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


SCREAMING_SNAKE_CASE :	Optional[int]							     =		logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE :	Dict							     =		"cuda" if torch.cuda.is_available() else "cpu"



def   __A		(			_A							,				_A=100							,				_A=" "					):



	"""simple docstring"""


	__a       =  text.split(__A					)
	return [character.join(text[i : i + n]					).strip() for i in range(0							,				len(__A					)							,				__A					)]



def   __A		(			_A					):



	"""simple docstring"""


	__a       =  [], []
	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   __A		(			_A							,				_A							,				_A					):



	"""simple docstring"""


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



def   __A		(			_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
	__a       =  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       =  dataset.map(__A							,				batched=__A							,				num_proc=processing_args.num_proc					)

	# And compute the embeddings
	__a       =  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=__A					)
	__a       =  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
	__a       =  Features(
	    {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
	__a       =  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
	__a       =  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
	__a       =  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
	__a       =  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
@dataclass
class    A_ :
		_SCREAMING_SNAKE_CASE    				=			field(
		    default=str(Path(__lowerCAmelCase ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv""" )			,       metadata={"""help""": """Path to a tab-separated csv file with columns \'title\' and \'text\'"""}			,       )
		_SCREAMING_SNAKE_CASE    				=			field(
		    default=__lowerCAmelCase			,       metadata={"""help""": """Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."""}			,       )
		_SCREAMING_SNAKE_CASE    				=			field(
		    default="""facebook/rag-sequence-nq"""			,       metadata={"""help""": """The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\'"""}			,       )
		_SCREAMING_SNAKE_CASE    				=			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\'"""
		        )
		    }			,       )
		_SCREAMING_SNAKE_CASE    				=			field(
		    default=str(Path(__lowerCAmelCase ).parent / """test_run""" / """dummy-kb""" )			,       metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}			,       )
@dataclass
class    A_ :
		_SCREAMING_SNAKE_CASE    				=			field(
		    default=__lowerCAmelCase			,       metadata={
		        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
		    }			,       )
		_SCREAMING_SNAKE_CASE    				=			field(
		    default=16			,       metadata={
		        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
		    }			,       )
@dataclass
class    A_ :
		_SCREAMING_SNAKE_CASE    				=			field(
		    default=7_68			,       metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}			,       )
		_SCREAMING_SNAKE_CASE    				=			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)

		SCREAMING_SNAKE_CASE :	List[Any]							     =		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
		SCREAMING_SNAKE_CASE :	Dict							     =		parser.parse_args_into_dataclasses()
		with TemporaryDirectory() as tmp_dir:
				SCREAMING_SNAKE_CASE :	List[str]							     =		rag_example_args.output_dir or tmp_dir
				main(rag_example_args, processing_args, index_hnsw_args)



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


