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		:     int         =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE		:     Tuple         =	"cuda" if torch.cuda.is_available() else "cpu"






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				Optional[int]=100		, _SCREAMING_SNAKE_CASE     :				Optional[int]=" "			):
						UpperCamelCase_		:							List[str]  =		text.split(_SCREAMING_SNAKE_CASE			)
						return [character.join(text[i : i + n]			).strip() for i in range(0		, len(_SCREAMING_SNAKE_CASE			)		, _SCREAMING_SNAKE_CASE			)]






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				dict			):
						UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		[], []
						for title, text in zip(documents["""title"""]		, documents["""text"""]			):
												if text is not None:
																		for passage in split_text(_SCREAMING_SNAKE_CASE			):
																								titles.append(title if title is not None else """"""			)
																								texts.append(_SCREAMING_SNAKE_CASE			)
						return {"title": titles, "text": texts}






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				dict		, _SCREAMING_SNAKE_CASE     :				DPRContextEncoder		, _SCREAMING_SNAKE_CASE     :				DPRContextEncoderTokenizerFast			):
						UpperCamelCase_		:							Union[str, Any]  =		ctx_tokenizer(
						    documents["""title"""]		, documents["""text"""]		, truncation=_SCREAMING_SNAKE_CASE		, padding="""longest"""		, return_tensors="""pt"""			)["""input_ids"""]
						UpperCamelCase_		:							Any  =		ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE			)		, return_dict=_SCREAMING_SNAKE_CASE			).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				"RagExampleArguments"		, _SCREAMING_SNAKE_CASE     :				"ProcessingArguments"		, _SCREAMING_SNAKE_CASE     :				"IndexHnswArguments"		, ):
						######################################
						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_		:							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
						UpperCamelCase_		:							int  =		dataset.map(_SCREAMING_SNAKE_CASE		, batched=_SCREAMING_SNAKE_CASE		, num_proc=processing_args.num_proc			)

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

						# And finally save your dataset
						UpperCamelCase_		:							str  =		os.path.join(rag_example_args.output_dir		, """my_knowledge_dataset"""			)
						dataset.save_to_disk(_SCREAMING_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
						UpperCamelCase_		:							List[Any]  =		faiss.IndexHNSWFlat(index_hnsw_args.d		, index_hnsw_args.m		, faiss.METRIC_INNER_PRODUCT			)
						dataset.add_faiss_index("""embeddings"""		, custom_index=_SCREAMING_SNAKE_CASE			)

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

@dataclass
class        UpperCamelCase						:
						a__		:str      =			field(
						    default=str(Path(__a				).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''				)	,	metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}	,	)
						a__		:Optional[str]      =			field(
						    default=__a	,	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=str(Path(__a				).parent / '''test_run''' / '''dummy-kb'''				)	,	metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,	)


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


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

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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE		:     Dict         =	[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

SCREAMING_SNAKE_CASE		:     int         =	{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE		:     Any         =	{F'''funnel-transformer/{name}''': 512 for name in _model_names}
SCREAMING_SNAKE_CASE		:     Tuple         =	{F'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}

class        UpperCamelCase						(				__a				):
						a__		:Dict      =			VOCAB_FILES_NAMES
						a__		:Dict      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Dict      =			PRETRAINED_INIT_CONFIGURATION
						a__		:str      =			FunnelTokenizer
						a__		:List[str]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:int      =			2





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase="<sep>"			,     __UpperCamelCase="<pad>"			,     __UpperCamelCase="<cls>"			,     __UpperCamelCase="<mask>"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=None			,     __UpperCamelCase="##"			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(
												    __UpperCamelCase			,     tokenizer_file=__UpperCamelCase			,     do_lower_case=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     sep_token=__UpperCamelCase			,     pad_token=__UpperCamelCase			,     cls_token=__UpperCamelCase			,     mask_token=__UpperCamelCase			,     bos_token=__UpperCamelCase			,     eos_token=__UpperCamelCase			,     clean_text=__UpperCamelCase			,     tokenize_chinese_chars=__UpperCamelCase			,     strip_accents=__UpperCamelCase			,     wordpieces_prefix=__UpperCamelCase			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
												if (
												    normalizer_state.get("""lowercase"""			,     __UpperCamelCase						) != do_lower_case
												    or normalizer_state.get("""strip_accents"""			,     __UpperCamelCase						) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""			,     __UpperCamelCase						) != tokenize_chinese_chars
												):
																		UpperCamelCase_		:							List[Any]  =		getattr(__UpperCamelCase			,     normalizer_state.pop("""type"""						)						)
																		UpperCamelCase_		:							Union[str, Any]  =		do_lower_case
																		UpperCamelCase_		:							Tuple  =		strip_accents
																		UpperCamelCase_		:							Dict  =		tokenize_chinese_chars
																		UpperCamelCase_		:							Union[str, Any]  =		normalizer_class(**__UpperCamelCase						)

												UpperCamelCase_		:							Dict  =		do_lower_case





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=None						)				->				Dict:
												UpperCamelCase_		:							Any  =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																		output += token_ids_a + [self.sep_token_id]

												return output





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				List[int]:
												UpperCamelCase_		:							Optional[Any]  =		[self.sep_token_id]
												UpperCamelCase_		:							Any  =		[self.cls_token_id]
												if token_ids_a is None:
																		return len(cls						) * [self.cls_token_type_id] + len(token_ids_a + sep						) * [0]
												return len(cls						) * [self.cls_token_type_id] + len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												UpperCamelCase_		:							int  =		self._tokenizer.model.save(__UpperCamelCase			,     name=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)






