code: 
  
  
  """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
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						["safety_checker", "feature_extractor"]
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = True				,					)      ->  int:
       super().__init__()
       if hasattr(scheduler.config				,					'''steps_offset'''		) and scheduler.config.steps_offset != 1:
           a					=(
               f'''The configuration file of this scheduler: {scheduler} is outdated.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'''				,					__A				,					standard_warn=__A		)
           a					=dict(scheduler.config		)
           a					=1
           a					=FrozenDict(__A		)
  
       self.register_modules(
           vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					unet=__A				,					scheduler=__A				,					safety_checker=__A				,					feature_extractor=__A				,					)
       self.register_to_config(requires_safety_checker=__A		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict[str, Any]:
       return {k: getattr(self				,					__A		) for k in self.config.keys() if not k.startswith('''_'''		)}
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A = "auto"		)      ->  int:
       if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           a					=self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       self.enable_attention_slicing(__A		)
  
  
   @torch.no_grad()
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A = 0.8				,					__A = 50				,					__A = 7.5				,					__A = None				,					__A = 1				,					__A = 0.0				,					__A = None				,					__A = "pil"				,					__A = True				,					__A = None				,					__A = 1				,					)      ->  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=__A				,					image=__A				,					mask_image=__A				,					strength=__A				,					num_inference_steps=__A				,					guidance_scale=__A				,					negative_prompt=__A				,					num_images_per_prompt=__A				,					eta=__A				,					generator=__A				,					output_type=__A				,					return_dict=__A				,					callback=__A				,					)
  
  
   @torch.no_grad()
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A = 0.8				,					__A = 50				,					__A = 7.5				,					__A = None				,					__A = 1				,					__A = 0.0				,					__A = None				,					__A = "pil"				,					__A = True				,					__A = None				,					__A = 1				,					**__A				,					)      ->  Optional[Any]:
       # 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=__A				,					image=__A				,					strength=__A				,					num_inference_steps=__A				,					guidance_scale=__A				,					negative_prompt=__A				,					num_images_per_prompt=__A				,					eta=__A				,					generator=__A				,					output_type=__A				,					return_dict=__A				,					callback=__A				,					callback_steps=__A				,					)
  
  
  
  
  
   @torch.no_grad()
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = 512				,					__A = 512				,					__A = 50				,					__A = 7.5				,					__A = None				,					__A = 1				,					__A = 0.0				,					__A = None				,					__A = None				,					__A = "pil"				,					__A = True				,					__A = None				,					__A = 1				,					)      ->  Tuple:
       # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
       return StableDiffusionPipeline(**self.components		)(
           prompt=__A				,					height=__A				,					width=__A				,					num_inference_steps=__A				,					guidance_scale=__A				,					negative_prompt=__A				,					num_images_per_prompt=__A				,					eta=__A				,					generator=__A				,					latents=__A				,					output_type=__A				,					return_dict=__A				,					callback=__A				,					callback_steps=__A				,					)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  lowerCamelCase_     :     List[str]    		=						logging.getLogger(__name__)
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self		)      ->  Dict:
       a					=False
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Tuple:
       if not self.initialized:
           a					=RagRetriever(
               __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
           a					=True
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       self.retriever.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       a					=False
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
       a	,   a					=self.retriever._main_retrieve(__A				,					__A		)
       a					=self.retriever.index.get_doc_dicts(__A		)
       return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A=None		)      ->  int:
       if index is not None and index.is_initialized() and len(__A		) > 0:
           raise ValueError(
               '''When using Ray for distributed fine-tuning, '''
               '''you\'ll need to provide the paths instead, '''
               '''as the dataset and the index are loaded '''
               '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''		)
  
       super().__init__(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
  
       a					=retrieval_workers
       a					=question_encoder_tokenizer
       a					=generator_tokenizer
       if len(self.retrieval_workers		) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(__A				,					__A				,					__A				,					__A		)
                   for worker in self.retrieval_workers
               ]		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''initializing retrieval'''		)
  
       if len(self.retrieval_workers		) > 0:
           ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]		)
       else:
           # Non-distributed training. Load index into this same process.
           self.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  Dict:
       if len(self.retrieval_workers		) > 0:
           # Select a random retrieval actor.
           a					=self.retrieval_workers[random.randint(0				,					len(self.retrieval_workers		) - 1		)]
           a	,   a	,   a					=ray.get(
               random_worker.retrieve.remote(__A				,					__A		)		)
       else:
           a	,   a					=self._main_retrieve(__A				,					__A		)
           a					=self.index.get_doc_dicts(__A		)
       return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Tuple:
       return super(__A				,					cls		).get_tokenizers(__A				,					__A				,					**__A		)
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					__A=None				,					**__A		)      ->  Optional[Any]:
       a					=kwargs.pop('''config'''				,					__A		) or RagConfig.from_pretrained(__A				,					**__A		)
       a					=RagTokenizer.from_pretrained(__A				,					config=__A		)
       a					=rag_tokenizer.question_encoder
       a					=rag_tokenizer.generator
  
       if indexed_dataset is not None:
           a					='''custom'''
           a					=CustomHFIndex(config.retrieval_vector_size				,					__A		)
       else:
           a					=cls._build_index(__A		)
  
       return cls(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					retrieval_workers=__A				,					index=__A				,					)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''re-loading the new dataset with embeddings'''		)
       # access from the training loop
  
       ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]		)
  
       # build the index object again
       a					=self._build_index(self.config		)
  
       ray.get(
           [
               worker.create_rag_retriever.remote(
                   self.config				,					self.question_encoder_tokenizer				,					self.generator_tokenizer				,					__A		)
               for worker in self.retrieval_workers
           ]		)