

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_   (      _a						):


    '''simple docstring'''






    a__     							=       {
        'sld_warmup_steps': 15,
        'sld_guidance_scale': 20,
        'sld_threshold': 0.0,
        'sld_momentum_scale': 0.0,
        'sld_mom_beta': 0.0,
    }
    a__     							=       {
        'sld_warmup_steps': 10,
        'sld_guidance_scale': 10_00,
        'sld_threshold': 0.01,
        'sld_momentum_scale': 0.3,
        'sld_mom_beta': 0.4,
    }
    a__     							=       {
        'sld_warmup_steps': 7,
        'sld_guidance_scale': 20_00,
        'sld_threshold': 0.0_25,
        'sld_momentum_scale': 0.5,
        'sld_mom_beta': 0.7,
    }
    a__     							=       {
        'sld_warmup_steps': 0,
        'sld_guidance_scale': 50_00,
        'sld_threshold': 1.0,
        'sld_momentum_scale': 0.5,
        'sld_mom_beta': 0.7,
    }






@dataclass
class 					A_   (      _a						):


    '''simple docstring'''






    a__     							=       42
    a__     							=       42
    a__     							=       42
    a__     							=       42


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








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


A_		:			int										=   logging.get_logger(__name__)
class 					A_   (      _a						):


    '''simple docstring'''






    a__     							=       "speech-encoder-decoder"
    a__     							=       True


    def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
        super().__init__(**lowercase__  )
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )

        __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
        __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
        __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
        __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )

        __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
        __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
        __UpperCAmelCase									=			True


    @classmethod
    def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
        logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
        __UpperCAmelCase									=			True
        __UpperCAmelCase									=			True

        return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )






    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
        __UpperCAmelCase									=			self.encoder.to_dict()
        __UpperCAmelCase									=			self.decoder.to_dict()
        __UpperCAmelCase									=			self.__class__.model_type
        return output




