'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
from ..utils import DummyObject, requires_backends
class AllegroTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AsymmetricAutoencoderKL(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AuraFlowTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderDC(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKL(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLAllegro(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLCogVideoX(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLHunyuanVideo(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLLTXVideo(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLMochi(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLTemporalDecoder(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderOobleck(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderTiny(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoencoderKLSapiensVideoGenVideo(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class CogVideoXTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class CogView3PlusTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ConsistencyDecoderVAE(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ControlNetUnionModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ControlNetXSAdapter(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DiTTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class FluxControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class FluxMultiControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class FluxTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class HunyuanDiT2DControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class HunyuanDiT2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class HunyuanDiT2DMultiControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class HunyuanVideoTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class I2VGenXLUNet(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class Kandinsky3UNet(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class LatteTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class LTXVideoTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class LuminaNextDiT2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class MochiTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ModelMixin(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class MotionAdapter(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class MultiAdapter(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class MultiControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class PixArtTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class PriorTransformer(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SanaTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SAPIControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SAPIMultiControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SAPITransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SAPIPhotoGenControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SAPIPhotoGenMultiControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SAPIPhotoGenTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SapiensImageGenTransformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SapiensVideoGenVideoTransformer3DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SD3ControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SD3MultiControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SD3Transformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SparseControlNetModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class StableAudioDiTModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class T2IAdapter(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class T5FilmDecoder(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class Transformer2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNet1DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNet2DConditionModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNet2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNet3DConditionModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNetControlNetXSModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNetMotionModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UNetSpatioTemporalConditionModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UVit2DModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class VQModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
def get_constant_schedule(*args, **kwargs): requires_backends(get_constant_schedule, ['torch'])
def get_constant_schedule_with_warmup(*args, **kwargs): requires_backends(get_constant_schedule_with_warmup, ['torch'])
def get_cosine_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_schedule_with_warmup, ['torch'])
def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ['torch'])
def get_linear_schedule_with_warmup(*args, **kwargs): requires_backends(get_linear_schedule_with_warmup, ['torch'])
def get_polynomial_decay_schedule_with_warmup(*args, **kwargs): requires_backends(get_polynomial_decay_schedule_with_warmup, ['torch'])
def get_scheduler(*args, **kwargs): requires_backends(get_scheduler, ['torch'])
class AudioPipelineOutput(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoPipelineForImage2Image(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoPipelineForInpainting(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AutoPipelineForText2Image(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class BlipDiffusionControlNetPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class BlipDiffusionPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class CLIPImageProjection(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ConsistencyModelPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DanceDiffusionPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDIMPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDPMPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DiffusionPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DiTPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ImagePipelineOutput(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class KarrasVePipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class LDMPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class LDMSuperResolutionPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class PNDMPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class RePaintPipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ScoreSdeVePipeline(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class StableDiffusionMixin(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DiffusersQuantizer(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class AmusedScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class CMStochasticIterativeScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class CogVideoXDDIMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class CogVideoXDPMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDIMInverseScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDIMParallelScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDIMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDPMParallelScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDPMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DDPMWuerstchenScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DEISMultistepScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DPMSolverMultistepInverseScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DPMSolverMultistepScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class DPMSolverSinglestepScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class EDMDPMSolverMultistepScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class EDMEulerScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class EulerAncestralDiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class EulerDiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class FlowMatchEulerDiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class FlowMatchHeunDiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class HeunDiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class IPNDMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class KarrasVeScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class KDPM2DiscreteScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class LCMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class PNDMScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class RePaintScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SASolverScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class SchedulerMixin(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class ScoreSdeVeScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class TCDScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UnCLIPScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class UniPCMultistepScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class VQDiffusionScheduler(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
class EMAModel(metaclass=DummyObject):
    _backends = ['torch']
    def __init__(self, *args, **kwargs): requires_backends(self, ['torch'])
    @classmethod
    def from_config(cls, *args, **kwargs): requires_backends(cls, ['torch'])
    @classmethod
    def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ['torch'])
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
