





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


_SCREAMING_SNAKE_CASE							=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
_SCREAMING_SNAKE_CASE							=						"""cuda""" if torch.cuda.is_available() else """cpu"""


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a=1_00      ,       __a=" "      ):
	snake_case_						:	str      =		text.split(__A      )
	return [character.join(text[i : i + n]      ).strip() for i in range(0      ,       len(__A      )      ,       __A      )]


def 							SCREAMING_SNAKE_CASE__  (			__a      ):
	snake_case_  ,snake_case_						:	List[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 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ):
	snake_case_						:	List[str]      =		ctx_tokenizer(
	    documents['title']      ,       documents['text']      ,       truncation=__A      ,       padding='longest'      ,       return_tensors='pt'      )['input_ids']
	snake_case_						:	Optional[Any]      =		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      ,       ):
	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_						:	Optional[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_						:	List[str]      =		dataset.map(__A      ,       batched=__A      ,       num_proc=processing_args.num_proc      )

	# And compute the embeddings
	snake_case_						:	int      =		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      ).to(device=__A      )
	snake_case_						:	Tuple      =		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      )
	snake_case_						:	Any      =		Features(
	    {'text': Value('string'      ), 'title': Value('string'      ), 'embeddings': Sequence(Value('float32'      )      )}      )  # optional, save as float32 instead of float64 to save space
	snake_case_						:	str      =		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_						:	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_						:	List[str]      =		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_						:	int      =		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 SCREAMING_SNAKE_CASE_  :
						__magic_name__:	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\'"}				, )
						__magic_name__:	Optional[str]       					=				field(
						    default=snake_case_				, metadata={"help": "Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."}				, )
						__magic_name__:	str       					=				field(
						    default="facebook/rag-sequence-nq"				, metadata={"help": "The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\'"}				, )
						__magic_name__:	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\'"
						        )
						    }				, )
						__magic_name__:	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 SCREAMING_SNAKE_CASE_  :
						__magic_name__:	Optional[int]       					=				field(
						    default=snake_case_				, metadata={
						        "help": "The number of processes to use to split the documents into passages. Default is single process."
						    }				, )
						__magic_name__:	int       					=				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_  :
						__magic_name__:	int       					=				field(
						    default=768				, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}				, )
						__magic_name__:	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)

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






'''simple docstring'''





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  "▁"

lowercase_		       =  {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_		       =  {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase_		       =  {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowercase_		       =  {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Dict          =       ['input_ids', 'attention_mask']

 __lowerCamelCase    :       List[int]          =       []
 __lowerCamelCase    :       List[int]          =       []



 def __init__(self ,   A ,   A ,   A=None ,   A=None ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<pad>" ,   A="<unk>" ,   A="m2m100" ,   A = None ,   A=8 ,   **A ,   )    ->     None:


      """simple docstring"""



      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

      _a      =  language_codes
      _a      =  FAIRSEQ_LANGUAGE_CODES[language_codes]
      _a      =  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

      _a      =  kwargs.get('''additional_special_tokens''' ,   []       )
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(A       )
          for lang_code in fairseq_language_code
          if self.get_lang_token(A       ) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=A ,   tgt_lang=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   unk_token=A ,   pad_token=A ,   language_codes=A ,   sp_model_kwargs=self.sp_model_kwargs ,   num_madeup_words=A ,   **A ,   )

      _a      =  vocab_file
      _a      =  load_json(A       )
      _a      =  {v: k for k, v in self.encoder.items()}
      _a      =  spm_file
      _a      =  load_spm(A ,   self.sp_model_kwargs       )

      _a      =  len(self.encoder       )

      _a      =  {
          self.get_lang_token(A       ): self.encoder_size + i for i, lang_code in enumerate(A       )
      }
      _a      =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(A       )}
      _a      =  {v: k for k, v in self.lang_token_to_id.items()}

      _a      =  src_lang if src_lang is not None else '''en'''
      _a      =  tgt_lang
      _a      =  self.get_lang_id(self._src_lang       )
      self.set_src_lang_special_tokens(self._src_lang       )

      _a      =  num_madeup_words



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return len(self.encoder       ) + len(self.lang_token_to_id       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang       )



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


      """simple docstring"""



      return self.sp_model.encode(A ,   out_type=A       )



 def 					a__  (self ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
      return self.encoder.get(A ,   self.encoder[self.unk_token]       )



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
      return self.decoder.get(A ,   self.unk_token       )



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  []
      _a      =  ''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A       ) + token
                _a      =  []
           else:
                current_sub_tokens.append(A       )
      out_string += self.sp_model.decode(A       )
      return out_string.strip()



 def 					a__  (self ,   A ,   A = None ,   A = False       )    ->     List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A ,   token_ids_a=A ,   already_has_special_tokens=A       )

      _a      =  [1] * len(self.prefix_tokens       )
      _a      =  [1] * len(self.suffix_tokens       )
      if token_ids_a is None:
           return prefix_ones + ([0] * len(A       )) + suffix_ones
      return prefix_ones + ([0] * len(A       )) + ([0] * len(A       )) + suffix_ones



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  {self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab



 def __getstate__(self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      return state



 def __setstate__(self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''       ):
           _a      =  {}

      _a      =  load_spm(self.spm_file ,   self.sp_model_kwargs       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  Path(A       )
      if not save_dir.is_dir():
           raise OSError(f'''{save_directory} should be a directory'''       )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
      )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
      )

      save_json(self.encoder ,   A       )

      if os.path.abspath(self.spm_file       ) != os.path.abspath(A       ) and os.path.isfile(self.spm_file       ):
           copyfile(self.spm_file ,   A       )
      elif not os.path.isfile(self.spm_file       ):
           with open(A ,   '''wb'''       ) as fi:
                _a      =  self.sp_model.serialized_model_proto()
                fi.write(A       )

      return (str(A       ), str(A       ))



 def 					a__  (self ,   A ,   A = "en" ,   A = None ,   A = "ro" ,   **A ,   )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  src_lang
      _a      =  tgt_lang
      self.set_src_lang_special_tokens(self.src_lang       )
      return super().prepare_seqaseq_batch(A ,   A ,   **A       )



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Union[str, Any]:


      """simple docstring"""



      if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
      _a      =  src_lang
      _a      =  self(A ,   add_special_tokens=A ,   **A       )
      _a      =  self.get_lang_id(A       )
      _a      =  tgt_lang_id
      return inputs



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      self.set_src_lang_special_tokens(self.src_lang       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      self.set_tgt_lang_special_tokens(self.tgt_lang       )



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      return self.lang_code_to_token[lang]



 def 					a__  (self ,   A       )    ->     int:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      return self.lang_token_to_id[lang_token]





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  sentencepiece.SentencePieceProcessor(**__A)
     spm.Load(str(__A))
     return spm




def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(__A  ,	'''r''') as f:
          return json.load(__A)




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     with open(__A  ,	'''w''') as f:
          json.dump(__A  ,	__A  ,	indent=2)



