     [nodes[i].freq for i in range(__lowerCAmelCase    )]

			# This 2D array stores the overall tree cost (which's as minimized as possible);
			# for a single key, cost is equal to frequency of the key.
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
			# array
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# stores tree roots that will be used later for constructing binary search tree
			lowerCAmelCase_      =     [[i if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]

			for interval_length in range(2   ,							n + 1    ):
						for i in range(n - interval_length + 1    ):
									lowerCAmelCase_      =     i + interval_length - 1

									lowerCAmelCase_      =     sys.maxsize  # set the value to "infinity"
									lowerCAmelCase_      =     total[i][j - 1] + freqs[j]

									# Apply Knuth's optimization
									# Loop without optimization: for r in range(i, j + 1):
									for r in range(root[i][j - 1]   ,							root[i + 1][j] + 1    ):  # r is a temporal root
												lowerCAmelCase_      =     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
												lowerCAmelCase_      =     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
												lowerCAmelCase_      =     left + total[i][j] + right

												if dp[i][j] > cost:
															lowerCAmelCase_      =     cost
															lowerCAmelCase_      =     r

			print("Binary search tree nodes:"    )
			for node in nodes:
						print(__lowerCAmelCase    )

			print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							0   ,							n - 1   ,							-1   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     [Node(__lowerCAmelCase   ,							randint(1   ,							50    )    ) for i in range(10   ,							0   ,							-1    )]
			find_optimal_binary_search_tree(__lowerCAmelCase    )


if __name__ == "__main__":
			main()



