

"""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,
)


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




def     _A    (    UpperCamelCase_	:			str,     UpperCamelCase_	:			str=100,     UpperCamelCase_	:			Union[str, Any]=" ")      ->		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     _A    (    UpperCamelCase_	:			dict)      ->		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     _A    (    UpperCamelCase_	:			dict,     UpperCamelCase_	:			DPRContextEncoder,     UpperCamelCase_	:			DPRContextEncoderTokenizerFast)      ->		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     _A    (    UpperCamelCase_	:			"RagExampleArguments",     UpperCamelCase_	:			"ProcessingArguments",     UpperCamelCase_	:			"IndexHnswArguments",     )      ->		Dict:
   '''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

   ######################################
   logger.info("Step 3 - Load RAG")
   ######################################

   # Easy way to load the model
   __lowercase								=		RagRetriever.from_pretrained(
       rag_example_args.rag_model_name,     index_name="custom",     indexed_dataset=_A)
   __lowercase								=		RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,     retriever=_A)
   __lowercase								=		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")
   ######################################

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




@dataclass
class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      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      :   Optional[str]  						=      field(
     default=lowerCAmelCase__	,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=lowerCAmelCase__	,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}	,)







@dataclass
class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   Optional[int]  						=      field(
     default=lowerCAmelCase__	,metadata={
         "help": "The number of processes to use to split the documents into passages. Default is single process."
     }	,)
 __UpperCAmelCase      :   int  						=      field(
     default=1_6	,metadata={
         "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
     }	,)







@dataclass
class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   int  						=      field(
     default=7_6_8	,metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}	,)
 __UpperCAmelCase      :   int  						=      field(
     default=1_2_8	,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)

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







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__A	:   Any	=					logging.get_logger(__name__)

__A	:   Union[str, Any]	=					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A	:   Optional[int]	=					{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

__A	:   Optional[Any]	=					{
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}




@lru_cache()
def        __UpperCamelCase     (      )				->int:



							"""simple docstring"""
							lowerCamelCase_								=(
							    list(range(ord("""!"""		)      ,  ord("""~"""		) + 1		)		) + list(range(ord("""¡"""		)      ,  ord("""¬"""		) + 1		)		) + list(range(ord("""®"""		)      ,  ord("""ÿ"""		) + 1		)		)
							)
							lowerCamelCase_								=bs[:]
							lowerCamelCase_								=0
							for b in range(2**8		):
														if b not in bs:
																					bs.append(_A		)
																					cs.append(2**8 + n		)
																					n += 1
							lowerCamelCase_								=[chr(_A		) for n in cs]
							return dict(zip(_A      ,  _A		)		)




def        __UpperCamelCase     (      _A					:							Optional[int]		)				->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_								=set()
							lowerCamelCase_								=word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)		)
														lowerCamelCase_								=char
							return pairs






class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Any	=		VOCAB_FILES_NAMES
						_UpperCamelCase:Any	=		PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase:Optional[int]	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase:List[str]	=		["input_ids", "attention_mask"]







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE="replace"     ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"     ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"     ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=False     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[Any]:
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else bos_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else eos_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else unk_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else pad_token
													super().__init__(
													    errors=_SCREAMING_SNAKE_CASE     ,						unk_token=_SCREAMING_SNAKE_CASE     ,						bos_token=_SCREAMING_SNAKE_CASE     ,						eos_token=_SCREAMING_SNAKE_CASE     ,						pad_token=_SCREAMING_SNAKE_CASE     ,						add_prefix_space=_SCREAMING_SNAKE_CASE     ,						add_bos_token=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE     ,						)
													lowerCamelCase_								=add_bos_token

													with open(_SCREAMING_SNAKE_CASE     ,						encoding="""utf-8"""							) as vocab_handle:
																				lowerCamelCase_								=json.load(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								={v: k for k, v in self.encoder.items()}
													lowerCamelCase_								=errors  # how to handle errors in decoding
													lowerCamelCase_								=bytes_to_unicode()
													lowerCamelCase_								={v: k for k, v in self.byte_encoder.items()}
													with open(_SCREAMING_SNAKE_CASE     ,						encoding="""utf-8"""							) as merges_handle:
																				lowerCamelCase_								=merges_handle.read().split("""\n"""							)[1:-1]
													lowerCamelCase_								=[tuple(merge.split()							) for merge in bpe_merges]
													lowerCamelCase_								=dict(zip(_SCREAMING_SNAKE_CASE     ,						range(len(_SCREAMING_SNAKE_CASE							)							)							)							)
													lowerCamelCase_								={}
													lowerCamelCase_								=add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													lowerCamelCase_								=re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)







						@property
						def        _snake_case				(			self							)->							str:
													return len(self.encoder							)







						def        _snake_case				(			self							)->							Optional[Any]:
													return dict(self.encoder     ,						**self.added_tokens_encoder							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													if token in self.cache:
																				return self.cache[token]
													lowerCamelCase_								=tuple(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=get_pairs(_SCREAMING_SNAKE_CASE							)

													if not pairs:
																				return token

													while True:
																				lowerCamelCase_								=min(_SCREAMING_SNAKE_CASE     ,						key=lambda _SCREAMING_SNAKE_CASE							: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE     ,						float("""inf"""							)							)							)
																				if bigram not in self.bpe_ranks:
																											break
																				lowerCamelCase_	,   lowerCamelCase_								=bigram
																				lowerCamelCase_								=[]
																				lowerCamelCase_								=0
																				while i < len(_SCREAMING_SNAKE_CASE							):
																											try:
																																		lowerCamelCase_								=word.index(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
																											except ValueError:
																																		new_word.extend(word[i:]							)
																																		break
																											else:
																																		new_word.extend(word[i:j]							)
																																		lowerCamelCase_								=j

																											if word[i] == first and i < len(_SCREAMING_SNAKE_CASE							) - 1 and word[i + 1] == second:
																																		new_word.append(first + second							)
																																		i += 2
																											else:
																																		new_word.append(word[i]							)
																																		i += 1
																				lowerCamelCase_								=tuple(_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=new_word
																				if len(_SCREAMING_SNAKE_CASE							) == 1:
																											break
																				else:
																											lowerCamelCase_								=get_pairs(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=""" """.join(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=word
													return word







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=None							)->							Any:
													if self.add_bos_token:
																				lowerCamelCase_								=[self.bos_token_id]
													else:
																				lowerCamelCase_								=[]

													lowerCamelCase_								=bos_token_ids + token_ids_a

													if token_ids_a is None:
																				return output

													return output + bos_token_ids + token_ids_a







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = False							)->							List[int]:
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_SCREAMING_SNAKE_CASE     ,						token_ids_a=_SCREAMING_SNAKE_CASE     ,						already_has_special_tokens=_SCREAMING_SNAKE_CASE							)

													if not self.add_bos_token:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_SCREAMING_SNAKE_CASE     ,						token_ids_a=_SCREAMING_SNAKE_CASE     ,						already_has_special_tokens=_SCREAMING_SNAKE_CASE							)

													if token_ids_a is None:
																				return [1] + ([0] * len(_SCREAMING_SNAKE_CASE							))
													return [1] + ([0] * len(_SCREAMING_SNAKE_CASE							)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE							))







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Any:
													lowerCamelCase_								=[]
													for token in re.findall(self.pat     ,						_SCREAMING_SNAKE_CASE							):
																				lowerCamelCase_								="""""".join(
																				    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																				bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE							).split(""" """							)							)
													return bpe_tokens







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[str]:
													return self.encoder.get(_SCREAMING_SNAKE_CASE     ,						self.encoder.get(self.unk_token							)							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													return self.decoder.get(_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													lowerCamelCase_								="""""".join(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""     ,						errors=self.errors							)
													return text







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							Tuple[str]:
													if not os.path.isdir(_SCREAMING_SNAKE_CASE							):
																				logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
																				return
													lowerCamelCase_								=os.path.join(
													    _SCREAMING_SNAKE_CASE     ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													lowerCamelCase_								=os.path.join(
													    _SCREAMING_SNAKE_CASE     ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

													with open(_SCREAMING_SNAKE_CASE     ,						"""w"""     ,						encoding="""utf-8"""							) as f:
																				f.write(json.dumps(self.encoder     ,						indent=2     ,						sort_keys=_SCREAMING_SNAKE_CASE     ,						ensure_ascii=_SCREAMING_SNAKE_CASE							) + """\n"""							)

													lowerCamelCase_								=0
													with open(_SCREAMING_SNAKE_CASE     ,						"""w"""     ,						encoding="""utf-8"""							) as writer:
																				writer.write("""#version: 0.2\n"""							)
																				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,						key=lambda _SCREAMING_SNAKE_CASE							: kv[1]							):
																											if index != token_index:
																																		logger.warning(
																																		    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																																		    """ Please check that the tokenizer is not corrupted!"""							)
																																		lowerCamelCase_								=token_index
																											writer.write(""" """.join(_SCREAMING_SNAKE_CASE							) + """\n"""							)
																											index += 1

													return vocab_file, merge_file







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=False     ,						**_SCREAMING_SNAKE_CASE							)->							Optional[Any]:
													lowerCamelCase_								=kwargs.pop("""add_prefix_space"""     ,						self.add_prefix_space							)
													if is_split_into_words or add_prefix_space:
																				lowerCamelCase_								=""" """ + text
													return (text, kwargs)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[int]:
													lowerCamelCase_								=[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE     ,						add_special_tokens=_SCREAMING_SNAKE_CASE							) + [self.eos_token_id]							)
													if len(_SCREAMING_SNAKE_CASE							) > self.model_max_length:
																				lowerCamelCase_								=input_ids[-self.model_max_length :]
													return input_ids
