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




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str=1_0_0  ,       lowerCAmelCase__							:    Union[str, Any]=" "  ):
 __a			:  Optional[Any]		    =  text.split(lowerCAmelCase__  )
 return [character.join(text[i : i + n]  ).strip() for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  )]




def        __UpperCamelCase					(    lowerCAmelCase__							:    dict  ):
 __a    ,	__a			:  List[str]		    =  [], []
 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        __UpperCamelCase					(    lowerCAmelCase__							:    dict  ,       lowerCAmelCase__							:    DPRContextEncoder  ,       lowerCAmelCase__							:    DPRContextEncoderTokenizerFast  ):
 __a			:  str		    =  ctx_tokenizer(
     documents['''title''']  ,       documents['''text''']  ,       truncation=lowerCAmelCase__  ,       padding='''longest'''  ,       return_tensors='''pt'''  )['''input_ids''']
 __a			:  List[Any]		    =  ctx_encoder(input_ids.to(device=lowerCAmelCase__  )  ,       return_dict=lowerCAmelCase__  ).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}




def        __UpperCamelCase					(    lowerCAmelCase__							:    "RagExampleArguments"  ,       lowerCAmelCase__							:    "ProcessingArguments"  ,       lowerCAmelCase__							:    "IndexHnswArguments"  ,       ):
 ######################################
 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			:  Union[str, 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
 __a			:  Dict		    =  dataset.map(lowerCAmelCase__  ,       batched=lowerCAmelCase__  ,       num_proc=processing_args.num_proc  )

 # And compute the embeddings
 __a			:  int		    =  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=lowerCAmelCase__  )
 __a			:  Dict		    =  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
 __a			:  Dict		    =  Features(
     {'''text''': Value('''string'''  ), '''title''': Value('''string'''  ), '''embeddings''': Sequence(Value('''float32'''  )  )}  )  # optional, save as float32 instead of float64 to save space
 __a			:  Union[str, Any]		    =  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
 __a			:  Union[str, Any]		    =  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
 __a			:  Optional[Any]		    =  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
 __a			:  List[str]		    =  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



@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							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'"}   ,)
   _SCREAMING_SNAKE_CASE				:							Optional[str]     	=				field(
       default=__lowercase   ,metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}   ,)
   _SCREAMING_SNAKE_CASE				:							str     	=				field(
       default="facebook/rag-sequence-nq"   ,metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}   ,)
   _SCREAMING_SNAKE_CASE				:							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'"
           )
       }   ,)
   _SCREAMING_SNAKE_CASE				:							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 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				field(
       default=__lowercase   ,metadata={
           "help": "The number of processes to use to split the documents into passages. Default is single process."
       }   ,)
   _SCREAMING_SNAKE_CASE				:							int     	=				field(
       default=16   ,metadata={
           "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
       }   ,)





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

      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)


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        snake_case_ ,    padding=self.padding ,    max_length=self.max_length ,    pad_to_multiple_of=self.pad_to_multiple_of ,    return_tensors='''pt''' if labels is None else None ,    )

    if labels is None:
     return batch

    __a			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
