"""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,
)


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



def 			A__						(  lowercase_    :  str			,   lowercase_    :  Tuple=100			,   lowercase_    :  Optional[Any]=" "	)     ->       List[str]:
			__lowerCamelCase								=				text.split(_UpperCAmelCase	)
			return [character.join(text[i : i + n]	).strip() for i in range(0			,   len(_UpperCAmelCase	)			,   _UpperCAmelCase	)]



def 			A__						(  lowercase_    :  Optional[int]	)     ->       dict:
			__lowerCamelCase		,__lowerCamelCase								=				[], []
			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 			A__						(  lowercase_    :  Any			,   lowercase_    :  int			,   lowercase_    :  str	)     ->       dict:
			__lowerCamelCase								=				ctx_tokenizer(
			    documents['title']			,   documents['text']			,   truncation=_UpperCAmelCase			,   padding='longest'			,   return_tensors='pt'	)['input_ids']
			__lowerCamelCase								=				ctx_encoder(input_ids.to(device=_UpperCAmelCase	)			,   return_dict=_UpperCAmelCase	).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}



def 			A__						(  lowercase_    :  Union[str, Any]			,   lowercase_    :  str			,   lowercase_    :  Any			,   )     ->       Union[str, Any]:
			######################################
			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
			__lowerCamelCase								=				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
			__lowerCamelCase								=				dataset.map(_UpperCAmelCase			,   batched=_UpperCAmelCase			,   num_proc=processing_args.num_proc	)

			# And compute the embeddings
			__lowerCamelCase								=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=_UpperCAmelCase	)
			__lowerCamelCase								=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
			__lowerCamelCase								=				Features(
			    {'text': Value('string'	), 'title': Value('string'	), 'embeddings': Sequence(Value('float32'	)	)}	)  # optional, save as float32 instead of float64 to save space
			__lowerCamelCase								=				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
			__lowerCamelCase								=				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
			__lowerCamelCase								=				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
			__lowerCamelCase								=				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
			__lowerCamelCase								=				RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name			,   index_name='custom'			,   indexed_dataset=_UpperCAmelCase	)
			__lowerCamelCase								=				RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,   retriever=_UpperCAmelCase	)
			__lowerCamelCase								=				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'	)
			######################################

			__lowerCamelCase								=				rag_example_args.question or 'What does Moses\' rod turn into ?'
			__lowerCamelCase								=				tokenizer.question_encoder(_UpperCAmelCase			,   return_tensors='pt'	)['input_ids']
			__lowerCamelCase								=				model.generate(_UpperCAmelCase	)
			__lowerCamelCase								=				tokenizer.batch_decode(_UpperCAmelCase			,   skip_special_tokens=_UpperCAmelCase	)[0]
			logger.info('Q: ' + question	)
			logger.info('A: ' + generated_string	)






@dataclass
class     _SCREAMING_SNAKE_CASE       :



			'''simple docstring'''



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






@dataclass
class     _SCREAMING_SNAKE_CASE       :



			'''simple docstring'''



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






@dataclass
class     _SCREAMING_SNAKE_CASE       :



			'''simple docstring'''



			__UpperCAmelCase  :       List[Any]						=						field(
			    default=768						,							metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						,							)
			__UpperCAmelCase  :       List[Any]						=						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)

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







"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :		Any        =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE  :		int        =      {
    '''microsoft/mpnet-base''': 5_1_2,
}

_SCREAMING_SNAKE_CASE  :		Tuple        =      {
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[int]						=						VOCAB_FILES_NAMES
   __UpperCAmelCase  :       Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase  :       Dict						=						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase  :       Dict						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase  :       Union[str, Any]						=						MPNetTokenizer
   __UpperCAmelCase  :       List[Any]						=						["input_ids", "attention_mask"]







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE="<s>"   ,							_SCREAMING_SNAKE_CASE="</s>"   ,							_SCREAMING_SNAKE_CASE="</s>"   ,							_SCREAMING_SNAKE_CASE="<s>"   ,							_SCREAMING_SNAKE_CASE="[UNK]"   ,							_SCREAMING_SNAKE_CASE="<pad>"   ,							_SCREAMING_SNAKE_CASE="<mask>"   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							**_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      super().__init__(
          _SCREAMING_SNAKE_CASE   ,							tokenizer_file=_SCREAMING_SNAKE_CASE   ,							do_lower_case=_SCREAMING_SNAKE_CASE   ,							bos_token=_SCREAMING_SNAKE_CASE   ,							eos_token=_SCREAMING_SNAKE_CASE   ,							sep_token=_SCREAMING_SNAKE_CASE   ,							cls_token=_SCREAMING_SNAKE_CASE   ,							unk_token=_SCREAMING_SNAKE_CASE   ,							pad_token=_SCREAMING_SNAKE_CASE   ,							mask_token=_SCREAMING_SNAKE_CASE   ,							tokenize_chinese_chars=_SCREAMING_SNAKE_CASE   ,							strip_accents=_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							)

      __lowerCamelCase								=				json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
      if (
          pre_tok_state.get('lowercase'   ,							_SCREAMING_SNAKE_CASE  ) != do_lower_case
          or pre_tok_state.get('strip_accents'   ,							_SCREAMING_SNAKE_CASE  ) != strip_accents
      ):
         __lowerCamelCase								=				getattr(_SCREAMING_SNAKE_CASE   ,							pre_tok_state.pop('type'  )  )
         __lowerCamelCase								=				do_lower_case
         __lowerCamelCase								=				strip_accents
         __lowerCamelCase								=				pre_tok_class(**_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				do_lower_case







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      if self._mask_token is None:
         if self.verbose:
            logger.error('Using mask_token, but it is not set yet.'  )
         return None
      return str(self._mask_token  )







   @mask_token.setter
   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				AddedToken(_SCREAMING_SNAKE_CASE   ,							lstrip=_SCREAMING_SNAKE_CASE   ,							rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ) else value
      __lowerCamelCase								=				value







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None  ):


      '''simple docstring'''

      __lowerCamelCase								=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
         return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


      '''simple docstring'''

      __lowerCamelCase								=				[self.sep_token_id]
      __lowerCamelCase								=				[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep  ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


      '''simple docstring'''

      __lowerCamelCase								=				self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,							name=_SCREAMING_SNAKE_CASE  )
      return tuple(_SCREAMING_SNAKE_CASE  )






