



"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_UpperCamelCase:					List[Any]     				=	logging.get_logger(__name__)  # pylint: disable=invalid-name


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					['safety_checker', 'feature_extractor']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				AutoencoderKL,  lowerCAmelCase   :				CLIPTextModel,  lowerCAmelCase   :				CLIPTokenizer,  lowerCAmelCase   :				UNetaDConditionModel,  lowerCAmelCase   :				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],  lowerCAmelCase   :				StableDiffusionSafetyChecker,  lowerCAmelCase   :				CLIPImageProcessor,  lowerCAmelCase   :				bool = True,  )     ->							List[Any]:
         super().__init__()
         if hasattr(scheduler.config,  'steps_offset'      ) and scheduler.config.steps_offset != 1:
               lowercase	:   Tuple											= (
                   f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                   f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                   'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
                   ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
                   ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
                   ' file'
               )
               deprecate('steps_offset!=1',  '1.0.0',  lowerCAmelCase,  standard_warn=lowerCAmelCase      )
               lowercase	:   Optional[Any]											= dict(scheduler.config      )
               lowercase	:   List[str]											= 1
               lowercase	:   Union[str, Any]											= FrozenDict(lowerCAmelCase      )

         self.register_modules(
             vae=lowerCAmelCase,  text_encoder=lowerCAmelCase,  tokenizer=lowerCAmelCase,  unet=lowerCAmelCase,  scheduler=lowerCAmelCase,  safety_checker=lowerCAmelCase,  feature_extractor=lowerCAmelCase,  )
         self.register_to_config(requires_safety_checker=lowerCAmelCase      )


   @property
   def 							lowercase					(				self   :				Dict      )     ->							Dict[str, Any]:
         return {k: getattr(self,  lowerCAmelCase      ) for k in self.config.keys() if not k.startswith('_'      )}


   def 							lowercase					(				self   :				Tuple,  lowerCAmelCase   :				Optional[Union[str, int]] = "auto"      )     ->							str:
         if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               lowercase	:   Any											= self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(lowerCAmelCase      )


   def 							lowercase					(				self   :				Tuple      )     ->							List[str]:
         self.enable_attention_slicing(lowerCAmelCase      )


   @torch.no_grad()
   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, List[str]],  lowerCAmelCase   :				Union[torch.FloatTensor, PIL.Image.Image],  lowerCAmelCase   :				Union[torch.FloatTensor, PIL.Image.Image],  lowerCAmelCase   :				float = 0.8,  lowerCAmelCase   :				Optional[int] = 50,  lowerCAmelCase   :				Optional[float] = 7.5,  lowerCAmelCase   :				Optional[Union[str, List[str]]] = None,  lowerCAmelCase   :				Optional[int] = 1,  lowerCAmelCase   :				Optional[float] = 0.0,  lowerCAmelCase   :				Optional[torch.Generator] = None,  lowerCAmelCase   :				Optional[str] = "pil",  lowerCAmelCase   :				bool = True,  lowerCAmelCase   :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None,  lowerCAmelCase   :				int = 1,  )     ->							List[str]:
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionInpaintPipelineLegacy(**self.components      )(
             prompt=lowerCAmelCase,  image=lowerCAmelCase,  mask_image=lowerCAmelCase,  strength=lowerCAmelCase,  num_inference_steps=lowerCAmelCase,  guidance_scale=lowerCAmelCase,  negative_prompt=lowerCAmelCase,  num_images_per_prompt=lowerCAmelCase,  eta=lowerCAmelCase,  generator=lowerCAmelCase,  output_type=lowerCAmelCase,  return_dict=lowerCAmelCase,  callback=lowerCAmelCase,  )


   @torch.no_grad()
   def 							lowercase					(				self   :				Tuple,  lowerCAmelCase   :				Union[str, List[str]],  lowerCAmelCase   :				Union[torch.FloatTensor, PIL.Image.Image],  lowerCAmelCase   :				float = 0.8,  lowerCAmelCase   :				Optional[int] = 50,  lowerCAmelCase   :				Optional[float] = 7.5,  lowerCAmelCase   :				Optional[Union[str, List[str]]] = None,  lowerCAmelCase   :				Optional[int] = 1,  lowerCAmelCase   :				Optional[float] = 0.0,  lowerCAmelCase   :				Optional[torch.Generator] = None,  lowerCAmelCase   :				Optional[str] = "pil",  lowerCAmelCase   :				bool = True,  lowerCAmelCase   :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None,  lowerCAmelCase   :				int = 1,  **lowerCAmelCase   :				int,  )     ->							Tuple:
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionImgaImgPipeline(**self.components      )(
             prompt=lowerCAmelCase,  image=lowerCAmelCase,  strength=lowerCAmelCase,  num_inference_steps=lowerCAmelCase,  guidance_scale=lowerCAmelCase,  negative_prompt=lowerCAmelCase,  num_images_per_prompt=lowerCAmelCase,  eta=lowerCAmelCase,  generator=lowerCAmelCase,  output_type=lowerCAmelCase,  return_dict=lowerCAmelCase,  callback=lowerCAmelCase,  callback_steps=lowerCAmelCase,  )






   @torch.no_grad()
   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				Union[str, List[str]],  lowerCAmelCase   :				int = 512,  lowerCAmelCase   :				int = 512,  lowerCAmelCase   :				int = 50,  lowerCAmelCase   :				float = 7.5,  lowerCAmelCase   :				Optional[Union[str, List[str]]] = None,  lowerCAmelCase   :				Optional[int] = 1,  lowerCAmelCase   :				float = 0.0,  lowerCAmelCase   :				Optional[torch.Generator] = None,  lowerCAmelCase   :				Optional[torch.FloatTensor] = None,  lowerCAmelCase   :				Optional[str] = "pil",  lowerCAmelCase   :				bool = True,  lowerCAmelCase   :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None,  lowerCAmelCase   :				int = 1,  )     ->							List[Any]:
         # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
         return StableDiffusionPipeline(**self.components      )(
             prompt=lowerCAmelCase,  height=lowerCAmelCase,  width=lowerCAmelCase,  num_inference_steps=lowerCAmelCase,  guidance_scale=lowerCAmelCase,  negative_prompt=lowerCAmelCase,  num_images_per_prompt=lowerCAmelCase,  eta=lowerCAmelCase,  generator=lowerCAmelCase,  latents=lowerCAmelCase,  output_type=lowerCAmelCase,  return_dict=lowerCAmelCase,  callback=lowerCAmelCase,  callback_steps=lowerCAmelCase,  )





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
