


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def     UpperCamelCase_(			_A				:List[str]			,       _A				:Union[str, Any]					)->						Union[str, Any]:
			assert isinstance(lowercase_			,       lowercase_					)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"			,       [False, True]					)
def     UpperCamelCase_(			_A				:Any			,       _A				:Any			,       _A				:Optional[int]					)->						str:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCamelCase__   =    CsvDatasetReader(lowercase_			,       cache_dir=lowercase_			,       keep_in_memory=lowercase_					).read()
			_check_csv_dataset(lowercase_			,       lowercase_					)
@pytest.mark.parametrize(
    "features"			,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,       )
def     UpperCamelCase_(			_A				:Optional[Any]			,       _A				:List[Any]			,       _A				:Dict					)->						List[str]:
			UpperCamelCase__   =    tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    features.copy() if features else default_expected_features
			UpperCamelCase__   =    (
			    Features({feature: Value(lowercase_					) for feature, dtype in features.items()}					) if features is not None else None
			)
			UpperCamelCase__   =    CsvDatasetReader(lowercase_			,       features=lowercase_			,       cache_dir=lowercase_					).read()
			_check_csv_dataset(lowercase_			,       lowercase_					)
@pytest.mark.parametrize("split"			,       [None, NamedSplit("train"					), "train", "test"]					)
def     UpperCamelCase_(			_A				:Dict			,       _A				:List[Any]			,       _A				:int					)->						Optional[Any]:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    CsvDatasetReader(lowercase_			,       cache_dir=lowercase_			,       split=lowercase_					).read()
			_check_csv_dataset(lowercase_			,       lowercase_					)
			assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"			,       [str, list]					)
def     UpperCamelCase_(			_A				:List[str]			,       _A				:Tuple			,       _A				:Tuple					)->						int:

			if issubclass(lowercase_			,       lowercase_					):
						UpperCamelCase__   =    csv_path
			elif issubclass(lowercase_			,       lowercase_					):
						UpperCamelCase__   =    [csv_path]
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    CsvDatasetReader(lowercase_			,       cache_dir=lowercase_					).read()
			_check_csv_dataset(lowercase_			,       lowercase_					)
def     UpperCamelCase_(			_A				:List[str]			,       _A				:Any			,       _A				:Dict=("train",)					)->						Optional[Any]:
			assert isinstance(lowercase_			,       lowercase_					)
			for split in splits:
						UpperCamelCase__   =    dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"			,       [False, True]					)
def     UpperCamelCase_(			_A				:str			,       _A				:Dict			,       _A				:Any					)->						Dict:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=lowercase_			,       keep_in_memory=lowercase_					).read()
			_check_csv_datasetdict(lowercase_			,       lowercase_					)
@pytest.mark.parametrize(
    "features"			,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,       )
def     UpperCamelCase_(			_A				:Any			,       _A				:str			,       _A				:Dict					)->						Union[str, Any]:
			UpperCamelCase__   =    tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    features.copy() if features else default_expected_features
			UpperCamelCase__   =    (
			    Features({feature: Value(lowercase_					) for feature, dtype in features.items()}					) if features is not None else None
			)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       features=lowercase_			,       cache_dir=lowercase_					).read()
			_check_csv_datasetdict(lowercase_			,       lowercase_					)
@pytest.mark.parametrize("split"			,       [None, NamedSplit("train"					), "train", "test"]					)
def     UpperCamelCase_(			_A				:List[Any]			,       _A				:str			,       _A				:Any					)->						Optional[Any]:

			if split:
						UpperCamelCase__   =    {split: csv_path}
			else:
						UpperCamelCase__   =    {"train": csv_path, "test": csv_path}
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			UpperCamelCase__   =    CsvDatasetReader(lowercase_			,       cache_dir=lowercase_					).read()
			_check_csv_datasetdict(lowercase_			,       lowercase_			,       splits=list(path.keys()					)					)
			assert all(dataset[split].split == split for split in path.keys()					)
def     UpperCamelCase_(			_A				:Any					)->						List[Any]:

			with open(lowercase_			,       encoding="utf-8"					) as csvfile:
						yield from csv.reader(lowercase_					)
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:List[str]					)->						Optional[Any]:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    os.path.join(lowercase_			,       "tmp.csv"					)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=lowercase_					).read()
			CsvDatasetWriter(dataset["train"]			,       lowercase_			,       num_proc=1					).write()

			UpperCamelCase__   =    iter_csv_file(lowercase_					)
			UpperCamelCase__   =    iter_csv_file(lowercase_					)

			for rowa, rowa in zip(lowercase_			,       lowercase_					):
						assert rowa == rowa
def     UpperCamelCase_(			_A				:Any			,       _A				:List[Any]					)->						int:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    os.path.join(lowercase_			,       "tmp.csv"					)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=lowercase_					).read()
			CsvDatasetWriter(dataset["train"]			,       lowercase_			,       num_proc=2					).write()

			UpperCamelCase__   =    iter_csv_file(lowercase_					)
			UpperCamelCase__   =    iter_csv_file(lowercase_					)

			for rowa, rowa in zip(lowercase_			,       lowercase_					):
						assert rowa == rowa







def     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    tmp_path / "cache"
			UpperCamelCase__   =    os.path.join(lowercase_			,       "tmp.csv"					)
			UpperCamelCase__   =    CsvDatasetReader({"train": csv_path}			,       cache_dir=lowercase_					).read()
			with pytest.raises(lowercase_					):
						CsvDatasetWriter(dataset["train"]			,       lowercase_			,       num_proc=0					)






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     UpperCamelCase_(			_A				:str			,       _A				:Tuple=1_00			,       _A				:str=" "					)->						List[str]:
			UpperCamelCase__   =    text.split(_A					)
			return [character.join(text[i : i + n]					).strip() for i in range(0			,       len(_A					)			,       _A					)]
def     UpperCamelCase_(			_A				:dict					)->						dict:
			UpperCamelCase__, UpperCamelCase__   =    [], []
			for title, text in zip(documents["title"]			,       documents["text"]					):
						if text is not None:
									for passage in split_text(_A					):
												titles.append(title if title is not None else ""					)
												texts.append(_A					)
			return {"title": titles, "text": texts}
def     UpperCamelCase_(			_A				:dict			,       _A				:DPRContextEncoder			,       _A				:DPRContextEncoderTokenizerFast					)->						dict:
			UpperCamelCase__   =    ctx_tokenizer(
			    documents["title"]			,       documents["text"]			,       truncation=_A			,       padding="longest"			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    ctx_encoder(input_ids.to(device=_A					)			,       return_dict=_A					).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}
def     UpperCamelCase_(			_A				:"RagExampleArguments"			,       _A				:"ProcessingArguments"			,       _A				:"IndexHnswArguments"			,       )->						List[str]:
			######################################
			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__   =    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__   =    dataset.map(_A			,       batched=_A			,       num_proc=processing_args.num_proc					)

			# And compute the embeddings
			UpperCamelCase__   =    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=_A					)
			UpperCamelCase__   =    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
			UpperCamelCase__   =    Features(
			    {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
			UpperCamelCase__   =    dataset.map(
			    partial(_A			,       ctx_encoder=_A			,       ctx_tokenizer=_A					)			,       batched=_A			,       batch_size=processing_args.batch_size			,       features=_A			,       )

			# And finally save your dataset
			UpperCamelCase__   =    os.path.join(rag_example_args.output_dir			,       "my_knowledge_dataset"					)
			dataset.save_to_disk(_A					)
			# 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__   =    faiss.IndexHNSWFlat(index_hnsw_args.d			,       index_hnsw_args.m			,       faiss.METRIC_INNER_PRODUCT					)
			dataset.add_faiss_index("embeddings"			,       custom_index=_A					)

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

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

			# Easy way to load the model
			UpperCamelCase__   =    RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name			,       index_name="custom"			,       indexed_dataset=_A					)
			UpperCamelCase__   =    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_A					)
			UpperCamelCase__   =    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__   =    rag_example_args.question or "What does Moses' rod turn into ?"
			UpperCamelCase__   =    tokenizer.question_encoder(_A			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    model.generate(_A					)
			UpperCamelCase__   =    tokenizer.batch_decode(_A			,       skip_special_tokens=_A					)[0]
			logger.info("Q: " + question					)
			logger.info("A: " + generated_string					)







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






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







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   Optional[int]					=						field(
			    default=UpperCAmelCase   ,		metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }   ,		)
			_UpperCamelCase						:   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"""






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


