




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 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=100			,_SCREAMING_SNAKE_CASE=" "   )     ->			Optional[Any]:
       lowerCamelCase       :				Optional[int]      			= text.split(_SCREAMING_SNAKE_CASE   )
       return [character.join(text[i : i + n]   ).strip() for i in range(0			,len(_SCREAMING_SNAKE_CASE   )			,_SCREAMING_SNAKE_CASE   )]

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			int:
       lowerCamelCase			,				lowerCamelCase       :				Any      			= [], []
       for title, text in zip(documents["title"]			,documents["text"]   ):
              if text is not None:
                     for passage in split_text(_SCREAMING_SNAKE_CASE   ):
                            titles.append(title if title is not None else ""   )
                            texts.append(_SCREAMING_SNAKE_CASE   )
       return {"title": titles, "text": texts}

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			List[str]:
       lowerCamelCase       :				Any      			= load_dataset(
           "csv"			,data_files=[csv_path]			,split="train"			,delimiter="\t"			,column_names=["title", "text"]   )
       lowerCamelCase       :				List[Any]      			= kb_dataset.map(
           _SCREAMING_SNAKE_CASE			,batched=_SCREAMING_SNAKE_CASE			,num_proc=1   )  # if you want you can load already splitted csv.
       lowerCamelCase       :				List[Any]      			= [kb_dataset.shard(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,contiguous=_SCREAMING_SNAKE_CASE   ) for i in range(_SCREAMING_SNAKE_CASE   )]
       lowerCamelCase       :				Tuple      			= kb_list[process_num]

       lowerCamelCase       :				List[Any]      			= "data_" + str(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= os.path.join(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				List[Any]      			= DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"   )
       lowerCamelCase       :				Any      			= ctx_encoder.to(device=_SCREAMING_SNAKE_CASE   )

       def embed(
           _SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ) -> dict:
              lowerCamelCase       :				List[Any]      			= ctx_tokenizer(
                  documents["title"]			,documents["text"]			,truncation=_SCREAMING_SNAKE_CASE			,padding="longest"			,return_tensors="pt"   )["input_ids"]
              lowerCamelCase       :				int      			= ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE   )			,return_dict=_SCREAMING_SNAKE_CASE   ).pooler_output
              return {"embeddings": embeddings.detach().cpu().numpy()}

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

       lowerCamelCase       :				Any      			= data_shrad.map(
           partial(_SCREAMING_SNAKE_CASE			,ctx_encoder=_SCREAMING_SNAKE_CASE			,ctx_tokenizer=_SCREAMING_SNAKE_CASE			,device=_SCREAMING_SNAKE_CASE   )			,batched=_SCREAMING_SNAKE_CASE			,batch_size=16			,features=_SCREAMING_SNAKE_CASE			,)
       dataset.save_to_disk(_SCREAMING_SNAKE_CASE   )


def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Tuple:
       lowerCamelCase       :				List[Any]      			= []

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

       lowerCamelCase       :				int      			= concatenate_datasets(_SCREAMING_SNAKE_CASE   )
       faiss.omp_set_num_threads(96   )

       lowerCamelCase       :				Optional[int]      			= faiss.IndexHNSWFlat(768			,128			,faiss.METRIC_INNER_PRODUCT   )
       concat.add_faiss_index("embeddings"			,custom_index=_SCREAMING_SNAKE_CASE   )
       concat.get_index("embeddings"   ).save(
           _SCREAMING_SNAKE_CASE   )  # since we load the index in to memory,we can directly update the index in the disk












import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


SCREAMING_SNAKE_CASE__	:				List[str]  =       logging.get_logger(__name__)  # pylint: disable=invalid-name

def 					A				(				self			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase       :				Dict      			= image.to(device=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				str      			= image  # take image as image_embeddings
       lowerCamelCase       :				List[Any]      			= image_embeddings.unsqueeze(1   )

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				str      			= image_embeddings.shape
       lowerCamelCase       :				List[str]      			= image_embeddings.repeat(1			,_SCREAMING_SNAKE_CASE			,1   )
       lowerCamelCase       :				Optional[int]      			= image_embeddings.view(bs_embed * num_images_per_prompt			,_SCREAMING_SNAKE_CASE			,-1   )

       if do_classifier_free_guidance:
              lowerCamelCase       :				Dict      			= torch.zeros_like(_SCREAMING_SNAKE_CASE   )

              # 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       :				Optional[Any]      			= torch.cat([uncond_embeddings, image_embeddings]   )

       return image_embeddings




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  )					->				Tuple:
           super().__init__()

           lowerCamelCase       :				str      			= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

           lowerCamelCase       :				Dict      			= decoder_pipe_kwargs.get("torch_dtype"					,  UpperCamelCase__					) or prior.dtype

           lowerCamelCase       :				Dict      			= StableDiffusionImageVariationPipeline.from_pretrained(
               "lambdalabs/sd-image-variations-diffusers"					,  **UpperCamelCase__					)

           # replace `_encode_image` method
           lowerCamelCase       :				List[str]      			= types.MethodType(_encode_image					,  self.decoder_pipe					)

           self.register_modules(
               prior=UpperCamelCase__					,  tokenizer=UpperCamelCase__					,  text_encoder=UpperCamelCase__					,  prior_scheduler=UpperCamelCase__					,  )







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				Any:
           if text_model_output is None:
                  lowerCamelCase       :				List[str]      			= len(UpperCamelCase__					) if isinstance(UpperCamelCase__					,  UpperCamelCase__					) else 1
                  # get prompt text embeddings
                  lowerCamelCase       :				Any      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				int      			= text_inputs.input_ids
                  lowerCamelCase       :				Optional[Any]      			= text_inputs.attention_mask.bool().to(UpperCamelCase__					)

                  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                         lowerCamelCase       :				Optional[int]      			= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
                         logger.warning(
                             "The following part of your input was truncated because CLIP can only handle sequences up to"
                             F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''					)
                         lowerCamelCase       :				Optional[Any]      			= text_input_ids[:, : self.tokenizer.model_max_length]

                  lowerCamelCase       :				int      			= self.text_encoder(text_input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Optional[Any]      			= text_encoder_output.text_embeds
                  lowerCamelCase       :				Optional[int]      			= text_encoder_output.last_hidden_state

           else:
                  lowerCamelCase       :				Dict      			= text_model_output[0].shape[0]
                  lowerCamelCase			,				lowerCamelCase       :				int      			= text_model_output[0], text_model_output[1]
                  lowerCamelCase       :				int      			= text_attention_mask

           lowerCamelCase       :				Dict      			= text_embeddings.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				int      			= text_encoder_hidden_states.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				List[str]      			= text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

           if do_classifier_free_guidance:
                  lowerCamelCase       :				Dict      			= [""] * batch_size

                  lowerCamelCase       :				List[Any]      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  truncation=UpperCamelCase__					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				str      			= uncond_input.attention_mask.bool().to(UpperCamelCase__					)
                  lowerCamelCase       :				int      			= self.text_encoder(uncond_input.input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Union[str, Any]      			= uncond_embeddings_text_encoder_output.text_embeds
                  lowerCamelCase       :				List[Any]      			= uncond_embeddings_text_encoder_output.last_hidden_state

                  # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.shape[1]
                  lowerCamelCase       :				str      			= uncond_embeddings.repeat(1					,  UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.view(batch_size * num_images_per_prompt					,  UpperCamelCase__					)

                  lowerCamelCase       :				Any      			= uncond_text_encoder_hidden_states.shape[1]
                  lowerCamelCase       :				int      			= uncond_text_encoder_hidden_states.repeat(1					,  UpperCamelCase__					,  1					)
                  lowerCamelCase       :				Tuple      			= uncond_text_encoder_hidden_states.view(
                      batch_size * num_images_per_prompt					,  UpperCamelCase__					,  -1					)
                  lowerCamelCase       :				List[str]      			= uncond_text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

                  # done duplicates

                  # 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       :				int      			= torch.cat([uncond_embeddings, text_embeddings]					)
                  lowerCamelCase       :				str      			= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

                  lowerCamelCase       :				Dict      			= torch.cat([uncond_text_mask, text_mask]					)

           return text_embeddings, text_encoder_hidden_states, text_mask







    @property
    def    _lowercase					(   self					)					->				Any:
           if self.device != torch.device("meta"					) or not hasattr(self.prior					,  "_hf_hook"					):
                  return self.device
           for module in self.prior.modules():
                  if (
                      hasattr(UpperCamelCase__					,  "_hf_hook"					)
                      and hasattr(module._hf_hook					,  "execution_device"					)
                      and module._hf_hook.execution_device is not None
                  ):
                         return torch.device(module._hf_hook.execution_device					)
           return self.device







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Any:
           if latents is None:
                  lowerCamelCase       :				Any      			= randn_tensor(UpperCamelCase__					,  generator=UpperCamelCase__					,  device=UpperCamelCase__					,  dtype=UpperCamelCase__					)
           else:
                  if latents.shape != shape:
                         raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
                  lowerCamelCase       :				Any      			= latents.to(UpperCamelCase__					)

           lowerCamelCase       :				List[str]      			= latents * scheduler.init_noise_sigma
           return latents







    def    _lowercase					(   self					,  UpperCamelCase__ = None					)					->				str:
           self.decoder_pipe.to(UpperCamelCase__					)
           super().to(UpperCamelCase__					)







    @torch.no_grad()
    def __call__(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 25					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 4.0					,  UpperCamelCase__ = 8.0					,  UpperCamelCase__ = 50					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 0.0					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = True					,  )					->				Union[str, Any]:
           if prompt is not None:
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Union[str, Any]      			= 1
                  elif isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				int      			= len(UpperCamelCase__					)
                  else:
                         raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__					)}'''					)
           else:
                  lowerCamelCase       :				Any      			= text_model_output[0].shape[0]

           lowerCamelCase       :				str      			= self._execution_device

           lowerCamelCase       :				Optional[Any]      			= batch_size * num_images_per_prompt

           lowerCamelCase       :				int      			= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Tuple      			= self._encode_prompt(
               UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)

           # prior

           self.prior_scheduler.set_timesteps(UpperCamelCase__					,  device=UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.timesteps

           lowerCamelCase       :				List[Any]      			= self.prior.config.embedding_dim

           lowerCamelCase       :				Dict      			= self.prepare_latents(
               (batch_size, embedding_dim)					,  text_embeddings.dtype					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  self.prior_scheduler					,  )

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

                  lowerCamelCase       :				List[Any]      			= self.prior(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  proj_embedding=UpperCamelCase__					,  encoder_hidden_states=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  ).predicted_image_embedding

                  if do_classifier_free_guidance:
                         lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= predicted_image_embedding.chunk(2					)
                         lowerCamelCase       :				Optional[Any]      			= predicted_image_embedding_uncond + prior_guidance_scale * (
                             predicted_image_embedding_text - predicted_image_embedding_uncond
                         )

                  if i + 1 == prior_timesteps_tensor.shape[0]:
                         lowerCamelCase       :				Union[str, Any]      			= None
                  else:
                         lowerCamelCase       :				Dict      			= prior_timesteps_tensor[i + 1]

                  lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.step(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  sample=UpperCamelCase__					,  generator=UpperCamelCase__					,  prev_timestep=UpperCamelCase__					,  ).prev_sample

           lowerCamelCase       :				Tuple      			= self.prior.post_process_latents(UpperCamelCase__					)

           lowerCamelCase       :				Any      			= prior_latents

           lowerCamelCase       :				Union[str, Any]      			= self.decoder_pipe(
               image=UpperCamelCase__					,  height=UpperCamelCase__					,  width=UpperCamelCase__					,  num_inference_steps=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type=UpperCamelCase__					,  return_dict=UpperCamelCase__					,  num_images_per_prompt=UpperCamelCase__					,  eta=UpperCamelCase__					,  )
           return output






