

"""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       _UpperCAmelCase     (						__a):
			__a	:    Any			   =							{
			    """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[Any]			   =							{
			    """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	:    int			   =							{
			    """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	:    Any			   =							{
			    """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       _UpperCAmelCase     (						__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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			:					Any							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





