
'''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     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Optional[Any]								=		{
	    '''sld_warmup_steps''': 15,
	    '''sld_guidance_scale''': 20,
	    '''sld_threshold''': 0.0,
	    '''sld_momentum_scale''': 0.0,
	    '''sld_mom_beta''': 0.0,
	}
	_UpperCamelCase							:  Optional[Any]								=		{
	    '''sld_warmup_steps''': 10,
	    '''sld_guidance_scale''': 1_000,
	    '''sld_threshold''': 0.0_1,
	    '''sld_momentum_scale''': 0.3,
	    '''sld_mom_beta''': 0.4,
	}
	_UpperCamelCase							:  Tuple								=		{
	    '''sld_warmup_steps''': 7,
	    '''sld_guidance_scale''': 2_000,
	    '''sld_threshold''': 0.0_2_5,
	    '''sld_momentum_scale''': 0.5,
	    '''sld_mom_beta''': 0.7,
	}
	_UpperCamelCase							:  Union[str, Any]								=		{
	    '''sld_warmup_steps''': 0,
	    '''sld_guidance_scale''': 5_000,
	    '''sld_threshold''': 1.0,
	    '''sld_momentum_scale''': 0.5,
	    '''sld_mom_beta''': 0.7,
	}







@dataclass
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Union[List[PIL.Image.Image], np.ndarray]
	_UpperCamelCase							:  Optional[List[bool]]
	_UpperCamelCase							:  Optional[Union[List[PIL.Image.Image], np.ndarray]]
	_UpperCamelCase							:  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,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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






