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,
)


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


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		Optional[Any]=100						,  lowercase_:		int=" " ):
       _UpperCamelCase   :				Tuple       =     text.split(lowercase_ )
       return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(lowercase_ )						,  lowercase_ )]


def SCREAMING_SNAKE_CASE (   lowercase_:		dict ):
       _UpperCamelCase  ,					_UpperCamelCase   :				str       =     [], []
       for title, text in zip(documents['title']						,  documents['text'] ):
              if text is not None:
                     for passage in split_text(lowercase_ ):
                            titles.append(title if title is not None else '' )
                            texts.append(lowercase_ )
       return {"title": titles, "text": texts}


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


def SCREAMING_SNAKE_CASE (   lowercase_:		"RagExampleArguments"						,  lowercase_:		"ProcessingArguments"						,  lowercase_:		"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
       _UpperCamelCase   :				str       =     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
       _UpperCamelCase   :				Dict       =     dataset.map(lowercase_						,  batched=lowercase_						,  num_proc=processing_args.num_proc )

       # And compute the embeddings
       _UpperCamelCase   :				List[str]       =     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=lowercase_ )
       _UpperCamelCase   :				Tuple       =     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
       _UpperCamelCase   :				Any       =     Features(
           {'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} )  # optional, save as float32 instead of float64 to save space
       _UpperCamelCase   :				Dict       =     dataset.map(
           partial(lowercase_						,  ctx_encoder=lowercase_						,  ctx_tokenizer=lowercase_ )						,  batched=lowercase_						,  batch_size=processing_args.batch_size						,  features=lowercase_						,  )

       # And finally save your dataset
       _UpperCamelCase   :				Optional[int]       =     os.path.join(rag_example_args.output_dir						,  'my_knowledge_dataset' )
       dataset.save_to_disk(lowercase_ )
       # 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
       _UpperCamelCase   :				List[str]       =     faiss.IndexHNSWFlat(index_hnsw_args.d						,  index_hnsw_args.m						,  faiss.METRIC_INNER_PRODUCT )
       dataset.add_faiss_index('embeddings'						,  custom_index=lowercase_ )

       # And save the index
       _UpperCamelCase   :				List[str]       =     os.path.join(rag_example_args.output_dir						,  'my_knowledge_dataset_hnsw_index.faiss' )
       dataset.get_index('embeddings' ).save(lowercase_ )
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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

       # Easy way to load the model
       _UpperCamelCase   :				Union[str, Any]       =     RagRetriever.from_pretrained(
           rag_example_args.rag_model_name						,  index_name='custom'						,  indexed_dataset=lowercase_ )
       _UpperCamelCase   :				List[Any]       =     RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name						,  retriever=lowercase_ )
       _UpperCamelCase   :				List[str]       =     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' )
       ######################################

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




@dataclass
class       __lowerCAmelCase     :



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







@dataclass
class       __lowerCAmelCase     :



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int] 			=       field(
     default=a_,		metadata={
         'help': 'The number of processes to use to split the documents into passages. Default is single process.'
     },		)
 _lowerCamelCase  :		int 			=       field(
     default=16,		metadata={
         'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
     },		)







@dataclass
class       __lowerCAmelCase     :



 '''simple docstring'''
 _lowerCamelCase  :		int 			=       field(
     default=768,		metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'},		)
 _lowerCamelCase  :		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)

 UpperCAmelCase							= HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 UpperCAmelCase						,    UpperCAmelCase						,    UpperCAmelCase							= parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  UpperCAmelCase							= 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, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


UpperCAmelCase							= logging.get_logger(__name__)

UpperCAmelCase							= {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

UpperCAmelCase							= {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

UpperCAmelCase							= {
    '''Salesforce/codegen-350M-mono''': 20_48,
}


@lru_cache()
def SCREAMING_SNAKE_CASE (   ):
       _UpperCamelCase   :				Any       =     (
           list(range(ord('!' )						,  ord('~' ) + 1 ) ) + list(range(ord('¡' )						,  ord('¬' ) + 1 ) ) + list(range(ord('®' )						,  ord('ÿ' ) + 1 ) )
       )
       _UpperCamelCase   :				int       =     bs[:]
       _UpperCamelCase   :				Any       =     0
       for b in range(2**8 ):
              if b not in bs:
                     bs.append(lowercase_ )
                     cs.append(2**8 + n )
                     n += 1
       _UpperCamelCase   :				str       =     [chr(lowercase_ ) for n in cs]
       return dict(zip(lowercase_						,  lowercase_ ) )


def SCREAMING_SNAKE_CASE (   lowercase_:		int ):
       _UpperCamelCase   :				List[str]       =     set()
       _UpperCamelCase   :				Optional[int]       =     word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCamelCase   :				Optional[Any]       =     char
       return pairs





class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Union[str, Any] 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		Optional[int] 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		List[str] 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		Optional[int] 			=       ['input_ids', 'attention_mask']

 def __init__(	self				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[Any]="replace"				,	lowerCamelCase				:						int="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						List[Any]=False				,	lowerCamelCase				:						Optional[int]=False				,	**lowerCamelCase				:						Optional[Any]				,	)       ->							int:
        _UpperCamelCase   :				int       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else bos_token
        _UpperCamelCase   :				Any       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else eos_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else unk_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else pad_token
        super().__init__(
            errors=lowerCamelCase				,	unk_token=lowerCamelCase				,	bos_token=lowerCamelCase				,	eos_token=lowerCamelCase				,	pad_token=lowerCamelCase				,	add_prefix_space=lowerCamelCase				,	add_bos_token=lowerCamelCase				,	**lowerCamelCase				,	)
        _UpperCamelCase   :				List[str]       =     add_bos_token

        with open(lowerCamelCase				,	encoding='utf-8'     ) as vocab_handle:
               _UpperCamelCase   :				Union[str, Any]       =     json.load(lowerCamelCase     )
        _UpperCamelCase   :				int       =     {v: k for k, v in self.encoder.items()}
        _UpperCamelCase   :				Optional[int]       =     errors  # how to handle errors in decoding
        _UpperCamelCase   :				Dict       =     bytes_to_unicode()
        _UpperCamelCase   :				Optional[Any]       =     {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCamelCase				,	encoding='utf-8'     ) as merges_handle:
               _UpperCamelCase   :				Dict       =     merges_handle.read().split('\n'     )[1:-1]
        _UpperCamelCase   :				int       =     [tuple(merge.split()     ) for merge in bpe_merges]
        _UpperCamelCase   :				Optional[Any]       =     dict(zip(lowerCamelCase				,	range(len(lowerCamelCase     )     )     )     )
        _UpperCamelCase   :				List[Any]       =     {}
        _UpperCamelCase   :				Any       =     add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _UpperCamelCase   :				Union[str, Any]       =     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				:						Optional[Any]     )       ->							Union[str, Any]:
        return len(self.encoder     )

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

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Dict     )       ->							Union[str, Any]:
        if token in self.cache:
               return self.cache[token]
        _UpperCamelCase   :				Dict       =     tuple(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     get_pairs(lowerCamelCase     )

        if not pairs:
               return token

        while True:
               _UpperCamelCase   :				List[Any]       =     min(lowerCamelCase				,	key=lambda lowerCamelCase     : self.bpe_ranks.get(lowerCamelCase				,	float('inf'     )     )     )
               if bigram not in self.bpe_ranks:
                      break
               _UpperCamelCase  ,					_UpperCamelCase   :				Optional[int]       =     bigram
               _UpperCamelCase   :				Optional[Any]       =     []
               _UpperCamelCase   :				int       =     0
               while i < len(lowerCamelCase     ):
                      try:
                             _UpperCamelCase   :				str       =     word.index(lowerCamelCase				,	lowerCamelCase     )
                      except ValueError:
                             new_word.extend(word[i:]     )
                             break
                      else:
                             new_word.extend(word[i:j]     )
                             _UpperCamelCase   :				List[Any]       =     j

                      if word[i] == first and i < len(lowerCamelCase     ) - 1 and word[i + 1] == second:
                             new_word.append(first + second     )
                             i += 2
                      else:
                             new_word.append(word[i]     )
                             i += 1
               _UpperCamelCase   :				List[str]       =     tuple(lowerCamelCase     )
               _UpperCamelCase   :				Tuple       =     new_word
               if len(lowerCamelCase     ) == 1:
                      break
               else:
                      _UpperCamelCase   :				int       =     get_pairs(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     ' '.join(lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     word
        return word

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Any=None     )       ->							Any:
        if self.add_bos_token:
               _UpperCamelCase   :				List[str]       =     [self.bos_token_id]
        else:
               _UpperCamelCase   :				List[str]       =     []

        _UpperCamelCase   :				Union[str, Any]       =     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				:						Union[str, Any]				,	lowerCamelCase				:						Union[str, Any]     )       ->							str:
        _UpperCamelCase   :				Tuple       =     []
        for token in re.findall(self.pat				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     ''.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(lowerCamelCase     ).split(' '     )     )
        return bpe_tokens

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Any     )       ->							List[str]:
        return self.encoder.get(lowerCamelCase				,	self.encoder.get(self.unk_token     )     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						List[Any]     )       ->							List[str]:
        return self.decoder.get(lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Optional[Any]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     ''.join(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'				,	errors=self.errors     )
        return text

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

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

        _UpperCamelCase   :				Any       =     0
        with open(lowerCamelCase				,	'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 lowerCamelCase     : 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!'     )
                             _UpperCamelCase   :				Dict       =     token_index
                      writer.write(' '.join(lowerCamelCase     ) + '\n'     )
                      index += 1

        return vocab_file, merge_file

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Optional[int]				,	lowerCamelCase				:						int=False				,	**lowerCamelCase				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     kwargs.pop('add_prefix_space'				,	self.add_prefix_space     )
        if is_split_into_words or add_prefix_space:
               _UpperCamelCase   :				Union[str, Any]       =     ' ' + text
        return (text, kwargs)

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = None				,	lowerCamelCase				:						Optional[List[str]] = None				,	**lowerCamelCase				:						Union[str, Any]				,	)       ->							str:
        _UpperCamelCase   :				List[str]       =     super()._decode(
            token_ids=lowerCamelCase				,	skip_special_tokens=lowerCamelCase				,	clean_up_tokenization_spaces=lowerCamelCase				,	**lowerCamelCase				,	)

        if truncate_before_pattern is not None and len(lowerCamelCase     ) > 0:
               _UpperCamelCase   :				Union[str, Any]       =     self.truncate(lowerCamelCase				,	lowerCamelCase     )

        return decoded_text

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						Optional[int]     )       ->							Any:
        def find_re(lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						List[Any]     ):
               _UpperCamelCase   :				Dict       =     pattern.search(lowerCamelCase				,	lowerCamelCase     )
               return m.start() if m else -1

        _UpperCamelCase   :				int       =     [re.compile(lowerCamelCase				,	re.MULTILINE     ) for pattern in truncate_before_pattern]

        _UpperCamelCase   :				Union[str, Any]       =     list(re.finditer('^print'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				List[Any]       =     completion[: prints[1].start()]

        _UpperCamelCase   :				Dict       =     list(re.finditer('^def'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				Tuple       =     completion[: defs[1].start()]

        _UpperCamelCase   :				Optional[Any]       =     0

        _UpperCamelCase   :				int       =     [
            pos for pos in [find_re(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     ) for terminal in terminals] if pos != -1
        ]

        if len(lowerCamelCase     ) > 0:
               return completion[: min(lowerCamelCase     )]
        else:
               return completion



