






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 __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   {
	    "sld_warmup_steps": 15,
	    "sld_guidance_scale": 20,
	    "sld_threshold": 0.0,
	    "sld_momentum_scale": 0.0,
	    "sld_mom_beta": 0.0,
	}
	UpperCamelCase__			=   {
	    "sld_warmup_steps": 10,
	    "sld_guidance_scale": 1000,
	    "sld_threshold": 0.01,
	    "sld_momentum_scale": 0.3,
	    "sld_mom_beta": 0.4,
	}
	UpperCamelCase__			=   {
	    "sld_warmup_steps": 7,
	    "sld_guidance_scale": 2000,
	    "sld_threshold": 0.025,
	    "sld_momentum_scale": 0.5,
	    "sld_mom_beta": 0.7,
	}
	UpperCamelCase__			=   {
	    "sld_warmup_steps": 0,
	    "sld_guidance_scale": 5000,
	    "sld_threshold": 1.0,
	    "sld_momentum_scale": 0.5,
	    "sld_mom_beta": 0.7,
	}





@dataclass
class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   42
	UpperCamelCase__			=   42
	UpperCamelCase__			=   42
	UpperCamelCase__			=   42


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

		_a   			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
