
"""simple docstring"""







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,
)


lowerCamelCase__     =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__     =						"""cuda""" if torch.cuda.is_available() else """cpu"""
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase=100			,    _UpperCamelCase=" "						):
	__lowerCAmelCase     :		str		     = text.split(_UpperCamelCase						)
	return [character.join(text[i : i + n]						).strip() for i in range(0			,    len(_UpperCamelCase						)			,    _UpperCamelCase						)]
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     ,  __lowerCAmelCase     :		Union[str, Any]		     = [], []
	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			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		str		     = ctx_tokenizer(
	    documents['title']			,    documents['text']			,    truncation=_UpperCamelCase			,    padding='longest'			,    return_tensors='pt'						)['input_ids']
	__lowerCAmelCase     :		Union[str, Any]		     = ctx_encoder(input_ids.to(device=_UpperCamelCase						)			,    return_dict=_UpperCamelCase						).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    ):
	######################################
	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     :		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
	__lowerCAmelCase     :		Any		     = dataset.map(_UpperCamelCase			,    batched=_UpperCamelCase			,    num_proc=processing_args.num_proc						)

	# And compute the embeddings
	__lowerCAmelCase     :		Dict		     = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=_UpperCamelCase						)
	__lowerCAmelCase     :		List[str]		     = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
	__lowerCAmelCase     :		List[str]		     = Features(
	    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
	__lowerCAmelCase     :		Any		     = 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     :		Union[str, Any]		     = 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     :		Dict		     = 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     :		Union[str, Any]		     = 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

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

	# Easy way to load the model
	__lowerCAmelCase     :		Union[str, Any]		     = RagRetriever.from_pretrained(
	    rag_example_args.rag_model_name			,    index_name='custom'			,    indexed_dataset=_UpperCamelCase						)
	__lowerCAmelCase     :		int		     = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,    retriever=_UpperCamelCase						)
	__lowerCAmelCase     :		int		     = 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'						)
	######################################

	__lowerCAmelCase     :		List[str]		     = rag_example_args.question or 'What does Moses\' rod turn into ?'
	__lowerCAmelCase     :		List[str]		     = tokenizer.question_encoder(_UpperCamelCase			,    return_tensors='pt'						)['input_ids']
	__lowerCAmelCase     :		Dict		     = model.generate(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[Any]		     = tokenizer.batch_decode(_UpperCamelCase			,    skip_special_tokens=_UpperCamelCase						)[0]
	logger.info('Q: ' + question						)
	logger.info('A: ' + generated_string						)




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




@dataclass
class 					A__			:
		A_		:			Optional[int]							  =      field(
		    default=_lowerCamelCase			,	metadata={
		        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
		    }			,	)
		A_		:			int							  =      field(
		    default=1_6			,	metadata={
		        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
		    }			,	)




@dataclass
class 					A__			:
		A_		:			int							  =      field(
		    default=7_6_8			,	metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}			,	)
		A_		:			int							  =      field(
		    default=1_2_8			,	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)

"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						):
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 20, 'width': 20}
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}

			__lowerCAmelCase     :		Any		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Any		     = num_channels
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Tuple		     = min_resolution
			__lowerCAmelCase     :		Union[str, Any]		     = max_resolution
			__lowerCAmelCase     :		Optional[int]		     = do_resize
			__lowerCAmelCase     :		Any		     = size
			__lowerCAmelCase     :		Union[str, Any]		     = do_center_crop
			__lowerCAmelCase     :		List[Any]		     = crop_size
			__lowerCAmelCase     :		Optional[int]		     = do_normalize
			__lowerCAmelCase     :		int		     = image_mean
			__lowerCAmelCase     :		Tuple		     = image_std







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			List[str]							  =      DeiTImageProcessor if is_vision_available() else None
		A_		:			int							  =      True

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = DeiTImageProcessingTester(self					)

		@property
		def 				__lowerCamelCase				( self					):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_normalize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_mean'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_std'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'height': 20, 'width': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		str		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'height': 42, 'width': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Dict		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						Image.Image					)

			# Test not batched input
			__lowerCAmelCase     :		Dict		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		int		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						numpify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						np.ndarray					)

			# Test not batched input
			__lowerCAmelCase     :		str		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Dict		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)







		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Union[str, Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						torchify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						torch.Tensor					)

			# Test not batched input
			__lowerCAmelCase     :		Union[str, Any]		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Optional[int]		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)