

from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .scheduling_consistency_models import CMStochasticIterativeScheduler
     from .scheduling_ddim import DDIMScheduler
     from .scheduling_ddim_inverse import DDIMInverseScheduler
     from .scheduling_ddim_parallel import DDIMParallelScheduler
     from .scheduling_ddpm import DDPMScheduler
     from .scheduling_ddpm_parallel import DDPMParallelScheduler
     from .scheduling_deis_multistep import DEISMultistepScheduler
     from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
     from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
     from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
     from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
     from .scheduling_euler_discrete import EulerDiscreteScheduler
     from .scheduling_heun_discrete import HeunDiscreteScheduler
     from .scheduling_ipndm import IPNDMScheduler
     from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
     from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler
     from .scheduling_repaint import RePaintScheduler
     from .scheduling_sde_ve import ScoreSdeVeScheduler
     from .scheduling_sde_vp import ScoreSdeVpScheduler
     from .scheduling_unclip import UnCLIPScheduler
     from .scheduling_unipc_multistep import UniPCMultistepScheduler
     from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
     from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .scheduling_ddim_flax import FlaxDDIMScheduler
     from .scheduling_ddpm_flax import FlaxDDPMScheduler
     from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
     from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
     from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
     from .scheduling_pndm_flax import FlaxPNDMScheduler
     from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
     from .scheduling_utils_flax import (
         FlaxKarrasDiffusionSchedulers,
         FlaxSchedulerMixin,
         FlaxSchedulerOutput,
         broadcast_to_shape_from_left,
     )


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 .scheduling_lms_discrete 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 .scheduling_dpmsolver_sde import DPMSolverSDEScheduler







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

