

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


__lowercase     : int       =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowercase     : Optional[Any]       =						'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       lowercase					(	__A :					str ,    __A :					Union[str, Any]=100 ,    __A :					Optional[Any]=" "		)				->			List[str]:





    '''simple docstring'''




    snake_case							:		Dict        =		text.split(__A		)
    return [character.join(text[i : i + n]		).strip() for i in range(0 ,    len(__A		) ,    __A		)]







def       lowercase					(	__A :					dict		)				->			dict:





    '''simple docstring'''




    snake_case      ,		snake_case							:		Optional[Any]        =		[], []
    for title, text in zip(documents["""title"""] ,    documents["""text"""]		):
        if text is not None:
            for passage in split_text(__A		):
                titles.append(title if title is not None else """"""		)
                texts.append(__A		)
    return {"title": titles, "text": texts}







def       lowercase					(	__A :					dict ,    __A :					DPRContextEncoder ,    __A :					DPRContextEncoderTokenizerFast		)				->			dict:





    '''simple docstring'''




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







def       lowercase					(	__A :					"RagExampleArguments" ,    __A :					"ProcessingArguments" ,    __A :					"IndexHnswArguments" ,    )				->			int:





    '''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
    snake_case							:		Any        =		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
    snake_case							:		Optional[Any]        =		dataset.map(__A ,    batched=__A ,    num_proc=processing_args.num_proc		)

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

    # And finally save your dataset
    snake_case							:		Optional[Any]        =		os.path.join(rag_example_args.output_dir ,    """my_knowledge_dataset"""		)
    dataset.save_to_disk(__A		)
    # 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
    snake_case							:		Union[str, Any]        =		faiss.IndexHNSWFlat(index_hnsw_args.d ,    index_hnsw_args.m ,    faiss.METRIC_INNER_PRODUCT		)
    dataset.add_faiss_index("""embeddings""" ,    custom_index=__A		)

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



@dataclass
class 						_A     :





  '''simple docstring'''



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







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Optional[int]					=   field(
      default=snake_case						,    metadata={
          '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
      }						,    )
  __lowerCamelCase			:      int					=   field(
      default=1_6						,    metadata={
          '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
      }						,    )







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      int					=   field(
      default=7_6_8						,    metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}						,    )
  __lowerCamelCase			:      int					=   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     : Dict       =						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     __lowercase    ,  __lowercase    ,  __lowercase     : Dict       =						parser.parse_args_into_dataclasses()
     with TemporaryDirectory() as tmp_dir:
          __lowercase     : Union[str, Any]       =						rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Dict[str, int] = None			,						lowerCamelCase__	:    int = 3_2			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    List[str]=7			,						lowerCamelCase__	:    int=3_0			,						lowerCamelCase__	:    List[Any]=4_0_0			,						lowerCamelCase__	:    int=3			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		do_resize
         A_							       =		size if size is not None else {'''shortest_edge''': 2_8_8}
         A_							       =		size_divisor
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		do_center_crop
         A_							       =		image_mean
         A_							       =		image_std
         A_							       =		do_pad
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		min_resolution
         A_							       =		max_resolution

   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int=False       ) ->			Dict:
         """simple docstring"""





         if not batched:
               A_							       =		self.size['''shortest_edge''']
               A_							       =		image_inputs[0]
               if isinstance(lowerCamelCase__			,						Image.Image       ):
                     A_       ,A_							       =		image.size
               else:
                     A_       ,A_							       =		image.shape[1], image.shape[2]
               A_							       =		size / min(lowerCamelCase__			,						lowerCamelCase__       )
               if h < w:
                     A_       ,A_							       =		size, scale * w
               else:
                     A_       ,A_							       =		scale * h, size

               A_							       =		int((1_3_3_3 / 8_0_0) * size       )
               if max(lowerCamelCase__			,						lowerCamelCase__       ) > max_size:
                     A_							       =		max_size / max(lowerCamelCase__			,						lowerCamelCase__       )
                     A_							       =		newh * scale
                     A_							       =		neww * scale

               A_       ,A_							       =		int(newh + 0.5       ), int(neww + 0.5       )
               A_       ,A_							       =		(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

         else:
               A_							       =		[]
               for image in image_inputs:
                     A_       ,A_							       =		self.get_expected_values([image]       )
                     expected_values.append((expected_height, expected_width)       )
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[0]       )[0]
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[1]       )[1]

         return expected_height, expected_width



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    BridgeTowerImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		BridgeTowerImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_mean'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_std'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_normalize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_resize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size_divisor'''       )       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						Image.Image       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						numpify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						np.ndarray       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						torchify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						torch.Tensor       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





