


'''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


lowerCamelCase							:   Dict  						=	logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase							:   List[Any]  						=	'cuda' if torch.cuda.is_available() else 'cpu'

def _SCREAMING_SNAKE_CASE      (A  , A=100  , A=" "       )       ->       List[str]:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A       )       ->       dict:






       """simple docstring"""







       lowercase__			,lowercase__      			= [], []
       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 _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       dict:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , )       ->       List[str]:






       """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
       lowercase__      			= 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
       lowercase__      			= dataset.map(A  , batched=A  , num_proc=processing_args.num_proc       )

       # And compute the embeddings
       lowercase__      			= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=A       )
       lowercase__      			= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
       lowercase__      			= Features(
           {'''text''': Value('''string'''       ), '''title''': Value('''string'''       ), '''embeddings''': Sequence(Value('''float32'''       )       )}       )  # optional, save as float32 instead of float64 to save space
       lowercase__      			= 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
       lowercase__      			= 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
       lowercase__      			= 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
       lowercase__      			= 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   __lowerCAmelCase						:





      '''simple docstring'''






      lowerCAmelCase__					:     str  					=							field(
          default=str(Path(lowercase_     ).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=lowercase_  ,     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(lowercase_     ).parent / """test_run""" / """dummy-kb"""     )  ,     metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}  ,     )






@dataclass
class   __lowerCAmelCase						:





      '''simple docstring'''






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






@dataclass
class   __lowerCAmelCase						:





      '''simple docstring'''






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

 lowerCamelCase							:   Optional[Any]  						=	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase							:   Union[str, Any]  						=	parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  lowerCamelCase							:   Optional[int]  						=	rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)









"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					BatchEncoding:
								if images is None and text is None:
											raise ValueError('''You have to specify either images or text.'''     )

								# Get only text
								if images is None:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
											return text_encoding

								# add pixel_values
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, Any]			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
											lowerCamelCase__		:		Optional[Any]			=None

								if text_encoding is not None:
											encoding_image_processor.update(lowerCamelCase     )

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




