


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



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=100							,				lowerCamelCase_=" "				)					->		List[str]:
    _lowercase :							Optional[int]   	=	text.split(lowerCamelCase_				)
    return [character.join(text[i : i + n]				).strip() for i in range(0							,				len(lowerCamelCase_				)							,				lowerCamelCase_				)]



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict:
    _lowercase :							Any   	=	[], []
    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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		dict:
    _lowercase :							str   	=	ctx_tokenizer(
        documents['title']							,				documents['text']							,				truncation=lowerCamelCase_							,				padding='longest'							,				return_tensors='pt'				)['input_ids']
    _lowercase :							Dict   	=	ctx_encoder(input_ids.to(device=lowerCamelCase_				)							,				return_dict=lowerCamelCase_				).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				)					->		int:
    ######################################
    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 :							Union[str, 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
    _lowercase :							str   	=	dataset.map(lowerCamelCase_							,				batched=lowerCamelCase_							,				num_proc=processing_args.num_proc				)

    # And compute the embeddings
    _lowercase :							Optional[int]   	=	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				).to(device=lowerCamelCase_				)
    _lowercase :							str   	=	DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				)
    _lowercase :							str   	=	Features(
        {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space
    _lowercase :							List[Any]   	=	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
    _lowercase :							Optional[int]   	=	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
    _lowercase :							List[Any]   	=	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
    _lowercase :							int   	=	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 _lowerCamelCase:
       lowercase_				:			List[str]									=    field(
           default=str(Path(__lowerCamelCase     ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""     ),				metadata={"""help""": """Path to a tab-separated csv file with columns \'title\' and \'text\'"""},				)
       lowercase_				:			Union[str, Any]									=    field(
           default=__lowerCamelCase,				metadata={"""help""": """Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."""},				)
       lowercase_				:			List[str]									=    field(
           default="""facebook/rag-sequence-nq""",				metadata={"""help""": """The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\'"""},				)
       lowercase_				:			Tuple									=    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\'"""
               )
           },				)
       lowercase_				:			Dict									=    field(
           default=str(Path(__lowerCamelCase     ).parent / """test_run""" / """dummy-kb"""     ),				metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""},				)






@dataclass
class _lowerCamelCase:
       lowercase_				:			Optional[Any]									=    field(
           default=__lowerCamelCase,				metadata={
               """help""": """The number of processes to use to split the documents into passages. Default is single process."""
           },				)
       lowercase_				:			Dict									=    field(
           default=16,				metadata={
               """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
           },				)






@dataclass
class _lowerCamelCase:
       lowercase_				:			List[Any]									=    field(
           default=7_68,				metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""},				)
       lowercase_				:			int									=    field(
           default=1_28,				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 :   Optional[Any]							      =				HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE :   Dict							      =				parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  SCREAMING_SNAKE_CASE :   List[Any]							      =				rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE :   Dict							      =				logging.get_logger(__name__)



def        UpperCamelCase_(   lowerCamelCase_=None							,				lowerCamelCase_=None				)					->		Tuple:
    return field(default_factory=lambda: default							,				metadata=lowerCamelCase_				)






@dataclass
class _lowerCamelCase:
       lowercase_				:			List[str]									=    list_field(
           default=[],				metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           },				)

       lowercase_				:			List[int]									=    list_field(
           default=[8],				metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}     )

       lowercase_				:			List[int]									=    list_field(
           default=[8, 32, 1_28, 5_12],				metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""},				)

       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Use FP16 to accelerate inference."""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Benchmark training of model"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Verbose memory tracing"""}     )
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           },				)
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Trace memory line by line"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Save result to a CSV file"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Save all print statements in a log file"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Whether to print environment information"""}     )
       lowercase_				:			bool									=    field(
           default=_a,				metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           },				)
       lowercase_				:			str									=    field(
           default=F'''inference_time_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving time results to csv."""},				)
       lowercase_				:			str									=    field(
           default=F'''inference_memory_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving memory results to csv."""},				)
       lowercase_				:			str									=    field(
           default=F'''train_time_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving time results to csv for training."""},				)
       lowercase_				:			str									=    field(
           default=F'''train_memory_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving memory results to csv for training."""},				)
       lowercase_				:			str									=    field(
           default=F'''env_info_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving environment information."""},				)
       lowercase_				:			str									=    field(
           default=F'''log_{round(time()     )}.csv''',				metadata={"""help""": """Log filename used if print statements are saved in log."""},				)
       lowercase_				:			int									=    field(default=3,				metadata={"""help""": """Times an experiment will be run."""}     )
       lowercase_				:			bool									=    field(
           default=_a,				metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           },				)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           warnings.warn(
               F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
               ' are deprecated in general and it is advised to use external Benchmarking libraries '
               ' to benchmark Transformer models.',   lowerCamelCase,   )
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return json.dumps(dataclasses.asdict(self),   indent=2)
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           if len(self.models) <= 0:
               raise ValueError(
                   'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
                   ' bert-base-cased` or `args.models = [\'bert-base-cased\'].')
           return self.models




       @property
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           if not self.multi_process:
               return False
           elif self.is_tpu:
               logger.info('Multiprocessing is currently not possible on TPU.')
               return False
           else:
               return True


