
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


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






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Dict=100       ,					__lowercase   :	Any=" " ) ->  List[str]:
			_snake_case		=			text.split(__lowercase )
			return [character.join(text[i : i + n] ).strip() for i in range(0       ,					len(__lowercase )       ,					__lowercase )]






def        a_		(       __lowercase   :	dict ) ->  dict:
			_snake_case	,					_snake_case		=			[], []
			for title, text in zip(documents['title']       ,					documents['text'] ):
						if text is not None:
									for passage in split_text(__lowercase ):
												titles.append(title if title is not None else '' )
												texts.append(__lowercase )
			return {"title": titles, "text": texts}






def        a_		(       __lowercase   :	dict       ,					__lowercase   :	DPRContextEncoder       ,					__lowercase   :	DPRContextEncoderTokenizerFast ) ->  dict:
			_snake_case		=			ctx_tokenizer(
			    documents['title']       ,					documents['text']       ,					truncation=__lowercase       ,					padding='longest'       ,					return_tensors='pt' )['input_ids']
			_snake_case		=			ctx_encoder(input_ids.to(device=__lowercase )       ,					return_dict=__lowercase ).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}






def        a_		(       __lowercase   :	"RagExampleArguments"       ,					__lowercase   :	"ProcessingArguments"       ,					__lowercase   :	"IndexHnswArguments"       ,					) ->  List[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
			_snake_case		=			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
			_snake_case		=			dataset.map(__lowercase       ,					batched=__lowercase       ,					num_proc=processing_args.num_proc )

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

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

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




@dataclass
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



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




@dataclass
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



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




@dataclass
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	_UpperCAmelCase       :		int       =					field(
	    default=7_6_8							,metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}							,)
	_UpperCAmelCase       :		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       :				List[Any]       =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
			_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase       :				Optional[int]       =			parser.parse_args_into_dataclasses()
			with TemporaryDirectory() as tmp_dir:
						_lowerCamelCase       :				Union[str, Any]       =			rag_example_args.output_dir or tmp_dir
						main(rag_example_args, processing_args, index_hnsw_args)

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_pegasus import PegasusTokenizer
else:
			_lowerCamelCase       :				int       =			None


_lowerCamelCase       :				List[str]       =			logging.get_logger(__name__)


_lowerCamelCase       :				Tuple       =			'''▁'''

_lowerCamelCase       :				Optional[Any]       =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase       :				Any       =			{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

_lowerCamelCase       :				Optional[int]       =			{
    '''google/pegasus-xsum''': 512,
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		int       =					VOCAB_FILES_NAMES
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase       :		Any       =					PegasusTokenizer
	_UpperCAmelCase       :		Dict       =					["input_ids", "attention_mask"]



	def __init__(  self	:    Tuple			,  lowercase	:    str=None			,  lowercase	:    Any=None			,  lowercase	:    List[Any]="<pad>"			,  lowercase	:    List[Any]="</s>"			,  lowercase	:    Tuple="<unk>"			,  lowercase	:    Any="<mask_2>"			,  lowercase	:    List[str]="<mask_1>"			,  lowercase	:    List[Any]=None			,  lowercase	:    Dict=103			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				_snake_case		=			offset

				if additional_special_tokens is not None:
							if not isinstance(lowercase			,  lowercase	):
										raise TypeError(
										    f'''additional_special_tokens should be of type {type(lowercase	)}, but is'''
										    f''' {type(lowercase	)}'''	)

							_snake_case		=			(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    f'''<unk_{i}>''' for i in range(len(lowercase	)			,  self.offset - 1	)
							]

							if len(set(lowercase	)	) != len(lowercase	):
										raise ValueError(
										    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
										    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''	)
							_snake_case		=			additional_special_tokens_extended
				else:
							_snake_case		=			[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [f'''<unk_{i}>''' for i in range(2			,  self.offset	)]

				super().__init__(
				    lowercase			,  tokenizer_file=lowercase			,  pad_token=lowercase			,  eos_token=lowercase			,  unk_token=lowercase			,  mask_token=lowercase			,  mask_token_sent=lowercase			,  offset=lowercase			,  additional_special_tokens=lowercase			,  **lowercase			,  )
				_snake_case		=			vocab_file
				_snake_case		=			False if not self.vocab_file else True



	def  A							(  self	:    List[str]			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			set(self.all_special_ids	)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

				if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
							raise ValueError(
							    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
							    f''' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'''	)

				return [1 if x in all_special_ids else 0 for x in seq]



	def  A							(  self	:    List[Any]			,  lowercase	:    List			,  lowercase	:    Optional[List] = None			,  lowercase	:    bool = False	):


				'''simple docstring'''


				if already_has_special_tokens:
							return self._special_token_mask(lowercase	)
				elif token_ids_a is None:
							return self._special_token_mask(lowercase	) + [1]
				else:
							return self._special_token_mask(token_ids_a + token_ids_a	) + [1]



	def  A							(  self	:    Any			,  lowercase	:    Tuple			,  lowercase	:    Any=None	):


				'''simple docstring'''


				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]





	def  A							(  self	:    int			,  lowercase	:    str			,  lowercase	:    Optional[str] = None	):


				'''simple docstring'''


				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'	)

				if not os.path.isdir(lowercase	):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
							return
				_snake_case		=			os.path.join(
				    lowercase			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

				if os.path.abspath(self.vocab_file	) != os.path.abspath(lowercase	):
							copyfile(self.vocab_file			,  lowercase	)

				return (out_vocab_file,)