


"""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             f'''The configuration file of this scheduler: {scheduler} is outdated.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				,					)



"""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
         ]		)