
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


__a         =   logging.getLogger(__name__)
torch.set_grad_enabled(False)
__a         =   'cuda' if torch.cuda.is_available() else 'cpu'




def 							a		(				snake_case__:   str		,  snake_case__:   Optional[int]=100		,  snake_case__:   Any=" "   ):


				'''simple docstring'''
				lowercase_	    =   text.split(snake_case__   )
				return [character.join(text[i : i + n]   ).strip() for i in range(0		,  len(snake_case__   )		,  snake_case__   )]




def 							a		(				snake_case__:   dict   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   [], []
				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 							a		(				snake_case__:   dict		,  snake_case__:   DPRContextEncoder		,  snake_case__:   DPRContextEncoderTokenizerFast   ):


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




def 							a		(				snake_case__:   "RagExampleArguments"		,  snake_case__:   "ProcessingArguments"		,  snake_case__:   "IndexHnswArguments"		,  ):


				'''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
				lowercase_	    =   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
				lowercase_	    =   dataset.map(snake_case__		,  batched=snake_case__		,  num_proc=processing_args.num_proc   )

				# And compute the embeddings
				lowercase_	    =   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=snake_case__   )
				lowercase_	    =   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
				lowercase_	    =   Features(
				    {'''text''': Value('''string'''   ), '''title''': Value('''string'''   ), '''embeddings''': Sequence(Value('''float32'''   )   )}   )  # optional, save as float32 instead of float64 to save space
				lowercase_	    =   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
				lowercase_	    =   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
				lowercase_	    =   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
				lowercase_	    =   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
@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :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\''}					,		)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		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(UpperCAmelCase				).parent / 'test_run' / 'dummy-kb'				)					,		metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}					,		)

@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :Optional[int]       	=      field(
	    default=UpperCAmelCase					,		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 					lowercase__:



	"""simple docstring"""




	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)

						__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)






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

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


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						__a         =   None

__a         =   logging.get_logger(__name__)

__a         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__a         =   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__a         =   {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Any       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Dict       	=      ['input_ids', 'attention_mask']
	a     :Optional[Any]       	=      BarthezTokenizer
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     Any="<mask>"			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	Any:
					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				tokenizer_file=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   False if not self.vocab_file else True
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep			) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					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(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

					return (out_vocab_file,)




