


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


snake_case_						:			str													=							logging.getLogger(__name__)
torch.set_grad_enabled(False)
snake_case_						:			Optional[Any]													=							"cuda" if torch.cuda.is_available() else "cpu"







def 						lowerCamelCase(   a__	,a__=100	,a__=" "):
				_SCREAMING_SNAKE_CASE			  =text.split(_lowerCamelCase)
				return [character.join(text[i : i + n]).strip() for i in range(0	,len(_lowerCamelCase)	,_lowerCamelCase)]







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







def 						lowerCamelCase(   a__	,a__	,a__	,):
				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
				_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =dataset.map(_lowerCamelCase	,batched=_lowerCamelCase	,num_proc=processing_args.num_proc)

				# And compute the embeddings
				_SCREAMING_SNAKE_CASE			  =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=_lowerCamelCase)
				_SCREAMING_SNAKE_CASE			  =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
				_SCREAMING_SNAKE_CASE			  =Features(
				    {'''text''': Value('''string'''), '''title''': Value('''string'''), '''embeddings''': Sequence(Value('''float32'''))})  # optional, save as float32 instead of float64 to save space
				_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =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__							:
						UpperCAmelCase             =						field(
						    default=str(Path(lowercase__  ).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=lowercase__   ,     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(lowercase__  ).parent / "test_run" / "dummy-kb"  )   ,     metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}   ,     )






@dataclass
class 						A__							:
						UpperCAmelCase             =						field(
						    default=lowercase__   ,     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__							:
						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)

							snake_case_						:			str													=							HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							snake_case_						:			List[Any]													=							parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														snake_case_						:			str													=							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


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )