
'''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      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							Union[str, Any]    						=					{
			    """sld_warmup_steps""": 15,
			    """sld_guidance_scale""": 20,
			    """sld_threshold""": 0.0,
			    """sld_momentum_scale""": 0.0,
			    """sld_mom_beta""": 0.0,
			}
			lowerCamelCase							:							Dict    						=					{
			    """sld_warmup_steps""": 10,
			    """sld_guidance_scale""": 1000,
			    """sld_threshold""": 0.01,
			    """sld_momentum_scale""": 0.3,
			    """sld_mom_beta""": 0.4,
			}
			lowerCamelCase							:							Any    						=					{
			    """sld_warmup_steps""": 7,
			    """sld_guidance_scale""": 2000,
			    """sld_threshold""": 0.025,
			    """sld_momentum_scale""": 0.5,
			    """sld_mom_beta""": 0.7,
			}
			lowerCamelCase							:							Union[str, Any]    						=					{
			    """sld_warmup_steps""": 0,
			    """sld_guidance_scale""": 5000,
			    """sld_threshold""": 1.0,
			    """sld_momentum_scale""": 0.5,
			    """sld_mom_beta""": 0.7,
			}





@dataclass
class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							Union[List[PIL.Image.Image], np.ndarray]
			lowerCamelCase							:							Optional[List[bool]]
			lowerCamelCase							:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
			lowerCamelCase							:							Optional[str]


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








'''simple docstring'''



import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__A       =      logging.get_logger(__name__)

__A       =      {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[str]    						=					"""vit-hybrid"""



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=768    ,       lowerCamelCase__=12    ,       lowerCamelCase__=12    ,       lowerCamelCase__=3_072    ,       lowerCamelCase__="gelu"    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.02    ,       lowerCamelCase__=1e-12    ,       lowerCamelCase__=224    ,       lowerCamelCase__=1    ,       lowerCamelCase__=3    ,       lowerCamelCase__=[1, 1_024, 24, 24]    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Optional[Any]:




									'''simple docstring'''



									super().__init__(**lowerCamelCase__    )

									if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""    )
															lowercase__								=    {
															    """global_padding""": """same""",
															    """layer_type""": """bottleneck""",
															    """depths""": [3, 4, 9],
															    """out_features""": ["""stage3"""],
															    """embedding_dynamic_padding""": True,
															}

									if isinstance(lowerCamelCase__    ,       lowerCamelCase__    ):
															if "model_type" in backbone_config:
																					lowercase__								=    CONFIG_MAPPING[backbone_config["""model_type"""]]
															else:
																					logger.info(
																					    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""    )
																					lowercase__								=    BitConfig
															lowercase__								=    backbone_config_class(**lowerCamelCase__    )

									lowercase__								=    backbone_featmap_shape
									lowercase__								=    backbone_config
									lowercase__								=    hidden_size
									lowercase__								=    num_hidden_layers
									lowercase__								=    num_attention_heads
									lowercase__								=    intermediate_size
									lowercase__								=    hidden_act
									lowercase__								=    hidden_dropout_prob
									lowercase__								=    attention_probs_dropout_prob
									lowercase__								=    initializer_range
									lowercase__								=    layer_norm_eps
									lowercase__								=    image_size
									lowercase__								=    patch_size
									lowercase__								=    num_channels
									lowercase__								=    qkv_bias



			def A__	(   self    )     ->						Dict[str, any]:




									'''simple docstring'''



									lowercase__								=    copy.deepcopy(self.__dict__    )
									lowercase__								=    self.backbone_config.to_dict()
									lowercase__								=    self.__class__.model_type
									return output






