


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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




