

import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

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



      """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  :   Tuple						):



      """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  :   Union[str, Any]							,__UpperCamelCase  :   List[Any]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Tuple							,__UpperCamelCase  :   List[Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      A_	     =							load_dataset(
          "csv"							,data_files=[csv_path]							,split="train"							,delimiter="\t"							,column_names=["title", "text"]						)
      A_	     =							kb_dataset.map(
          __UpperCamelCase							,batched=__UpperCamelCase							,num_proc=1						)  # if you want you can load already splitted csv.
      A_	     =							[kb_dataset.shard(__UpperCamelCase							,__UpperCamelCase							,contiguous=__UpperCamelCase						) for i in range(__UpperCamelCase						)]
      A_	     =							kb_list[process_num]

      A_	     =							"data_" + str(__UpperCamelCase						)
      A_	     =							os.path.join(__UpperCamelCase							,__UpperCamelCase						)

      A_	     =							DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"						)
      A_	     =							ctx_encoder.to(device=__UpperCamelCase						)

      def embed(
          __UpperCamelCase  :   dict							,__UpperCamelCase  :   DPRContextEncoder							,__UpperCamelCase  :   DPRContextEncoderTokenizerFast							,__UpperCamelCase  :   int						) -> dict:
            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()}

      A_	     =							Features(
          {"text": Value("string"						), "title": Value("string"						), "embeddings": Sequence(Value("float32"						)						)}						)  # optional, save as float32 instead of float64 to save space

      A_	     =							data_shrad.map(
          partial(__UpperCamelCase							,ctx_encoder=__UpperCamelCase							,ctx_tokenizer=__UpperCamelCase							,device=__UpperCamelCase						)							,batched=__UpperCamelCase							,batch_size=16							,features=__UpperCamelCase							,)
      dataset.save_to_disk(__UpperCamelCase						)



def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Optional[int]						):



      """simple docstring"""

      A_	     =							[]

      for shard_address in glob(str(__UpperCamelCase						) + "/*/"						):
            data_shard_list.append(load_from_disk(__UpperCamelCase						)						)

      A_	     =							concatenate_datasets(__UpperCamelCase						)
      faiss.omp_set_num_threads(96						)

      A_	     =							faiss.IndexHNSWFlat(768							,128							,faiss.METRIC_INNER_PRODUCT						)
      concat.add_faiss_index("embeddings"							,custom_index=__UpperCamelCase						)
      concat.get_index("embeddings"						).save(
          __UpperCamelCase						)  # since we load the index in to memory,we can directly update the index in the disk


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  )