




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,
)


_A							    =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
_A							    =	"""cuda""" if torch.cuda.is_available() else """cpu"""
def 						lowerCAmelCase_       (	__a	,	__a=100	,	__a=" " ):





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =text.split(snake_case__ )
							return [character.join(text[i : i + n] ).strip() for i in range(0	,	len(snake_case__ )	,	snake_case__ )]
def 						lowerCAmelCase_       (	__a ):





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Tuple	     =[], []
							for title, text in zip(documents['''title''']	,	documents['''text'''] ):
														if text is not None:
																					for passage in split_text(snake_case__ ):
																												titles.append(title if title is not None else '''''' )
																												texts.append(snake_case__ )
							return {"title": titles, "text": texts}
def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):





							"""simple docstring"""





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





							"""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
							SCREAMING_SNAKE_CASE			: List[str]	     =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			: int	     =dataset.map(snake_case__	,	batched=snake_case__	,	num_proc=processing_args.num_proc )

							# And compute the embeddings
							SCREAMING_SNAKE_CASE			: int	     =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=snake_case__ )
							SCREAMING_SNAKE_CASE			: List[str]	     =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
							SCREAMING_SNAKE_CASE			: Any	     =Features(
							    {'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} )  # optional, save as float32 instead of float64 to save space
							SCREAMING_SNAKE_CASE			: Any	     =dataset.map(
							    partial(snake_case__	,	ctx_encoder=snake_case__	,	ctx_tokenizer=snake_case__ )	,	batched=snake_case__	,	batch_size=processing_args.batch_size	,	features=snake_case__	,	)

							# And finally save your dataset
							SCREAMING_SNAKE_CASE			: Tuple	     =os.path.join(rag_example_args.output_dir	,	'''my_knowledge_dataset''' )
							dataset.save_to_disk(snake_case__ )
							# 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			: int	     =faiss.IndexHNSWFlat(index_hnsw_args.d	,	index_hnsw_args.m	,	faiss.METRIC_INNER_PRODUCT )
							dataset.add_faiss_index('''embeddings'''	,	custom_index=snake_case__ )

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

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

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

							SCREAMING_SNAKE_CASE			: int	     =rag_example_args.question or 'What does Moses\' rod turn into ?'
							SCREAMING_SNAKE_CASE			: Tuple	     =tokenizer.question_encoder(snake_case__	,	return_tensors='''pt''' )['input_ids']
							SCREAMING_SNAKE_CASE			: str	     =model.generate(snake_case__ )
							SCREAMING_SNAKE_CASE			: Tuple	     =tokenizer.batch_decode(snake_case__	,	skip_special_tokens=snake_case__ )[0]
							logger.info('''Q: ''' + question )
							logger.info('''A: ''' + generated_string )


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







@dataclass
class 							_lowerCAmelCase		:
				lowerCamelCase__									=    field(
				    default=a__  ,   metadata={
				        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
				    }  ,   )
				lowerCamelCase__									=    field(
				    default=16  ,   metadata={
				        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
				    }  ,   )







@dataclass
class 							_lowerCAmelCase		:
				lowerCamelCase__									=    field(
				    default=7_68  ,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}  ,   )
				lowerCamelCase__									=    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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RegNetForImageClassification""",
							    """RegNetModel""",
							    """RegNetPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRegNetForImageClassification""",
							    """TFRegNetModel""",
							    """TFRegNetPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """FlaxRegNetForImageClassification""",
							    """FlaxRegNetModel""",
							    """FlaxRegNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_regnet import (
														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RegNetForImageClassification,
														    RegNetModel,
														    RegNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_regnet import (
														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRegNetForImageClassification,
														    TFRegNetModel,
														    TFRegNetPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_regnet import (
														    FlaxRegNetForImageClassification,
														    FlaxRegNetModel,
														    FlaxRegNetPreTrainedModel,
														)


else:
							import sys

							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)






