"""simple docstring"""
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__      						=     logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE__      						=     "cuda" if torch.cuda.is_available() else "cpu"





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Tuple=1_0_0 ,			_UpperCamelCase       :				str=" "					)					->				List[str]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				dict					)					->				dict:





						"""simple docstring"""






						snake_case					,snake_case								=			[], []
						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    lowerCAmelCase__    (			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				DPRContextEncoder ,			_UpperCamelCase       :				DPRContextEncoderTokenizerFast					)					->				dict:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				"RagExampleArguments" ,			_UpperCamelCase       :				"ProcessingArguments" ,			_UpperCamelCase       :				"IndexHnswArguments" ,			)					->				Tuple:





						"""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
						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(_UpperCamelCase ,			batched=_UpperCamelCase ,			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=_UpperCamelCase					)
						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(_UpperCamelCase ,			ctx_encoder=_UpperCamelCase ,			ctx_tokenizer=_UpperCamelCase					) ,			batched=_UpperCamelCase ,			batch_size=processing_args.batch_size ,			features=_UpperCamelCase ,			)

						# And finally save your dataset
						snake_case								=			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
						snake_case								=			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
						snake_case								=			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



@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						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'"""}     ,    )
				_lowerCAmelCase     :    Optional[str]			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}     ,    )
				_lowerCAmelCase     :    str			  =						field(
				    default="""facebook/rag-sequence-nq"""     ,    metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}     ,    )
				_lowerCAmelCase     :    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'"""
				        )
				    }     ,    )
				_lowerCAmelCase     :    Optional[str]			  =						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   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    Optional[int]			  =						field(
				    default=lowerCAmelCase     ,    metadata={
				        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
				    }     ,    )
				_lowerCAmelCase     :    int			  =						field(
				    default=16     ,    metadata={
				        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
				    }     ,    )





@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    int			  =						field(
				    default=768     ,    metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}     ,    )
				_lowerCAmelCase     :    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__      						=     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
					SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__      						=     parser.parse_args_into_dataclasses()
					with TemporaryDirectory() as tmp_dir:
										SCREAMING_SNAKE_CASE__      						=     rag_example_args.output_dir or tmp_dir
										main(rag_example_args, processing_args, index_hnsw_args)



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__      						=  {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__      						=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

SCREAMING_SNAKE_CASE__      						=  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}





@lru_cache()
def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			(
						    list(range(ord('!'					) ,			ord('~'					) + 1					)					) + list(range(ord('¡'					) ,			ord('¬'					) + 1					)					) + list(range(ord('®'					) ,			ord('ÿ'					) + 1					)					)
						)
						snake_case								=			bs[:]
						snake_case								=			0
						for b in range(2**8					):
												if b not in bs:
																		bs.append(_UpperCamelCase					)
																		cs.append(2**8 + n					)
																		n += 1
						snake_case								=			[chr(_UpperCamelCase					) for n in cs]
						return dict(zip(_UpperCamelCase ,			_UpperCamelCase					)					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				List[str]:





						"""simple docstring"""






						snake_case								=			set()
						snake_case								=			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												snake_case								=			char
						return pairs



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						VOCAB_FILES_NAMES
				_lowerCAmelCase     :    str			  =						PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase     :    int			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase     :    str			  =						["""input_ids""", """attention_mask"""]

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase="replace"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="<unk>"		,    lowerCAmelCase="<pad>"		,    lowerCAmelCase="<mask>"		,    lowerCAmelCase=False		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else bos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else eos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else sep_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else cls_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else unk_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else mask_token

										super().__init__(
										    errors=lowerCAmelCase		,    bos_token=lowerCAmelCase		,    eos_token=lowerCAmelCase		,    unk_token=lowerCAmelCase		,    sep_token=lowerCAmelCase		,    cls_token=lowerCAmelCase		,    pad_token=lowerCAmelCase		,    mask_token=lowerCAmelCase		,    add_prefix_space=lowerCAmelCase		,    **lowerCAmelCase		,    )

										with open(lowerCAmelCase		,    encoding='utf-8'    ) as vocab_handle:
																snake_case								=			json.load(lowerCAmelCase    )
										snake_case								=			{v: k for k, v in self.encoder.items()}
										snake_case								=			errors  # how to handle errors in decoding
										snake_case								=			bytes_to_unicode()
										snake_case								=			{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase		,    encoding='utf-8'    ) as merges_handle:
																snake_case								=			merges_handle.read().split('\n'    )[1:-1]
										snake_case								=			[tuple(merge.split()    ) for merge in bpe_merges]
										snake_case								=			dict(zip(lowerCAmelCase		,    range(len(lowerCAmelCase    )    )    )    )
										snake_case								=			{}
										snake_case								=			add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case								=			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return len(self.encoder    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										return dict(self.encoder		,    **self.added_tokens_encoder    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if token in self.cache:
																return self.cache[token]
										snake_case								=			tuple(lowerCAmelCase    )
										snake_case								=			get_pairs(lowerCAmelCase    )

										if not pairs:
																return token

										while True:
																snake_case								=			min(lowerCAmelCase		,    key=lambda lowerCAmelCase    : self.bpe_ranks.get(lowerCAmelCase		,    float('inf'    )    )    )
																if bigram not in self.bpe_ranks:
																						break
																snake_case					,snake_case								=			bigram
																snake_case								=			[]
																snake_case								=			0
																while i < len(lowerCAmelCase    ):
																						try:
																												snake_case								=			word.index(lowerCAmelCase		,    lowerCAmelCase    )
																						except ValueError:
																												new_word.extend(word[i:]    )
																												break
																						else:
																												new_word.extend(word[i:j]    )
																												snake_case								=			j

																						if word[i] == first and i < len(lowerCAmelCase    ) - 1 and word[i + 1] == second:
																												new_word.append(first + second    )
																												i += 2
																						else:
																												new_word.append(word[i]    )
																												i += 1
																snake_case								=			tuple(lowerCAmelCase    )
																snake_case								=			new_word
																if len(lowerCAmelCase    ) == 1:
																						break
																else:
																						snake_case								=			get_pairs(lowerCAmelCase    )
										snake_case								=			' '.join(lowerCAmelCase    )
										snake_case								=			word
										return word

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			[]
										for token in re.findall(self.pat		,    lowerCAmelCase    ):
																snake_case								=			''.join(
																    self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase    ).split(' '    )    )
										return bpe_tokens

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.encoder.get(lowerCAmelCase		,    self.encoder.get(self.unk_token    )    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.decoder.get(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			''.join(lowerCAmelCase    )
										snake_case								=			bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'		,    errors=self.errors    )
										return text

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

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

										with open(lowerCAmelCase		,    'w'		,    encoding='utf-8'    ) as f:
																f.write(json.dumps(self.encoder		,    indent=2		,    sort_keys=lowerCAmelCase		,    ensure_ascii=lowerCAmelCase    ) + '\n'    )

										snake_case								=			0
										with open(lowerCAmelCase		,    'w'		,    encoding='utf-8'    ) as writer:
																writer.write('#version: 0.2\n'    )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,    key=lambda lowerCAmelCase    : kv[1]    ):
																						if index != token_index:
																												logger.warning(
																												    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																												    ' Please check that the tokenizer is not corrupted!'    )
																												snake_case								=			token_index
																						writer.write(' '.join(lowerCAmelCase    ) + '\n'    )
																						index += 1

										return vocab_file, merge_file

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case								=			[self.cls_token_id]
										snake_case								=			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = False    ):






										"""simple docstring"""

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase		,    token_ids_a=lowerCAmelCase		,    already_has_special_tokens=lowerCAmelCase    )

										if token_ids_a is None:
																return [1] + ([0] * len(lowerCAmelCase    )) + [1]
										return [1] + ([0] * len(lowerCAmelCase    )) + [1, 1] + ([0] * len(lowerCAmelCase    )) + [1]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			[self.sep_token_id]
										snake_case								=			[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 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=False		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			kwargs.pop('add_prefix_space'		,    self.add_prefix_space    )
										if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase    ) > 0 and not text[0].isspace()):
																snake_case								=			' ' + text
										return (text, kwargs)


