"""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,
)


lowercase__   			=      logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowercase__   			=      'cuda' if torch.cuda.is_available() else 'cpu'







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=100						,	_SCREAMING_SNAKE_CASE=" "  )      ->List[str]:
  a__:	str     				=      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  )      ->dict:
  a__				,       a__:	Dict     				=      [], []
  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  )      ->dict:
  a__:	str     				=      ctx_tokenizer(
      documents['title']						,	documents['text']						,	truncation=_SCREAMING_SNAKE_CASE						,	padding='longest'						,	return_tensors='pt'  )['input_ids']
  a__:	Any     				=      ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE  )						,	return_dict=_SCREAMING_SNAKE_CASE  ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	)      ->int:
  ######################################
  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__:	int     				=      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__:	Dict     				=      dataset.map(_SCREAMING_SNAKE_CASE						,	batched=_SCREAMING_SNAKE_CASE						,	num_proc=processing_args.num_proc  )

  # And compute the embeddings
  a__:	Union[str, Any]     				=      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=_SCREAMING_SNAKE_CASE  )
  a__:	Optional[Any]     				=      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
  a__:	Dict     				=      Features(
      {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space
  a__:	List[Any]     				=      dataset.map(
      partial(_SCREAMING_SNAKE_CASE						,	ctx_encoder=_SCREAMING_SNAKE_CASE						,	ctx_tokenizer=_SCREAMING_SNAKE_CASE  )						,	batched=_SCREAMING_SNAKE_CASE						,	batch_size=processing_args.batch_size						,	features=_SCREAMING_SNAKE_CASE						,	)

  # And finally save your dataset
  a__:	Any     				=      os.path.join(rag_example_args.output_dir						,	'my_knowledge_dataset'  )
  dataset.save_to_disk(_SCREAMING_SNAKE_CASE  )
  # 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__:	Tuple     				=      faiss.IndexHNSWFlat(index_hnsw_args.d						,	index_hnsw_args.m						,	faiss.METRIC_INNER_PRODUCT  )
  dataset.add_faiss_index('embeddings'						,	custom_index=_SCREAMING_SNAKE_CASE  )

  # And save the index
  a__:	Optional[int]     				=      os.path.join(rag_example_args.output_dir						,	'my_knowledge_dataset_hnsw_index.faiss'  )
  dataset.get_index('embeddings'  ).save(_SCREAMING_SNAKE_CASE  )
  # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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

  # Easy way to load the model
  a__:	List[str]     				=      RagRetriever.from_pretrained(
      rag_example_args.rag_model_name						,	index_name='custom'						,	indexed_dataset=_SCREAMING_SNAKE_CASE  )
  a__:	int     				=      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name						,	retriever=_SCREAMING_SNAKE_CASE  )
  a__:	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'  )
  ######################################

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




@dataclass
class   __snake_case			:
      a__								=						field(
          default=str(Path(__lowerCAmelCase							).parent / """test_data""" / """my_knowledge_dataset.csv"""							)			, metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}			, )
      a__								=						field(
          default=__lowerCAmelCase			, metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}			, )
      a__								=						field(
          default="""facebook/rag-sequence-nq"""			, metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}			, )
      a__								=						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'"""
              )
          }			, )
      a__								=						field(
          default=__lowerCAmelCase			, metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}			, )







@dataclass
class   __snake_case			:
      a__								=						field(
          default=__lowerCAmelCase			, metadata={
              """help""": """The number of processes to use to split the documents into passages. Default is single process."""
          }			, )
      a__								=						field(
          default=16			, metadata={
              """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
          }			, )







@dataclass
class   __snake_case			:
      a__								=						field(
          default=768			, metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}			, )
      a__								=						field(
          default=128			, 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)

 lowercase__   			=      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 lowercase__			,   lowercase__			,   lowercase__   			=      parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  lowercase__   			=      rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)



"""simple docstring"""
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

        self.register_modules(vae=lowercase       ,     text_encoder=lowercase       ,     tokenizer=lowercase       ,     unet=lowercase       ,     scheduler=lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])



      def __call__(		self       ,     lowercase       ,     lowercase       ,     lowercase = 0.3       ,     lowercase = 0.6       ,     lowercase = 0.5       ,     lowercase = 42       ,     lowercase = 50       ,     lowercase = 7.5       ,     )      ->     Image.Image:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      self.scheduler.step(lowercase       ,     self.scheduler.timesteps[tmax]       ,     lowercase).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
          if i > tmax:
            if i < tmin:  # layout generation phase
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

              a__:	Dict     				=      (mix_factor * latents) + (
                  1 - mix_factor
              ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
              a__:	List[Any]     				=      torch.cat([input] * 2)

            else:  # content generation phase
              a__:	Dict     				=      torch.cat([latents] * 2)

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)


