




"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_snake_case					   =     logging.get_logger(__name__)


if is_vision_available():
 import PIL






class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : Dict    =      ['''pixel_values''']


 def __init__(  self	:		Tuple							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		PILImageResampling = PILImageResampling.BICUBIC							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Union[int, float] = 1 / 255							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		bool = True							,		**UpperCAmelCase__	:		Dict							,		)      -> None:
   super().__init__(**UpperCAmelCase__							)
   _a						: Dict    =			size if size is not None else {"""shortest_edge""": 224}
   _a						: Optional[Any]    =			get_size_dict(UpperCAmelCase__							,		default_to_square=UpperCAmelCase__							)
   _a						: List[str]    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
   _a						: List[str]    =			get_size_dict(UpperCAmelCase__							)

   _a						: Optional[Any]    =			do_resize
   _a						: Dict    =			size
   _a						: List[str]    =			resample
   _a						: List[Any]    =			do_center_crop
   _a						: Optional[Any]    =			crop_size
   _a						: Any    =			do_rescale
   _a						: Optional[Any]    =			rescale_factor
   _a						: Union[str, Any]    =			do_normalize
   _a						: Any    =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   _a						: Tuple    =			image_std if image_std is not None else OPENAI_CLIP_STD
   _a						: int    =			do_convert_rgb


 def _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Dict[str, int]							,		UpperCAmelCase__	:		PILImageResampling = PILImageResampling.BICUBIC							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Union[str, Any]							,		)      -> np.ndarray:
   _a						: Dict    =			get_size_dict(UpperCAmelCase__							,		default_to_square=UpperCAmelCase__							)
   _a						: Union[str, Any]    =			get_resize_output_image_size(
       UpperCAmelCase__							,		size=(size["""height"""], size["""width"""])							,		default_to_square=UpperCAmelCase__							)
   return resize(UpperCAmelCase__							,		size=UpperCAmelCase__							,		resample=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Dict[str, int]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Optional[int]							,		)      -> np.ndarray:
   _a						: Tuple    =			get_size_dict(UpperCAmelCase__							)
   return center_crop(UpperCAmelCase__							,		size=(size["""height"""], size["""width"""])							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Union[int, float]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		str							,		)      -> List[str]:
   return rescale(UpperCAmelCase__							,		scale=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Union[float, List[float]]							,		UpperCAmelCase__	:		Union[float, List[float]]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Optional[Any]							,		)      -> np.ndarray:
   return normalize(UpperCAmelCase__							,		mean=UpperCAmelCase__							,		std=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)







 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		ImageInput							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		PILImageResampling = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		int = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		float = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		Optional[Union[str, TensorType]] = None							,		UpperCAmelCase__	:		Optional[ChannelDimension] = ChannelDimension.FIRST							,		**UpperCAmelCase__	:		Tuple							,		)      -> PIL.Image.Image:
   _a						: Tuple    =			do_resize if do_resize is not None else self.do_resize
   _a						: Dict    =			size if size is not None else self.size
   _a						: Optional[int]    =			get_size_dict(UpperCAmelCase__							,		default_to_square=UpperCAmelCase__							)
   _a						: List[Any]    =			resample if resample is not None else self.resample
   _a						: Optional[int]    =			do_center_crop if do_center_crop is not None else self.do_center_crop
   _a						: Tuple    =			crop_size if crop_size is not None else self.crop_size
   _a						: List[str]    =			get_size_dict(UpperCAmelCase__							)
   _a						: Union[str, Any]    =			do_rescale if do_rescale is not None else self.do_rescale
   _a						: Optional[int]    =			rescale_factor if rescale_factor is not None else self.rescale_factor
   _a						: str    =			do_normalize if do_normalize is not None else self.do_normalize
   _a						: Dict    =			image_mean if image_mean is not None else self.image_mean
   _a						: List[str]    =			image_std if image_std is not None else self.image_std
   _a						: int    =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   _a						: Union[str, Any]    =			make_list_of_images(UpperCAmelCase__							)

   if not valid_images(UpperCAmelCase__							):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""							)

   if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True."""							)

   if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     _a						: List[str]    =			[convert_to_rgb(UpperCAmelCase__							) for image in images]

   # All transformations expect numpy arrays.
   _a						: Dict    =			[to_numpy_array(UpperCAmelCase__							) for image in images]

   if do_resize:
     _a						: Dict    =			[self.resize(image=UpperCAmelCase__							,		size=UpperCAmelCase__							,		resample=UpperCAmelCase__							) for image in images]

   if do_center_crop:
     _a						: str    =			[self.center_crop(image=UpperCAmelCase__							,		size=UpperCAmelCase__							) for image in images]

   if do_rescale:
     _a						: List[str]    =			[self.rescale(image=UpperCAmelCase__							,		scale=UpperCAmelCase__							) for image in images]

   if do_normalize:
     _a						: Any    =			[self.normalize(image=UpperCAmelCase__							,		mean=UpperCAmelCase__							,		std=UpperCAmelCase__							) for image in images]

   _a						: Dict    =			[to_channel_dimension_format(UpperCAmelCase__							,		UpperCAmelCase__							) for image in images]

   _a						: List[Any]    =			{"""pixel_values""": images}
   return BatchFeature(data=UpperCAmelCase__							,		tensor_type=UpperCAmelCase__							)









"""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,
)


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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



  '''simple docstring'''
  _a						: str    =			text.split(UpperCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0  ,     len(UpperCamelCase__       )  ,     UpperCamelCase__       )]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a						: Optional[int]    =			[], []
  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     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			ctx_tokenizer(
      documents["""title"""]  ,     documents["""text"""]  ,     truncation=UpperCamelCase__  ,     padding="""longest"""  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Optional[int]    =			ctx_encoder(input_ids.to(device=UpperCamelCase__       )  ,     return_dict=UpperCamelCase__       ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''simple docstring'''
  ######################################
  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						: Dict    =			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						: Any    =			dataset.map(UpperCamelCase__  ,     batched=UpperCamelCase__  ,     num_proc=processing_args.num_proc       )

  # And compute the embeddings
  _a						: Optional[Any]    =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCamelCase__       )
  _a						: str    =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
  _a						: int    =			Features(
      {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  _a						: str    =			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						: List[Any]    =			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						: List[str]    =			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						: List[str]    =			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						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[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						: Optional[Any]    =			rag_example_args.question or """What does Moses' rod turn into ?"""
  _a						: int    =			tokenizer.question_encoder(UpperCamelCase__  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Union[str, Any]    =			model.generate(UpperCamelCase__       )
  _a						: Any    =			tokenizer.batch_decode(UpperCamelCase__  ,     skip_special_tokens=UpperCamelCase__       )[0]
  logger.info("""Q: """ + question       )
  logger.info("""A: """ + generated_string       )






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







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : Optional[int]    =      field(
     default=snake_case_	,  metadata={
         '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
     }	,  )
 UpperCamelCase      : int    =      field(
     default=16	,  metadata={
         '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
     }	,  )







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

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

