




'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 							_snake_case	(    _a  ):
   _A    :       Tuple      				=   {
       '''sld_warmup_steps''': 1_5,
       '''sld_guidance_scale''': 2_0,
       '''sld_threshold''': 0.0,
       '''sld_momentum_scale''': 0.0,
       '''sld_mom_beta''': 0.0,
   }
   _A    :       Optional[int]      				=   {
       '''sld_warmup_steps''': 1_0,
       '''sld_guidance_scale''': 1_0_0_0,
       '''sld_threshold''': 0.01,
       '''sld_momentum_scale''': 0.3,
       '''sld_mom_beta''': 0.4,
   }
   _A    :       List[Any]      				=   {
       '''sld_warmup_steps''': 7,
       '''sld_guidance_scale''': 2_0_0_0,
       '''sld_threshold''': 0.0_25,
       '''sld_momentum_scale''': 0.5,
       '''sld_mom_beta''': 0.7,
   }
   _A    :       Tuple      				=   {
       '''sld_warmup_steps''': 0,
       '''sld_guidance_scale''': 5_0_0_0,
       '''sld_threshold''': 1.0,
       '''sld_momentum_scale''': 0.5,
       '''sld_mom_beta''': 0.7,
   }





@dataclass
class 							_snake_case	(    _a  ):
   _A    :       Union[List[PIL.Image.Image], np.ndarray]
   _A    :       Optional[List[bool]]
   _A    :       Optional[Union[List[PIL.Image.Image], np.ndarray]]
   _A    :       Optional[str]


if is_transformers_available() and is_torch_available():
    from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
    from .safety_checker import SafeStableDiffusionSafetyChecker









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_      =							{"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_      =							[
        "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "YosoForMaskedLM",
        "YosoForMultipleChoice",
        "YosoForQuestionAnswering",
        "YosoForSequenceClassification",
        "YosoForTokenClassification",
        "YosoLayer",
        "YosoModel",
        "YosoPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yoso import (
            YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
            YosoForMaskedLM,
            YosoForMultipleChoice,
            YosoForQuestionAnswering,
            YosoForSequenceClassification,
            YosoForTokenClassification,
            YosoLayer,
            YosoModel,
            YosoPreTrainedModel,
        )


else:
    import sys

    A_      =							_LazyModule(__name__, globals()["__file__"], _import_structure)






