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


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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=1_0_0     ,  lowerCAmelCase__=" "     ):
							lowerCAmelCase__					     = text.split(lowerCAmelCase__     )
							return [character.join(text[i : i + n]     ).strip() for i in range(0     ,  len(lowerCAmelCase__     )     ,  lowerCAmelCase__     )]






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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
							lowerCAmelCase__					     = ctx_tokenizer(
							    documents['title']     ,  documents['text']     ,  truncation=lowerCAmelCase__     ,  padding='longest'     ,  return_tensors='pt'     )['input_ids']
							lowerCAmelCase__					     = ctx_encoder(input_ids.to(device=lowerCAmelCase__     )     ,  return_dict=lowerCAmelCase__     ).pooler_output
							return {"embeddings": embeddings.detach().cpu().numpy()}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  ):
							######################################
							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(lowerCAmelCase__     ,  batched=lowerCAmelCase__     ,  num_proc=processing_args.num_proc     )

							# And compute the embeddings
							lowerCAmelCase__					     = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=lowerCAmelCase__     )
							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(lowerCAmelCase__     ,  ctx_encoder=lowerCAmelCase__     ,  ctx_tokenizer=lowerCAmelCase__     )     ,  batched=lowerCAmelCase__     ,  batch_size=processing_args.batch_size     ,  features=lowerCAmelCase__     ,  )

							# And finally save your dataset
							lowerCAmelCase__					     = os.path.join(rag_example_args.output_dir     ,  'my_knowledge_dataset'     )
							dataset.save_to_disk(lowerCAmelCase__     )
							# 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=lowerCAmelCase__     )

							# And save the index
							lowerCAmelCase__					     = os.path.join(rag_example_args.output_dir     ,  'my_knowledge_dataset_hnsw_index.faiss'     )
							dataset.get_index('embeddings'     ).save(lowerCAmelCase__     )
							# dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class     a_			:






	'''simple docstring'''






	UpperCAmelCase_     			= field(
	    default=str(Path(SCREAMING_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\''}	,					)
	UpperCAmelCase_     			= field(
	    default=SCREAMING_SNAKE_CASE	,					metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}	,					)
	UpperCAmelCase_     			= field(
	    default='facebook/rag-sequence-nq'	,					metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}	,					)
	UpperCAmelCase_     			= 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_     			= field(
	    default=str(Path(SCREAMING_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     a_			:






	'''simple docstring'''






	UpperCAmelCase_     			= field(
	    default=SCREAMING_SNAKE_CASE	,					metadata={
	        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
	    }	,					)
	UpperCAmelCase_     			= field(
	    default=16	,					metadata={
	        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
	    }	,					)






@dataclass
class     a_			:






	'''simple docstring'''






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

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

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowerCAmelCase__     						=			logging.get_logger(__name__)

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

lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






lowerCAmelCase__     						=			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

lowerCAmelCase__     						=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase__     						=			r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '
@add_start_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			:






 '''simple docstring'''






 def __call__(      self :  Optional[int]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  Optional[bool] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        if titles is None and texts is None:
               return super().__call__(
                   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        elif titles is None or texts is None:
               lowerCAmelCase__					     = titles if texts is None else texts
               return super().__call__(
                   lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = titles if not isinstance(lowercase__      ,   lowercase__) else [titles]
        lowerCAmelCase__					     = texts if not isinstance(lowercase__      ,   lowercase__) else [texts]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = questions if not isinstance(lowercase__      ,   lowercase__) else [questions] * n_passages
        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   F"""There should be as many titles than texts but got {len(lowercase__)} titles and {len(lowercase__)} texts.""")
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   add_special_tokens=lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = {
            'input_ids': [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(lowercase__      ,   lowercase__)
            ]
        }
        if return_attention_mask is not False:
               lowerCAmelCase__					     = []
               for input_ids in encoded_inputs["input_ids"]:
                      attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
               lowerCAmelCase__					     = attention_mask
        return self.pad(lowercase__      ,   padding=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  BatchEncoding      ,   lowercase__ :  DPRReaderOutput      ,   lowercase__ :  int = 16      ,   lowercase__ :  int = 64      ,   lowercase__ :  int = 4      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = reader_input['input_ids']
        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = reader_output[:3]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = sorted(range(lowercase__)      ,   reverse=lowercase__      ,   key=relevance_logits.__getitem__)
        lowerCAmelCase__					     = []
        for doc_id in sorted_docs:
               lowerCAmelCase__					     = list(input_ids[doc_id])
               # assuming question & title information is at the beginning of the sequence
               lowerCAmelCase__					     = sequence_ids.index(self.sep_token_id      ,   2) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                      lowerCAmelCase__					     = sequence_ids.index(self.pad_token_id)
               else:
                      lowerCAmelCase__					     = len(lowercase__)

               lowerCAmelCase__					     = self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len]      ,   end_logits=end_logits[doc_id][passage_offset:sequence_len]      ,   max_answer_length=lowercase__      ,   top_spans=lowercase__      ,   )
               for start_index, end_index in best_spans:
                      start_index += passage_offset
                      end_index += passage_offset
                      nbest_spans_predictions.append(
                          DPRSpanPrediction(
                              span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]      ,   relevance_score=relevance_logits[doc_id]      ,   doc_id=lowercase__      ,   start_index=lowercase__      ,   end_index=lowercase__      ,   text=self.decode(sequence_ids[start_index : end_index + 1])      ,   ))
               if len(lowercase__) >= num_spans:
                      break
        return nbest_spans_predictions[:num_spans]




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = []
        for start_index, start_score in enumerate(lowercase__):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                      scores.append(((start_index, start_index + answer_length), start_score + end_score))
        lowerCAmelCase__					     = sorted(lowercase__      ,   key=lambda lowercase__: x[1]      ,   reverse=lowercase__)
        lowerCAmelCase__					     = []
        for (start_index, end_index), score in scores:
               if start_index > end_index:
                      raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
               lowerCAmelCase__					     = end_index - start_index + 1
               if length > max_answer_length:
                      raise ValueError(F"""Span is too long: {length} > {max_answer_length}""")
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals):
                      continue
               chosen_span_intervals.append((start_index, end_index))

               if len(lowercase__) == top_spans:
                      break
        return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= READER_PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_     			= ['input_ids', 'attention_mask']
