

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

	# And compute the embeddings
	__A   : Any								= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=_UpperCAmelCase					)
	__A   : List[Any]								= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
	__A   : Dict								= Features(
	    {"""text""": Value("""string"""					), """title""": Value("""string"""					), """embeddings""": Sequence(Value("""float32"""					)					)}					)  # optional, save as float32 instead of float64 to save space
	__A   : List[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
	__A   : int								= 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
	__A   : int								= 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
	__A   : List[str]								= 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
	__A   : str								= RagRetriever.from_pretrained(
	    rag_example_args.rag_model_name ,index_name="""custom""" ,indexed_dataset=_UpperCAmelCase					)
	__A   : Optional[int]								= RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,retriever=_UpperCAmelCase					)
	__A   : 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"""					)
	######################################

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






@dataclass
class     lowerCamelCase_			:
	_lowercase		:      Any    				=							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\''''}	,			)
	_lowercase		:      Any    				=							field(
	    default=lowerCamelCase__	,			metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}	,			)
	_lowercase		:      Optional[int]    				=							field(
	    default='''facebook/rag-sequence-nq'''	,			metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}	,			)
	_lowercase		:      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\''''
	        )
	    }	,			)
	_lowercase		:      Any    				=							field(
	    default=lowerCamelCase__	,			metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,			)






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






@dataclass
class     lowerCamelCase_			:
	_lowercase		:      int    				=							field(
	    default=768	,			metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}	,			)
	_lowercase		:      Any    				=							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_       :				Optional[Any]		   =      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
					UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_       :				Tuple		   =      parser.parse_args_into_dataclasses()
					with TemporaryDirectory() as tmp_dir:
										UpperCAmelCase_       :				Optional[Any]		   =      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_torch_available


UpperCAmelCase_       :				Optional[Any]		   =      {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_       :				List[str]		   =      [
			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ProphetNetDecoder''',
			    '''ProphetNetEncoder''',
			    '''ProphetNetForCausalLM''',
			    '''ProphetNetForConditionalGeneration''',
			    '''ProphetNetModel''',
			    '''ProphetNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
			from .tokenization_prophetnet import ProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_prophetnet import (
						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ProphetNetDecoder,
						    ProphetNetEncoder,
						    ProphetNetForCausalLM,
						    ProphetNetForConditionalGeneration,
						    ProphetNetModel,
						    ProphetNetPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





