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,
)


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






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__=1_0_0			,						lowerCAmelCase__=" "			):
       lowerCAmelCase__  			=      text.split(lowerCAmelCase__			)
       return [character.join(text[i : i + n]			).strip() for i in range(0			,						len(lowerCAmelCase__			)			,						lowerCAmelCase__			)]






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__			,      lowerCAmelCase__  			=      [], []
       for title, text in zip(documents['title']			,						documents['text']			):
              if text is not None:
                     for passage in split_text(lowerCAmelCase__			):
                            titles.append(title if title is not None else ''			)
                            texts.append(lowerCAmelCase__			)
       return {"title": titles, "text": texts}






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      ctx_tokenizer(
           documents['title']			,						documents['text']			,						truncation=lowerCAmelCase__			,						padding='longest'			,						return_tensors='pt'			)['input_ids']
       lowerCAmelCase__  			=      ctx_encoder(input_ids.to(device=lowerCAmelCase__			)			,						return_dict=lowerCAmelCase__			).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						):
       ######################################
       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__  			=      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__  			=      dataset.map(lowerCAmelCase__			,						batched=lowerCAmelCase__			,						num_proc=processing_args.num_proc			)

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

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

       # And save the index
       lowerCAmelCase__  			=      os.path.join(rag_example_args.output_dir			,						'my_knowledge_dataset_hnsw_index.faiss'			)
       dataset.get_index('embeddings'			).save(lowerCAmelCase__			)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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

       # Easy way to load the model
       lowerCAmelCase__  			=      RagRetriever.from_pretrained(
           rag_example_args.rag_model_name			,						index_name='custom'			,						indexed_dataset=lowerCAmelCase__			)
       lowerCAmelCase__  			=      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,						retriever=lowerCAmelCase__			)
       lowerCAmelCase__  			=      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__  			=      rag_example_args.question or 'What does Moses\' rod turn into ?'
       lowerCAmelCase__  			=      tokenizer.question_encoder(lowerCAmelCase__			,						return_tensors='pt'			)['input_ids']
       lowerCAmelCase__  			=      model.generate(lowerCAmelCase__			)
       lowerCAmelCase__  			=      tokenizer.batch_decode(lowerCAmelCase__			,						skip_special_tokens=lowerCAmelCase__			)[0]
       logger.info('Q: ' + question			)
       logger.info('A: ' + generated_string			)
@dataclass
class        a_				:






 '''simple docstring'''






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






@dataclass
class        a_				:






 '''simple docstring'''






 UpperCAmelCase_							=      field(
     default=SCREAMING_SNAKE_CASE ,  metadata={
         'help': 'The number of processes to use to split the documents into passages. Default is single process.'
     } ,  )
 UpperCAmelCase_							=      field(
     default=16 ,  metadata={
         'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
     } ,  )






@dataclass
class        a_				:






 '''simple docstring'''






 UpperCAmelCase_							=      field(
     default=768 ,  metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} ,  )
 UpperCAmelCase_							=      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)

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

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      1
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

        lowerCAmelCase__  			=      torch.rand((batch_size, num_channels, height, width))

        return sample




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Any):







        '''simple docstring'''



        if isinstance(image[0]    ,					PIL.Image.Image):
               return np.stack([np.array(lowercase__) for i in image]    ,					axis=0)
        elif isinstance(lowercase__    ,					torch.Tensor):
               return image.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        return image




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      self.to_np(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(
                   image_processor.preprocess(lowercase__)    ,					output_type=lowercase__    ,					)
               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)
        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)

               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        lowerCAmelCase__  			=      image_processor.numpy_to_pil(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)
               for i, o in zip(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      np.array(lowercase__)
                      lowerCAmelCase__  			=      self.to_np(lowercase__) if output_type == 'pil' else (self.to_np(lowercase__) * 255).round()
                      assert (
                          np.abs(in_np - out_np).max() < 1e-6
                      ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      input_pt_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        lowerCAmelCase__  			=      input_np_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_ad).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad).max() < 1e-6




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_list).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list).max() < 1e-6
