


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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			self.base_model(
           input_ids=_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    encoder_hidden_states=_lowerCamelCase   ,    encoder_attention_mask=_lowerCamelCase   ,    output_attentions=_lowerCamelCase   ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,    return_dict=_lowerCamelCase   ,    )

       if self.has_pre_transformation:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			self.transformation_pre(_lowerCamelCase       )

             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )
       else:
             a		:List[str]									=			self.transformation(outputs.last_hidden_state       )
             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )





