

'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


__a						:List[Any]            = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__a						:Dict            = 'cuda' if torch.cuda.is_available() else 'cpu'

def 			__snake_case	(   __UpperCamelCase  :   str							,__UpperCamelCase  :   List[Any]=100							,__UpperCamelCase  :   Optional[int]=" "						):



      """simple docstring"""

      A_	     =							text.split(__UpperCamelCase						)
      return [character.join(text[i : i + n]						).strip() for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						)]

def 			__snake_case	(   __UpperCamelCase  :   dict						):



      """simple docstring"""

      A_     ,    A_	     =							[], []
      for title, text in zip(documents["title"]							,documents["text"]						):
            if text is not None:
                  for passage in split_text(__UpperCamelCase						):
                        titles.append(title if title is not None else ""						)
                        texts.append(__UpperCamelCase						)
      return {"title": titles, "text": texts}

def 			__snake_case	(   __UpperCamelCase  :   dict							,__UpperCamelCase  :   DPRContextEncoder							,__UpperCamelCase  :   DPRContextEncoderTokenizerFast						):



      """simple docstring"""

      A_	     =							ctx_tokenizer(
          documents["title"]							,documents["text"]							,truncation=__UpperCamelCase							,padding="longest"							,return_tensors="pt"						)["input_ids"]
      A_	     =							ctx_encoder(input_ids.to(device=__UpperCamelCase						)							,return_dict=__UpperCamelCase						).pooler_output
      return {"embeddings": embeddings.detach().cpu().numpy()}

def 			__snake_case	(   __UpperCamelCase  :   "RagExampleArguments"							,__UpperCamelCase  :   "ProcessingArguments"							,__UpperCamelCase  :   "IndexHnswArguments"							,):



      """simple docstring"""

      logger.info("Step 1 - Create the dataset"						)
      ######################################

      # The dataset needed for RAG must have three columns:
      # - title (string): title of the document
      # - text (string): text of a passage of the document
      # - embeddings (array of dimension d): DPR representation of the passage

      # Let's say you have documents in tab-separated csv files with columns "title" and "text"
      assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

      # You can load a Dataset object this way
      A_	     =							load_dataset(
          "csv"							,data_files=[rag_example_args.csv_path]							,split="train"							,delimiter="\t"							,column_names=["title", "text"]						)

      # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

      # Then split the documents into passages of 100 words
      A_	     =							dataset.map(__UpperCamelCase							,batched=__UpperCamelCase							,num_proc=processing_args.num_proc						)

      # And compute the embeddings
      A_	     =							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__UpperCamelCase						)
      A_	     =							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
      A_	     =							Features(
          {"text": Value("string"						), "title": Value("string"						), "embeddings": Sequence(Value("float32"						)						)}						)  # optional, save as float32 instead of float64 to save space
      A_	     =							dataset.map(
          partial(__UpperCamelCase							,ctx_encoder=__UpperCamelCase							,ctx_tokenizer=__UpperCamelCase						)							,batched=__UpperCamelCase							,batch_size=processing_args.batch_size							,features=__UpperCamelCase							,)

      # And finally save your dataset
      A_	     =							os.path.join(rag_example_args.output_dir							,"my_knowledge_dataset"						)
      dataset.save_to_disk(__UpperCamelCase						)
      # from datasets import load_from_disk
      # dataset = load_from_disk(passages_path)  # to reload the dataset

      ######################################
      logger.info("Step 2 - Index the dataset"						)
      ######################################

      # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
      A_	     =							faiss.IndexHNSWFlat(index_hnsw_args.d							,index_hnsw_args.m							,faiss.METRIC_INNER_PRODUCT						)
      dataset.add_faiss_index("embeddings"							,custom_index=__UpperCamelCase						)

      # And save the index
      A_	     =							os.path.join(rag_example_args.output_dir							,"my_knowledge_dataset_hnsw_index.faiss"						)
      dataset.get_index("embeddings"						).save(__UpperCamelCase						)
      # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

      ######################################
      logger.info("Step 3 - Load RAG"						)
      ######################################

      # Easy way to load the model
      A_	     =							RagRetriever.from_pretrained(
          rag_example_args.rag_model_name							,index_name="custom"							,indexed_dataset=__UpperCamelCase						)
      A_	     =							RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,retriever=__UpperCamelCase						)
      A_	     =							RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

      # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
      # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

      ######################################
      logger.info("Step 4 - Have fun"						)
      ######################################

      A_	     =							rag_example_args.question or "What does Moses' rod turn into ?"
      A_	     =							tokenizer.question_encoder(__UpperCamelCase							,return_tensors="pt"						)["input_ids"]
      A_	     =							model.generate(__UpperCamelCase						)
      A_	     =							tokenizer.batch_decode(__UpperCamelCase							,skip_special_tokens=__UpperCamelCase						)[0]
      logger.info("Q: " + question						)
      logger.info("A: " + generated_string						)

@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: str					=					field(
          default=str(Path(snake_case_							).parent / 'test_data' / 'my_knowledge_dataset.csv'							)    ,							metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}    ,							)
      _lowerCamelCase					: Optional[str]					=					field(
          default=snake_case_    ,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}    ,							)
      _lowerCamelCase					: str					=					field(
          default='facebook/rag-sequence-nq'    ,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}    ,							)
      _lowerCamelCase					: str					=					field(
          default='facebook/dpr-ctx_encoder-multiset-base'    ,							metadata={
              'help': (
                  'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
                  ' \'facebook/dpr-ctx_encoder-multiset-base\''
              )
          }    ,							)
      _lowerCamelCase					: Optional[str]					=					field(
          default=snake_case_    ,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}    ,							)




@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: Optional[int]					=					field(
          default=snake_case_    ,							metadata={
              'help': 'The number of processes to use to split the documents into passages. Default is single process.'
          }    ,							)
      _lowerCamelCase					: int					=					field(
          default=1_6    ,							metadata={
              'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
          }    ,							)




@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: int					=					field(
          default=7_6_8    ,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}    ,							)
      _lowerCamelCase					: int					=					field(
          default=1_2_8    ,							metadata={
              'help': (
                  'The number of bi-directional links created for every new element during the HNSW index construction.'
              )
          }    ,							)


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      __a						:Optional[Any]            = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __a						:Optional[int]            = parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            __a						:List[Any]            = rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput

class      _a				( nn.Module							):




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							str	, UpperCAmelCase       :							str=1.0  ):
            super().__init__()

            A_	     =							cut_size
            A_	     =							cut_power







      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]  ):
            A_     ,    A_	     =							pixel_values.shape[2:4]
            A_	     =							min(UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							min(UpperCAmelCase	, UpperCAmelCase	, self.cut_size  )
            A_	     =							[]
            for _ in range(UpperCAmelCase  ):
                  A_	     =							int(torch.rand([]  ) ** self.cut_power * (max_size - min_size) + min_size  )
                  A_	     =							torch.randint(0	, sideX - size + 1	, ()  )
                  A_	     =							torch.randint(0	, sideY - size + 1	, ()  )
                  A_	     =							pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                  cutouts.append(F.adaptive_avg_poolad(UpperCAmelCase	, self.cut_size  )  )
            return torch.cat(UpperCAmelCase  )




def 			__snake_case	(   __UpperCamelCase  :   Dict							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							F.normalize(__UpperCamelCase							,dim=-1						)
      A_	     =							F.normalize(__UpperCamelCase							,dim=-1						)
      return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)

def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      for param in model.parameters():
            A_	     =							value



class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							AutoencoderKL	, UpperCAmelCase       :							CLIPTextModel	, UpperCAmelCase       :							CLIPModel	, UpperCAmelCase       :							CLIPTokenizer	, UpperCAmelCase       :							UNetaDConditionModel	, UpperCAmelCase       :							Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]	, UpperCAmelCase       :							CLIPImageProcessor	, ):
            super().__init__()
            self.register_modules(
                vae=UpperCAmelCase	, text_encoder=UpperCAmelCase	, clip_model=UpperCAmelCase	, tokenizer=UpperCAmelCase	, unet=UpperCAmelCase	, scheduler=UpperCAmelCase	, feature_extractor=UpperCAmelCase	, )

            A_	     =							transforms.Normalize(mean=feature_extractor.image_mean	, std=feature_extractor.image_std  )
            A_	     =							(
                feature_extractor.size
                if isinstance(feature_extractor.size	, UpperCAmelCase  )
                else feature_extractor.size["shortest_edge"]
            )
            A_	     =							MakeCutouts(self.cut_out_size  )

            set_requires_grad(self.text_encoder	, UpperCAmelCase  )
            set_requires_grad(self.clip_model	, UpperCAmelCase  )





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Optional[Union[str, int]] = "auto"  ):
            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(UpperCAmelCase  )





      def 		__A  (      self       :							Tuple  ):
            self.enable_attention_slicing(UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            set_requires_grad(self.vae	, UpperCAmelCase  )





      def 		__A  (      self       :							Dict  ):
            set_requires_grad(self.vae	, UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            set_requires_grad(self.unet	, UpperCAmelCase  )





      def 		__A  (      self       :							Tuple  ):
            set_requires_grad(self.unet	, UpperCAmelCase  )





      @torch.enable_grad()
      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Tuple=True	, ):
            A_	     =							latents.detach().requires_grad_()

            A_	     =							self.scheduler.scale_model_input(UpperCAmelCase	, UpperCAmelCase  )

            # predict the noise residual
            A_	     =							self.unet(UpperCAmelCase	, UpperCAmelCase	, encoder_hidden_states=UpperCAmelCase  ).sample

            if isinstance(self.scheduler	, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
                  A_	     =							self.scheduler.alphas_cumprod[timestep]
                  A_	     =							1 - alpha_prod_t
                  # compute predicted original sample from predicted noise also called
                  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                  A_	     =							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                  A_	     =							torch.sqrt(UpperCAmelCase  )
                  A_	     =							pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler	, UpperCAmelCase  ):
                  A_	     =							self.scheduler.sigmas[index]
                  A_	     =							latents - sigma * noise_pred
            else:
                  raise ValueError(f'''scheduler type {type(self.scheduler  )} not supported'''  )

            A_	     =							1 / self.vae.config.scaling_factor * sample
            A_	     =							self.vae.decode(UpperCAmelCase  ).sample
            A_	     =							(image / 2 + 0.5).clamp(0	, 1  )

            if use_cutouts:
                  A_	     =							self.make_cutouts(UpperCAmelCase	, UpperCAmelCase  )
            else:
                  A_	     =							transforms.Resize(self.cut_out_size  )(UpperCAmelCase  )
            A_	     =							self.normalize(UpperCAmelCase  ).to(latents.dtype  )

            A_	     =							self.clip_model.get_image_features(UpperCAmelCase  )
            A_	     =							image_embeddings_clip / image_embeddings_clip.norm(p=2	, dim=-1	, keepdim=UpperCAmelCase  )

            if use_cutouts:
                  A_	     =							spherical_dist_loss(UpperCAmelCase	, UpperCAmelCase  )
                  A_	     =							dists.view([num_cutouts, sample.shape[0], -1]  )
                  A_	     =							dists.sum(2  ).mean(0  ).sum() * clip_guidance_scale
            else:
                  A_	     =							spherical_dist_loss(UpperCAmelCase	, UpperCAmelCase  ).mean() * clip_guidance_scale

            A_	     =							-torch.autograd.grad(UpperCAmelCase	, UpperCAmelCase  )[0]

            if isinstance(self.scheduler	, UpperCAmelCase  ):
                  A_	     =							latents.detach() + grads * (sigma**2)
                  A_	     =							noise_pred_original
            else:
                  A_	     =							noise_pred_original - torch.sqrt(UpperCAmelCase  ) * grads
            return noise_pred, latents







      @torch.no_grad()
      def __call__(      self       :							Optional[Any]	, UpperCAmelCase       :							Union[str, List[str]]	, UpperCAmelCase       :							Optional[int] = 512	, UpperCAmelCase       :							Optional[int] = 512	, UpperCAmelCase       :							Optional[int] = 50	, UpperCAmelCase       :							Optional[float] = 7.5	, UpperCAmelCase       :							Optional[int] = 1	, UpperCAmelCase       :							float = 0.0	, UpperCAmelCase       :							Optional[float] = 100	, UpperCAmelCase       :							Optional[Union[str, List[str]]] = None	, UpperCAmelCase       :							Optional[int] = 4	, UpperCAmelCase       :							Optional[bool] = True	, UpperCAmelCase       :							Optional[torch.Generator] = None	, UpperCAmelCase       :							Optional[torch.FloatTensor] = None	, UpperCAmelCase       :							Optional[str] = "pil"	, UpperCAmelCase       :							bool = True	, ):
            if isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  A_	     =							1
            elif isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  A_	     =							len(UpperCAmelCase  )
            else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase  )}'''  )

            if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''  )

            # get prompt text embeddings
            A_	     =							self.tokenizer(
                UpperCAmelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=UpperCAmelCase	, return_tensors="pt"	, )
            A_	     =							self.text_encoder(text_input.input_ids.to(self.device  )  )[0]
            # duplicate text embeddings for each generation per prompt
            A_	     =							text_embeddings.repeat_interleave(UpperCAmelCase	, dim=0  )

            if clip_guidance_scale > 0:
                  if clip_prompt is not None:
                        A_	     =							self.tokenizer(
                            UpperCAmelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=UpperCAmelCase	, return_tensors="pt"	, ).input_ids.to(self.device  )
                  else:
                        A_	     =							text_input.input_ids.to(self.device  )
                  A_	     =							self.clip_model.get_text_features(UpperCAmelCase  )
                  A_	     =							text_embeddings_clip / text_embeddings_clip.norm(p=2	, dim=-1	, keepdim=UpperCAmelCase  )
                  # duplicate text embeddings clip for each generation per prompt
                  A_	     =							text_embeddings_clip.repeat_interleave(UpperCAmelCase	, dim=0  )

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            A_	     =							guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                  A_	     =							text_input.input_ids.shape[-1]
                  A_	     =							self.tokenizer([""]	, padding="max_length"	, max_length=UpperCAmelCase	, return_tensors="pt"  )
                  A_	     =							self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
                  # duplicate unconditional embeddings for each generation per prompt
                  A_	     =							uncond_embeddings.repeat_interleave(UpperCAmelCase	, dim=0  )

                  # For classifier free guidance, we need to do two forward passes.
                  # Here we concatenate the unconditional and text embeddings into a single batch
                  # to avoid doing two forward passes
                  A_	     =							torch.cat([uncond_embeddings, text_embeddings]  )

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            A_	     =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            A_	     =							text_embeddings.dtype
            if latents is None:
                  if self.device.type == "mps":
                        # randn does not work reproducibly on mps
                        A_	     =							torch.randn(UpperCAmelCase	, generator=UpperCAmelCase	, device="cpu"	, dtype=UpperCAmelCase  ).to(
                            self.device  )
                  else:
                        A_	     =							torch.randn(UpperCAmelCase	, generator=UpperCAmelCase	, device=self.device	, dtype=UpperCAmelCase  )
            else:
                  if latents.shape != latents_shape:
                        raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''  )
                  A_	     =							latents.to(self.device  )

            # set timesteps
            A_	     =							"offset" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
            A_	     =							{}
            if accepts_offset:
                  A_	     =							1

            self.scheduler.set_timesteps(UpperCAmelCase	, **UpperCAmelCase  )

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            A_	     =							self.scheduler.timesteps.to(self.device  )

            # scale the initial noise by the standard deviation required by the scheduler
            A_	     =							latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            A_	     =							"eta" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
            A_	     =							{}
            if accepts_eta:
                  A_	     =							eta

            # check if the scheduler accepts generator
            A_	     =							"generator" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
            if accepts_generator:
                  A_	     =							generator

            for i, t in enumerate(self.progress_bar(UpperCAmelCase  )  ):
                  # expand the latents if we are doing classifier free guidance
                  A_	     =							torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
                  A_	     =							self.scheduler.scale_model_input(UpperCAmelCase	, UpperCAmelCase  )

                  # predict the noise residual
                  A_	     =							self.unet(UpperCAmelCase	, UpperCAmelCase	, encoder_hidden_states=UpperCAmelCase  ).sample

                  # perform classifier free guidance
                  if do_classifier_free_guidance:
                        A_     ,    A_	     =							noise_pred.chunk(2  )
                        A_	     =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  # perform clip guidance
                  if clip_guidance_scale > 0:
                        A_	     =							(
                            text_embeddings.chunk(2  )[1] if do_classifier_free_guidance else text_embeddings
                        )
                        A_     ,    A_	     =							self.cond_fn(
                            UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, )

                  # compute the previous noisy sample x_t -> x_t-1
                  A_	     =							self.scheduler.step(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, **UpperCAmelCase  ).prev_sample

            # scale and decode the image latents with vae
            A_	     =							1 / self.vae.config.scaling_factor * latents
            A_	     =							self.vae.decode(UpperCAmelCase  ).sample

            A_	     =							(image / 2 + 0.5).clamp(0	, 1  )
            A_	     =							image.cpu().permute(0	, 2	, 3	, 1  ).numpy()

            if output_type == "pil":
                  A_	     =							self.numpy_to_pil(UpperCAmelCase  )

            if not return_dict:
                  return (image, None)

            return StableDiffusionPipelineOutput(images=UpperCAmelCase	, nsfw_content_detected=UpperCAmelCase  )