


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



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    Optional[Any]=1_00				,   UpperCAmelCase_   :    Union[str, Any]=" "     )					->		List[str]:
     __lowerCamelCase       :     Optional[Any]        =		text.split(UpperCAmelCase_     )
     return [character.join(text[i : i + n]     ).strip() for i in range(0				,   len(UpperCAmelCase_     )				,   UpperCAmelCase_     )]



def       UpperCAmelCase__ (     UpperCAmelCase_   :    dict     )					->		dict:
     __lowerCamelCase       :     Any        =		[], []
     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       UpperCAmelCase__ (     UpperCAmelCase_   :    dict				,   UpperCAmelCase_   :    DPRContextEncoder				,   UpperCAmelCase_   :    DPRContextEncoderTokenizerFast     )					->		dict:
     __lowerCamelCase       :     Tuple        =		ctx_tokenizer(
         documents['title']				,   documents['text']				,   truncation=UpperCAmelCase_				,   padding='longest'				,   return_tensors='pt'     )['input_ids']
     __lowerCamelCase       :     List[Any]        =		ctx_encoder(input_ids.to(device=UpperCAmelCase_     )				,   return_dict=UpperCAmelCase_     ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}



def       UpperCAmelCase__ (     UpperCAmelCase_   :    "RagExampleArguments"				,   UpperCAmelCase_   :    "ProcessingArguments"				,   UpperCAmelCase_   :    "IndexHnswArguments"				,   )					->		List[str]:
     ######################################
     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
     __lowerCamelCase       :     str        =		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
     __lowerCamelCase       :     Dict        =		dataset.map(UpperCAmelCase_				,   batched=UpperCAmelCase_				,   num_proc=processing_args.num_proc     )

     # And compute the embeddings
     __lowerCamelCase       :     Any        =		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=UpperCAmelCase_     )
     __lowerCamelCase       :     List[Any]        =		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
     __lowerCamelCase       :     Optional[int]        =		Features(
         {'text': Value('string'     ), 'title': Value('string'     ), 'embeddings': Sequence(Value('float32'     )     )}     )  # optional, save as float32 instead of float64 to save space
     __lowerCamelCase       :     Optional[int]        =		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
     __lowerCamelCase       :     Dict        =		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
     __lowerCamelCase       :     Dict        =		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
     __lowerCamelCase       :     Dict        =		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
     __lowerCamelCase       :     Union[str, Any]        =		RagRetriever.from_pretrained(
         rag_example_args.rag_model_name				,   index_name='custom'				,   indexed_dataset=UpperCAmelCase_     )
     __lowerCamelCase       :     Tuple        =		RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name				,   retriever=UpperCAmelCase_     )
     __lowerCamelCase       :     Union[str, Any]        =		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'     )
     ######################################

     __lowerCamelCase       :     int        =		rag_example_args.question or 'What does Moses\' rod turn into ?'
     __lowerCamelCase       :     Optional[Any]        =		tokenizer.question_encoder(UpperCAmelCase_				,   return_tensors='pt'     )['input_ids']
     __lowerCamelCase       :     Union[str, Any]        =		model.generate(UpperCAmelCase_     )
     __lowerCamelCase       :     str        =		tokenizer.batch_decode(UpperCAmelCase_				,   skip_special_tokens=UpperCAmelCase_     )[0]
     logger.info('Q: ' + question     )
     logger.info('A: ' + generated_string     )





@dataclass
class        UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  : str												=      field(
        default=str(Path(_UpperCAmelCase  ).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=_UpperCAmelCase ,		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=_UpperCAmelCase ,		metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} ,		)







@dataclass
class        UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  : Optional[int]												=      field(
        default=_UpperCAmelCase ,		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        UpperCAmelCase_  :





    """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__		: Union[str, Any]												=  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)










'''simple docstring'''



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        UpperCAmelCase_  (nn.Module  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=1.0     )    ->					List[str]:
         super().__init__()

         __lowerCamelCase       :     int        =		cut_size
         __lowerCamelCase       :     Optional[int]        =		cut_power







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







def       UpperCAmelCase__ (     UpperCAmelCase_   :    List[Any]				,   UpperCAmelCase_   :    Tuple							)					->		str:
     __lowerCamelCase       :     List[str]        =		F.normalize(UpperCAmelCase_				,   dim=-1							)
     __lowerCamelCase       :     Optional[Any]        =		F.normalize(UpperCAmelCase_				,   dim=-1							)
     return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Optional[Any]				,   UpperCAmelCase_   :    Any							)					->		List[str]:
     for param in model.parameters():
          __lowerCamelCase       :     Dict        =		value





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					)    ->					List[str]:
         super().__init__()
         self.register_modules(
             vae=SCREAMING_SNAKE_CASE_      ,					text_encoder=SCREAMING_SNAKE_CASE_      ,					clip_model=SCREAMING_SNAKE_CASE_      ,					tokenizer=SCREAMING_SNAKE_CASE_      ,					unet=SCREAMING_SNAKE_CASE_      ,					scheduler=SCREAMING_SNAKE_CASE_      ,					feature_extractor=SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     int        =		transforms.Normalize(mean=feature_extractor.image_mean      ,					std=feature_extractor.image_std     )
         __lowerCamelCase       :     Union[str, Any]        =		(
             feature_extractor.size
             if isinstance(feature_extractor.size      ,					SCREAMING_SNAKE_CASE_     )
             else feature_extractor.size['shortest_edge']
         )
         __lowerCamelCase       :     List[Any]        =		MakeCutouts(self.cut_out_size     )

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







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_ = "auto"     )    ->					str:
         if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              __lowerCamelCase       :     str        =		self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					Union[str, Any]:
         self.enable_attention_slicing(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					Union[str, Any]:
         set_requires_grad(self.vae      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					List[str]:
         set_requires_grad(self.vae      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					Optional[int]:
         set_requires_grad(self.unet      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					Union[str, Any]:
         set_requires_grad(self.unet      ,					SCREAMING_SNAKE_CASE_     )







    @torch.enable_grad()
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=True      ,					)    ->					Union[str, Any]:
         __lowerCamelCase       :     Union[str, Any]        =		latents.detach().requires_grad_()

         __lowerCamelCase       :     List[str]        =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )

         # predict the noise residual
         __lowerCamelCase       :     Tuple        =		self.unet(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					encoder_hidden_states=SCREAMING_SNAKE_CASE_     ).sample

         if isinstance(self.scheduler      ,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
              __lowerCamelCase       :     List[str]        =		self.scheduler.alphas_cumprod[timestep]
              __lowerCamelCase       :     str        =		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
              __lowerCamelCase       :     Union[str, Any]        =		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

              __lowerCamelCase       :     Union[str, Any]        =		torch.sqrt(SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     Optional[int]        =		pred_original_sample * (fac) + latents * (1 - fac)
         elif isinstance(self.scheduler      ,					SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     List[str]        =		self.scheduler.sigmas[index]
              __lowerCamelCase       :     List[Any]        =		latents - sigma * noise_pred
         else:
              raise ValueError(f'scheduler type {type(self.scheduler     )} not supported'     )

         __lowerCamelCase       :     Optional[Any]        =		1 / self.vae.config.scaling_factor * sample
         __lowerCamelCase       :     Optional[Any]        =		self.vae.decode(SCREAMING_SNAKE_CASE_     ).sample
         __lowerCamelCase       :     List[Any]        =		(image / 2 + 0.5).clamp(0      ,					1     )

         if use_cutouts:
              __lowerCamelCase       :     Union[str, Any]        =		self.make_cutouts(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
         else:
              __lowerCamelCase       :     Any        =		transforms.Resize(self.cut_out_size     )(SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Any        =		self.normalize(SCREAMING_SNAKE_CASE_     ).to(latents.dtype     )

         __lowerCamelCase       :     Optional[Any]        =		self.clip_model.get_image_features(SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Optional[Any]        =		image_embeddings_clip / image_embeddings_clip.norm(p=2      ,					dim=-1      ,					keepdim=SCREAMING_SNAKE_CASE_     )

         if use_cutouts:
              __lowerCamelCase       :     Optional[Any]        =		spherical_dist_loss(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     int        =		dists.view([num_cutouts, sample.shape[0], -1]     )
              __lowerCamelCase       :     List[Any]        =		dists.sum(2     ).mean(0     ).sum() * clip_guidance_scale
         else:
              __lowerCamelCase       :     Any        =		spherical_dist_loss(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ).mean() * clip_guidance_scale

         __lowerCamelCase       :     Union[str, Any]        =		-torch.autograd.grad(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )[0]

         if isinstance(self.scheduler      ,					SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     Optional[int]        =		latents.detach() + grads * (sigma**2)
              __lowerCamelCase       :     int        =		noise_pred_original
         else:
              __lowerCamelCase       :     Dict        =		noise_pred_original - torch.sqrt(SCREAMING_SNAKE_CASE_     ) * grads
         return noise_pred, latents







    @torch.no_grad()
    def __call__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = 5_12      ,					SCREAMING_SNAKE_CASE_ = 5_12      ,					SCREAMING_SNAKE_CASE_ = 50      ,					SCREAMING_SNAKE_CASE_ = 7.5      ,					SCREAMING_SNAKE_CASE_ = 1      ,					SCREAMING_SNAKE_CASE_ = 0.0      ,					SCREAMING_SNAKE_CASE_ = 1_00      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 4      ,					SCREAMING_SNAKE_CASE_ = True      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "pil"      ,					SCREAMING_SNAKE_CASE_ = True      ,					)    ->					Tuple:
         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     List[str]        =		1
         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     Tuple        =		len(SCREAMING_SNAKE_CASE_     )
         else:
              raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_     )}'     )

         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
         __lowerCamelCase       :     str        =		self.tokenizer(
             SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=self.tokenizer.model_max_length      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
         __lowerCamelCase       :     List[str]        =		self.text_encoder(text_input.input_ids.to(self.device     )     )[0]
         # duplicate text embeddings for each generation per prompt
         __lowerCamelCase       :     Optional[Any]        =		text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_      ,					dim=0     )

         if clip_guidance_scale > 0:
              if clip_prompt is not None:
                   __lowerCamelCase       :     int        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=self.tokenizer.model_max_length      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					).input_ids.to(self.device     )
              else:
                   __lowerCamelCase       :     str        =		text_input.input_ids.to(self.device     )
              __lowerCamelCase       :     Optional[Any]        =		self.clip_model.get_text_features(SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     Dict        =		text_embeddings_clip / text_embeddings_clip.norm(p=2      ,					dim=-1      ,					keepdim=SCREAMING_SNAKE_CASE_     )
              # duplicate text embeddings clip for each generation per prompt
              __lowerCamelCase       :     Optional[int]        =		text_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE_      ,					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.
         __lowerCamelCase       :     Tuple        =		guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
              __lowerCamelCase       :     List[Any]        =		text_input.input_ids.shape[-1]
              __lowerCamelCase       :     Union[str, Any]        =		self.tokenizer(['']      ,					padding='max_length'      ,					max_length=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )
              __lowerCamelCase       :     Any        =		self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
              # duplicate unconditional embeddings for each generation per prompt
              __lowerCamelCase       :     Optional[int]        =		uncond_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_      ,					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
              __lowerCamelCase       :     Tuple        =		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`.
         __lowerCamelCase       :     int        =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         __lowerCamelCase       :     Optional[int]        =		text_embeddings.dtype
         if latents is None:
              if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   __lowerCamelCase       :     List[str]        =		torch.randn(SCREAMING_SNAKE_CASE_      ,					generator=SCREAMING_SNAKE_CASE_      ,					device='cpu'      ,					dtype=SCREAMING_SNAKE_CASE_     ).to(
                       self.device     )
              else:
                   __lowerCamelCase       :     Dict        =		torch.randn(SCREAMING_SNAKE_CASE_      ,					generator=SCREAMING_SNAKE_CASE_      ,					device=self.device      ,					dtype=SCREAMING_SNAKE_CASE_     )
         else:
              if latents.shape != latents_shape:
                   raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'     )
              __lowerCamelCase       :     Union[str, Any]        =		latents.to(self.device     )

         # set timesteps
         __lowerCamelCase       :     Any        =		'offset' in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
         __lowerCamelCase       :     Optional[int]        =		{}
         if accepts_offset:
              __lowerCamelCase       :     List[str]        =		1

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

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

         # scale the initial noise by the standard deviation required by the scheduler
         __lowerCamelCase       :     Any        =		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]
         __lowerCamelCase       :     Optional[Any]        =		'eta' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
         __lowerCamelCase       :     int        =		{}
         if accepts_eta:
              __lowerCamelCase       :     List[str]        =		eta

         # check if the scheduler accepts generator
         __lowerCamelCase       :     Optional[int]        =		'generator' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
         if accepts_generator:
              __lowerCamelCase       :     List[Any]        =		generator

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

              # predict the noise residual
              __lowerCamelCase       :     Tuple        =		self.unet(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					encoder_hidden_states=SCREAMING_SNAKE_CASE_     ).sample

              # perform classifier free guidance
              if do_classifier_free_guidance:
                   __lowerCamelCase    ,	__lowerCamelCase       :     Optional[int]        =		noise_pred.chunk(2     )
                   __lowerCamelCase       :     Any        =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

              # compute the previous noisy sample x_t -> x_t-1
              __lowerCamelCase       :     List[Any]        =		self.scheduler.step(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     ).prev_sample

         # scale and decode the image latents with vae
         __lowerCamelCase       :     Any        =		1 / self.vae.config.scaling_factor * latents
         __lowerCamelCase       :     Tuple        =		self.vae.decode(SCREAMING_SNAKE_CASE_     ).sample

         __lowerCamelCase       :     str        =		(image / 2 + 0.5).clamp(0      ,					1     )
         __lowerCamelCase       :     Optional[Any]        =		image.cpu().permute(0      ,					2      ,					3      ,					1     ).numpy()

         if output_type == "pil":
              __lowerCamelCase       :     Optional[Any]        =		self.numpy_to_pil(SCREAMING_SNAKE_CASE_     )

         if not return_dict:
              return (image, None)

         return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_      ,					nsfw_content_detected=SCREAMING_SNAKE_CASE_     )






