





'''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







'''simple docstring'''






import timeit


class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase       ):
       if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ):  # Initializes a matrix identical to the one provided.
           A							:							List[Any]				=    arg
           A							:							Dict				=    len(_UpperCAmelCase       )
       else:  # Initializes a square matrix of the given size and set values to zero.
           A							:							str				=    arg
           A							:							Union[str, Any]				=    [[0 for _ in range(self.n       )] for _ in range(self.n       )]


   def __mul__(			self     , _UpperCAmelCase       ):
       A							:							List[str]				=    Matrix(self.n       )
       for i in range(self.n       ):
           for j in range(self.n       ):
               for k in range(self.n       ):
                   matrix.t[i][j] += self.t[i][k] * b.t[k][j]
       return matrix







def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			int		)		->	Union[str, Any]:
    A							:							Tuple				=    Matrix([[1, 0], [0, 1]]		)
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			Any		)		->	Any:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    A							:							Optional[Any]				=    Matrix([[1, 1], [1, 0]]		)
    A							:							int				=    modular_exponentiation(UpperCamelCase__     ,							n - 2		)
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			Any		)		->	List[Any]:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    A							:							str				=    fa
    A							:							List[Any]				=    fa
    n -= 2

    while n > 0:
        A,						A							:							List[str]				=    fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 				_lowerCamelCase(	)		->	Union[str, Any]:
    A							:							Any				=    '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
    A							:							Optional[int]				=    '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
    A							:							List[Any]				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print('''With matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							str				=    '''
from random import randint
from __main__ import simple_fibonacci
'''
    A							:							Any				=    '''simple_fibonacci(randint(1,70000), 1, 1)'''
    A							:							str				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print(
        '''Without matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	Optional[Any]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()
