

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		(snake_case_					:     Tuple    ,       snake_case_					:     Tuple=100    ,       snake_case_					:     Optional[Any]=" "  )							->		str:
  __UpperCAmelCase	:  Union[str, Any]		=     text.split(snake_case_  )
  return [character.join(text[i : i + n]  ).strip() for i in range(0    ,       len(snake_case_  )    ,       snake_case_  )]





def  _snake_case		(snake_case_					:     Dict  )							->		Dict:
  __UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     [], []
  for title, text in zip(documents['title']    ,       documents['text']  ):
    if text is not None:
      for passage in split_text(snake_case_  ):
        titles.append(title if title is not None else ''  )
        texts.append(snake_case_  )
  return {"title": titles, "text": texts}





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Any    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     Optional[Any]    ,       snake_case_					:     Dict    ,       snake_case_					:     Optional[Any]  )							->		Union[str, Any]:
  __UpperCAmelCase	:  List[str]		=     load_dataset(
      'csv'    ,       data_files=[csv_path]    ,       split='train'    ,       delimiter='\t'    ,       column_names=['title', 'text']  )
  __UpperCAmelCase	:  List[Any]		=     kb_dataset.map(
      snake_case_    ,       batched=snake_case_    ,       num_proc=1  )  # if you want you can load already splitted csv.
  __UpperCAmelCase	:  Dict		=     [kb_dataset.shard(snake_case_    ,       snake_case_    ,       contiguous=snake_case_  ) for i in range(snake_case_  )]
  __UpperCAmelCase	:  Optional[int]		=     kb_list[process_num]

  __UpperCAmelCase	:  Any		=     'data_' + str(snake_case_  )
  __UpperCAmelCase	:  Optional[Any]		=     os.path.join(snake_case_    ,       snake_case_  )

  __UpperCAmelCase	:  Optional[Any]		=     DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
  __UpperCAmelCase	:  Tuple		=     ctx_encoder.to(device=snake_case_  )

  def embed(
      snake_case_					:     dict    ,       snake_case_					:     DPRContextEncoder    ,       snake_case_					:     DPRContextEncoderTokenizerFast    ,       snake_case_					:     Union[str, Any]  ) -> dict:
    __UpperCAmelCase	:  Optional[int]		=     ctx_tokenizer(
        documents['title']    ,       documents['text']    ,       truncation=snake_case_    ,       padding='longest'    ,       return_tensors='pt'  )['input_ids']
    __UpperCAmelCase	:  List[Any]		=     ctx_encoder(input_ids.to(device=snake_case_  )    ,       return_dict=snake_case_  ).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}

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

  __UpperCAmelCase	:  str		=     data_shrad.map(
      partial(snake_case_    ,       ctx_encoder=snake_case_    ,       ctx_tokenizer=snake_case_    ,       device=snake_case_  )    ,       batched=snake_case_    ,       batch_size=16    ,       features=snake_case_    ,       )
  dataset.save_to_disk(snake_case_  )







def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     Optional[Any]  )							->		Dict:
  __UpperCAmelCase	:  Any		=     []

  for shard_address in glob(str(snake_case_  ) + '/*/'  ):
    data_shard_list.append(load_from_disk(snake_case_  )  )

  __UpperCAmelCase	:  str		=     concatenate_datasets(snake_case_  )
  faiss.omp_set_num_threads(96  )

  __UpperCAmelCase	:  List[str]		=     faiss.IndexHNSWFlat(768    ,       128    ,       faiss.METRIC_INNER_PRODUCT  )
  concat.add_faiss_index('embeddings'    ,       custom_index=snake_case_  )
  concat.get_index('embeddings'  ).save(
      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							 =			logging.get_logger(__name__)  # pylint: disable=invalid-name





def  _snake_case		(self					:     Optional[Any]    ,       snake_case_					:     Optional[Any]    ,       snake_case_					:     Dict    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     Optional[Any]  )							->		Union[str, Any]:
  __UpperCAmelCase	:  Any		=     image.to(device=snake_case_  )
  __UpperCAmelCase	:  Tuple		=     image  # take image as image_embeddings
  __UpperCAmelCase	:  str		=     image_embeddings.unsqueeze(1  )

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

  if do_classifier_free_guidance:
    __UpperCAmelCase	:  List[Any]		=     torch.zeros_like(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
    __UpperCAmelCase	:  Union[str, Any]		=     torch.cat([uncond_embeddings, image_embeddings]  )

  return image_embeddings







class      lowerCamelCase_ (			_lowerCAmelCase):




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  ):
   super().__init__()

   __UpperCAmelCase	:  List[Any]		=     {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   __UpperCAmelCase	:  Optional[Any]		=     decoder_pipe_kwargs.get('torch_dtype'  ,  _UpperCAmelCase							) or prior.dtype

   __UpperCAmelCase	:  str		=     StableDiffusionImageVariationPipeline.from_pretrained(
       'lambdalabs/sd-image-variations-diffusers'  ,  **_UpperCAmelCase							)

   # replace `_encode_image` method
   __UpperCAmelCase	:  Dict		=     types.MethodType(_encode_image  ,  self.decoder_pipe							)

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




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  ):
   if text_model_output is None:
     __UpperCAmelCase	:  Any		=     len(_UpperCAmelCase							) if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							) else 1
     # get prompt text embeddings
     __UpperCAmelCase	:  str		=     self.tokenizer(
         _UpperCAmelCase  ,  padding='max_length'  ,  max_length=self.tokenizer.model_max_length  ,  return_tensors='pt'  ,  )
     __UpperCAmelCase	:  int		=     text_inputs.input_ids
     __UpperCAmelCase	:  str		=     text_inputs.attention_mask.bool().to(_UpperCAmelCase							)

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       __UpperCAmelCase	:  Any		=     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}"							)
       __UpperCAmelCase	:  int		=     text_input_ids[:, : self.tokenizer.model_max_length]

     __UpperCAmelCase	:  Any		=     self.text_encoder(text_input_ids.to(_UpperCAmelCase							)							)

     __UpperCAmelCase	:  str		=     text_encoder_output.text_embeds
     __UpperCAmelCase	:  str		=     text_encoder_output.last_hidden_state

   else:
     __UpperCAmelCase	:  str		=     text_model_output[0].shape[0]
     __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     text_model_output[0], text_model_output[1]
     __UpperCAmelCase	:  Optional[Any]		=     text_attention_mask

   __UpperCAmelCase	:  Optional[Any]		=     text_embeddings.repeat_interleave(_UpperCAmelCase  ,  dim=0							)
   __UpperCAmelCase	:  Tuple		=     text_encoder_hidden_states.repeat_interleave(_UpperCAmelCase  ,  dim=0							)
   __UpperCAmelCase	:  Optional[Any]		=     text_mask.repeat_interleave(_UpperCAmelCase  ,  dim=0							)

   if do_classifier_free_guidance:
     __UpperCAmelCase	:  int		=     [''] * batch_size

     __UpperCAmelCase	:  Optional[int]		=     self.tokenizer(
         _UpperCAmelCase  ,  padding='max_length'  ,  max_length=self.tokenizer.model_max_length  ,  truncation=_UpperCAmelCase  ,  return_tensors='pt'  ,  )
     __UpperCAmelCase	:  List[str]		=     uncond_input.attention_mask.bool().to(_UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     self.text_encoder(uncond_input.input_ids.to(_UpperCAmelCase							)							)

     __UpperCAmelCase	:  Optional[Any]		=     uncond_embeddings_text_encoder_output.text_embeds
     __UpperCAmelCase	:  List[str]		=     uncond_embeddings_text_encoder_output.last_hidden_state

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

     __UpperCAmelCase	:  List[str]		=     uncond_embeddings.shape[1]
     __UpperCAmelCase	:  Tuple		=     uncond_embeddings.repeat(1  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  List[str]		=     uncond_embeddings.view(batch_size * num_images_per_prompt  ,  _UpperCAmelCase							)

     __UpperCAmelCase	:  Tuple		=     uncond_text_encoder_hidden_states.shape[1]
     __UpperCAmelCase	:  Union[str, Any]		=     uncond_text_encoder_hidden_states.repeat(1  ,  _UpperCAmelCase  ,  1							)
     __UpperCAmelCase	:  Any		=     uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt  ,  _UpperCAmelCase  ,  -1							)
     __UpperCAmelCase	:  int		=     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
     __UpperCAmelCase	:  List[str]		=     torch.cat([uncond_embeddings, text_embeddings]							)
     __UpperCAmelCase	:  str		=     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

     __UpperCAmelCase	:  Any		=     torch.cat([uncond_text_mask, text_mask]							)

   return text_embeddings, text_encoder_hidden_states, text_mask




 @property
 def     a       (			self							):
   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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   if latents is None:
     __UpperCAmelCase	:  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}"							)
     __UpperCAmelCase	:  Optional[int]		=     latents.to(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     latents * scheduler.init_noise_sigma
   return latents




 def     a       (			self  ,  _UpperCAmelCase = None							):
   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  ,  ):
   if prompt is not None:
     if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
       __UpperCAmelCase	:  List[Any]		=     1
     elif isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
       __UpperCAmelCase	:  Tuple		=     len(_UpperCAmelCase							)
     else:
       raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase							)}"							)
   else:
     __UpperCAmelCase	:  Tuple		=     text_model_output[0].shape[0]

   __UpperCAmelCase	:  Any		=     self._execution_device

   __UpperCAmelCase	:  Any		=     batch_size * num_images_per_prompt

   __UpperCAmelCase	:  List[str]		=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self._encode_prompt(
       _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

   # prior

   self.prior_scheduler.set_timesteps(_UpperCAmelCase  ,  device=_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.prior_scheduler.timesteps

   __UpperCAmelCase	:  Tuple		=     self.prior.config.embedding_dim

   __UpperCAmelCase	:  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
     __UpperCAmelCase	:  Dict		=     torch.cat([prior_latents] * 2							) if do_classifier_free_guidance else prior_latents

     __UpperCAmelCase	:  Union[str, Any]		=     self.prior(
         _UpperCAmelCase  ,  timestep=_UpperCAmelCase  ,  proj_embedding=_UpperCAmelCase  ,  encoder_hidden_states=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  ).predicted_image_embedding

     if do_classifier_free_guidance:
       __UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     predicted_image_embedding.chunk(2							)
       __UpperCAmelCase	:  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]:
       __UpperCAmelCase	:  int		=     None
     else:
       __UpperCAmelCase	:  Any		=     prior_timesteps_tensor[i + 1]

     __UpperCAmelCase	:  Union[str, Any]		=     self.prior_scheduler.step(
         _UpperCAmelCase  ,  timestep=_UpperCAmelCase  ,  sample=_UpperCAmelCase  ,  generator=_UpperCAmelCase  ,  prev_timestep=_UpperCAmelCase  ,  ).prev_sample

   __UpperCAmelCase	:  Union[str, Any]		=     self.prior.post_process_latents(_UpperCAmelCase							)

   __UpperCAmelCase	:  Any		=     prior_latents

   __UpperCAmelCase	:  List[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





