






"""simple docstring"""




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

_snake_case					   =     logging.get_logger(__name__)

_snake_case					   =     {'vocab_file': 'tokenizer.model'}

_snake_case					   =     {
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
_snake_case					   =     {
    'hf-internal-testing/llama-tokenizer': 2048,
}
_snake_case					   =     '▁'

_snake_case   ,				_snake_case					   =     '[INST]', '[/INST]'
_snake_case   ,				_snake_case					   =     '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_snake_case					   =     'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'






class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : Optional[int]    =      VOCAB_FILES_NAMES
 UpperCamelCase      : Tuple    =      PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase      : str    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase      : Any    =      ['''input_ids''', '''attention_mask''']


 def __init__(  self	:		Tuple							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		List[str]="<unk>"							,		UpperCAmelCase__	:		Optional[int]="<s>"							,		UpperCAmelCase__	:		Any="</s>"							,		UpperCAmelCase__	:		Union[str, Any]=None							,		UpperCAmelCase__	:		Optional[Dict[str, Any]] = None							,		UpperCAmelCase__	:		Optional[int]=True							,		UpperCAmelCase__	:		Union[str, Any]=False							,		UpperCAmelCase__	:		List[str]=False							,		UpperCAmelCase__	:		Union[str, Any]=True							,		**UpperCAmelCase__	:		Optional[int]							,		)      -> Optional[int]:
   _a						: List[str]    =			{} if sp_model_kwargs is None else sp_model_kwargs
   _a						: str    =			AddedToken(UpperCAmelCase__							,		lstrip=UpperCAmelCase__							,		rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							) else bos_token
   _a						: Union[str, Any]    =			AddedToken(UpperCAmelCase__							,		lstrip=UpperCAmelCase__							,		rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							) else eos_token
   _a						: List[Any]    =			AddedToken(UpperCAmelCase__							,		lstrip=UpperCAmelCase__							,		rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							) else unk_token
   _a						: Any    =			AddedToken(UpperCAmelCase__							,		lstrip=UpperCAmelCase__							,		rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							) else pad_token
   super().__init__(
       bos_token=UpperCAmelCase__							,		eos_token=UpperCAmelCase__							,		unk_token=UpperCAmelCase__							,		pad_token=UpperCAmelCase__							,		add_bos_token=UpperCAmelCase__							,		add_eos_token=UpperCAmelCase__							,		sp_model_kwargs=self.sp_model_kwargs							,		clean_up_tokenization_spaces=UpperCAmelCase__							,		legacy=UpperCAmelCase__							,		**UpperCAmelCase__							,		)
   if legacy:
     logger.warning_once(
         f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
         """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""							)
   _a						: str    =			legacy
   _a						: List[str]    =			vocab_file
   _a						: Union[str, Any]    =			add_bos_token
   _a						: List[Any]    =			add_eos_token
   _a						: Dict    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(UpperCAmelCase__							)


 def __getstate__(  self	:		List[str]							)      -> str:
   _a						: str    =			self.__dict__.copy()
   _a						: str    =			None
   _a						: List[str]    =			self.sp_model.serialized_model_proto()
   return state


 def __setstate__(  self	:		Optional[Any]							,		UpperCAmelCase__	:		List[Any]							)      -> int:
   _a						: Union[str, Any]    =			d
   _a						: Tuple    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


 @property
 def _lowercase			(  self	:		Optional[int]							)      -> Any:
   return self.sp_model.get_piece_size()


 def _lowercase			(  self	:		Dict							)      -> Optional[int]:
   _a						: Union[str, Any]    =			{self.convert_ids_to_tokens(UpperCAmelCase__							): i for i in range(self.vocab_size							)}
   vocab.update(self.added_tokens_encoder							)
   return vocab


 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		Any							,		**UpperCAmelCase__	:		Tuple							)      -> List[str]:
   # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
   # the beginning of the text
   if not self.legacy:
     _a						: List[Any]    =			SPIECE_UNDERLINE + text.replace(UpperCAmelCase__							,		""" """							)
   return super().tokenize(UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Tuple							,		UpperCAmelCase__	:		List[str]							)      -> int:
   if not self.legacy:
     _a						: Optional[int]    =			text.startswith(UpperCAmelCase__							)
     if is_first:
       _a						: str    =			text[1:]

   _a						: List[Any]    =			self.sp_model.encode(UpperCAmelCase__							,		out_type=UpperCAmelCase__							)

   if not self.legacy and not is_first and not text.startswith(""" """							) and tokens[0].startswith(UpperCAmelCase__							):
     _a						: str    =			([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
   return tokens


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		List[Any]							)      -> List[Any]:
   return self.sp_model.piece_to_id(UpperCAmelCase__							)


 def _lowercase			(  self	:		Tuple							,		UpperCAmelCase__	:		Dict							)      -> int:
   _a						: Union[str, Any]    =			self.sp_model.IdToPiece(UpperCAmelCase__							)
   return token


 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		Union[str, Any]							)      -> Optional[int]:
   _a						: Tuple    =			[]
   _a						: List[Any]    =			""""""
   _a						: Optional[Any]    =			False
   for i, token in enumerate(UpperCAmelCase__							):
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
       if not prev_is_special and i != 0:
         out_string += " "
       out_string += self.sp_model.decode(UpperCAmelCase__							) + token
       _a						: Any    =			True
       _a						: Optional[Any]    =			[]
     else:
       current_sub_tokens.append(UpperCAmelCase__							)
       _a						: Any    =			False
   out_string += self.sp_model.decode(UpperCAmelCase__							)
   return out_string


 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		Union[str, Any]							,		UpperCAmelCase__	:		Optional[str] = None							)      -> Tuple[str]:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
   _a						: List[str]    =			os.path.join(
       UpperCAmelCase__							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

   if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase__							) and os.path.isfile(self.vocab_file							):
     copyfile(self.vocab_file							,		UpperCAmelCase__							)
   elif not os.path.isfile(self.vocab_file							):
     with open(UpperCAmelCase__							,		"""wb"""							) as fi:
       _a						: Optional[int]    =			self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase__							)

   return (out_vocab_file,)


 def _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		int=None							)      -> List[Any]:
   _a						: Union[str, Any]    =			[self.bos_token_id] if self.add_bos_token else []
   _a						: Tuple    =			[self.eos_token_id] if self.add_eos_token else []

   _a						: str    =			bos_token_id + token_ids_a + eos_token_id

   if token_ids_a is not None:
     _a						: List[str]    =			output + bos_token_id + token_ids_a + eos_token_id

   return output


 def _lowercase			(  self	:		Optional[int]							,		UpperCAmelCase__	:		List[int]							,		UpperCAmelCase__	:		Optional[List[int]] = None							,		UpperCAmelCase__	:		bool = False							)      -> List[int]:
   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=UpperCAmelCase__							,		token_ids_a=UpperCAmelCase__							,		already_has_special_tokens=UpperCAmelCase__							)

   _a						: Tuple    =			[1] if self.add_bos_token else []
   _a						: Optional[int]    =			[1] if self.add_eos_token else []

   if token_ids_a is None:
     return bos_token_id + ([0] * len(UpperCAmelCase__							)) + eos_token_id
   return (
       bos_token_id
       + ([0] * len(UpperCAmelCase__							))
       + eos_token_id
       + bos_token_id
       + ([0] * len(UpperCAmelCase__							))
       + eos_token_id
   )


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		List[int]							,		UpperCAmelCase__	:		Optional[List[int]] = None							)      -> List[int]:
   _a						: List[Any]    =			[self.bos_token_id] if self.add_bos_token else []
   _a						: str    =			[self.eos_token_id] if self.add_eos_token else []

   _a						: Optional[Any]    =			[0] * len(bos_token_id + token_ids_a + eos_token_id							)

   if token_ids_a is not None:
     output += [1] * len(bos_token_id + token_ids_a + eos_token_id							)

   return output







 def _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		"Conversation"							)      -> List[int]:
   _a						: int    =			list(conversation.iter_texts()							)
   if not all([is_user for is_user, msg in dialogue[::2]]							) or not all(
       [not is_user for is_user, msg in dialogue[1::2]]							):
     raise ValueError(
         """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""							)

   _a						: List[int]    =			[]
   if len(conversation.past_user_inputs							) > 0:
     if not conversation.past_user_inputs[0].startswith(UpperCAmelCase__							) or E_SYS not in conversation.past_user_inputs[0]:
       _a						: Dict    =			(
           B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
       )
   elif not dialogue[0][1].startswith(UpperCAmelCase__							) or E_SYS not in dialogue[0][1]:
     _a						: Optional[int]    =			(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

   dialog_tokens += sum(
       [
           [self.bos_token_id]
           + self.encode(
               f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """							,		add_special_tokens=UpperCAmelCase__							)
           + [self.eos_token_id]
           for prompt, answer in zip(dialogue[::2]							,		dialogue[1::2]							)
       ]							,		[]							,		)
   if not (dialogue[-1][0]):
     raise ValueError(f"""Last message must be from user, got {dialogue[-1]['role']}"""							)
   dialog_tokens += [self.bos_token_id] + self.encode(
       f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""							,		add_special_tokens=UpperCAmelCase__							)
   return dialog_tokens









"""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)

