code: 
  
  
  '''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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=100							,     _UpperCAmelCase=" "				)    ->  List[str]:
  						_a     =			text.split(_UpperCAmelCase				)
  						return [character.join(text[i : i + n]				).strip() for i in range(0							,     len(_UpperCAmelCase				)							,     _UpperCAmelCase				)]
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  dict:
  						_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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  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()}
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     )    ->  Union[str, Any]:
  						######################################
  						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     =			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     =			dataset.map(_UpperCAmelCase							,     batched=_UpperCAmelCase							,     num_proc=processing_args.num_proc				)
  
  						# And compute the embeddings
  						_a     =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				).to(device=_UpperCAmelCase				)
  						_a     =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				)
  						_a     =			Features(
  						    {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space
  						_a     =			dataset.map(
  						    partial(_UpperCAmelCase							,     ctx_encoder=_UpperCAmelCase							,     ctx_tokenizer=_UpperCAmelCase				)							,     batched=_UpperCAmelCase							,     batch_size=processing_args.batch_size							,     features=_UpperCAmelCase							,     )
  
  						# And finally save your dataset
  						_a     =			os.path.join(rag_example_args.output_dir							,     'my_knowledge_dataset'				)
  						dataset.save_to_disk(_UpperCAmelCase				)
  						# 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     =			faiss.IndexHNSWFlat(index_hnsw_args.d							,     index_hnsw_args.m							,     faiss.METRIC_INNER_PRODUCT				)
  						dataset.add_faiss_index('embeddings'							,     custom_index=_UpperCAmelCase				)
  
  						# And save the index
  						_a     =			os.path.join(rag_example_args.output_dir							,     'my_knowledge_dataset_hnsw_index.faiss'				)
  						dataset.get_index('embeddings'				).save(_UpperCAmelCase				)
  						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index
  
  						######################################
  						logger.info('Step 3 - Load RAG'				)
  						######################################
  
  						# Easy way to load the model
  						_a     =			RagRetriever.from_pretrained(
  						    rag_example_args.rag_model_name							,     index_name='custom'							,     indexed_dataset=_UpperCAmelCase				)
  						_a     =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,     retriever=_UpperCAmelCase				)
  						_a     =			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     =			rag_example_args.question or 'What does Moses\' rod turn into ?'
  						_a     =			tokenizer.question_encoder(_UpperCAmelCase							,     return_tensors='pt'				)['input_ids']
  						_a     =			model.generate(_UpperCAmelCase				)
  						_a     =			tokenizer.batch_decode(_UpperCAmelCase							,     skip_special_tokens=_UpperCAmelCase				)[0]
  						logger.info('Q: ' + question				)
  						logger.info('A: ' + generated_string				)
  
  
  
  
  @dataclass
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_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 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_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=1_6			,							metadata={
  							        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
  							    }			,							)
  
  
  
  
  @dataclass
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  field(
  							    default=7_6_8			,							metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}			,							)
  							_A  				=  field(
  							    default=1_2_8			,							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)
  
  
  
style_context: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
  						_a     =			min(img.size				)
  
  						if s < target_image_size:
  												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)
  
  						_a     =			target_image_size / s
  						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
  						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
  						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
  						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
  						return img
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
  						_a     =			2.0 * x - 1.0
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
  						_a     =			x.detach().cpu()
  						if process:
  												_a     =			post_process_tensor(_UpperCAmelCase				)
  						_a     =			x.numpy()
  						if process:
  												_a     =			(255 * x).astype(np.uinta				)
  						_a     =			Image.fromarray(_UpperCAmelCase				)
  						if not x.mode == mode:
  												_a     =			x.convert(_UpperCAmelCase				)
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
  						_a     =			(x + 1.0) / 2.0
  						_a     =			x.permute(1							,     2							,     0				)
  						return x
  
  
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			post_process_tensor(x.squeeze()				)
  						return x.permute(2							,     0							,     1				).unsqueeze(0				)
  
  
  