




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


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[Any]=100 ,     UpperCamelCase__				:      List[str]=" "      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    text.split(UpperCamelCase__      )
				return [character.join(text[i : i + n]      ).strip() for i in range(0 ,     len(UpperCamelCase__      ) ,     UpperCamelCase__      )]







def 			lowerCamelCase_		(       UpperCamelCase__				:      dict      )     ->       dict:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      dict ,     UpperCamelCase__				:      DPRContextEncoder ,     UpperCamelCase__				:      DPRContextEncoderTokenizerFast      )     ->       dict:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      "RagExampleArguments" ,     UpperCamelCase__				:      "ProcessingArguments" ,     UpperCamelCase__				:      "IndexHnswArguments" ,     )     ->       Union[str, Any]:





				"""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
				__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(UpperCamelCase__ ,     batched=UpperCamelCase__ ,     num_proc=processing_args.num_proc      )

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

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

				# And save the index
				__lowerCamelCase		       =    os.path.join(rag_example_args.output_dir ,     'my_knowledge_dataset_hnsw_index.faiss'      )
				dataset.get_index('embeddings'      ).save(UpperCamelCase__      )
				# dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







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







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={
			        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
			    }			,				)
			snake_case_   		=       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"""







			snake_case_   		=       field(
			    default=7_68			,				metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}			,				)
			snake_case_   		=       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)

					__A        =    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
					__A ,      __A ,      __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)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : List[str]  					=       {
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[int]        =						'''convbert'''
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_0_5_2_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       Dict="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.02     ,							SCREAMING_SNAKE_CASE__				:       Any=1E-12     ,							SCREAMING_SNAKE_CASE__				:       int=1     ,							SCREAMING_SNAKE_CASE__				:       int=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=9     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     ,							)		->			Any:
             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

             a_					:		Tuple							=  vocab_size
             a_					:		List[str]							=  hidden_size
             a_					:		List[str]							=  num_hidden_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		Optional[int]							=  intermediate_size
             a_					:		int							=  hidden_act
             a_					:		Dict							=  hidden_dropout_prob
             a_					:		int							=  attention_probs_dropout_prob
             a_					:		str							=  max_position_embeddings
             a_					:		List[str]							=  type_vocab_size
             a_					:		List[str]							=  initializer_range
             a_					:		Tuple							=  layer_norm_eps
             a_					:		Optional[int]							=  embedding_size
             a_					:		List[Any]							=  head_ratio
             a_					:		List[Any]							=  conv_kernel_size
             a_					:		Tuple							=  num_groups
             a_					:		Tuple							=  classifier_dropout
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Mapping[str, Mapping[int, str]]:
             if self.task == "multiple-choice":
                    a_					:		Tuple							=  {0: 'batch', 1: 'choice', 2: 'sequence'}
             else:
                    a_					:		List[str]							=  {0: 'batch', 1: 'sequence'}
             return OrderedDict(
                 [
                     ('input_ids', dynamic_axis),
                     ('attention_mask', dynamic_axis),
                     ('token_type_ids', dynamic_axis),
                 ]     )


