
"""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


UpperCamelCase_       :    Tuple						      =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_       :    Union[str, Any]						      =			'''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_			,     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 __lowerCAmelCase					:




			"""simple docstring"""







			snake_case						   =					field(
			    default=str(Path(_lowercase   ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"   )  ,					metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}  ,					)
			snake_case						   =					field(
			    default=_lowercase  ,					metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}  ,					)
			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'"}  ,					)
			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'"
			        )
			    }  ,					)
			snake_case						   =					field(
			    default=str(Path(_lowercase   ).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"""







			snake_case						   =					field(
			    default=_lowercase  ,					metadata={
			        "help": "The number of processes to use to split the documents into passages. Default is single process."
			    }  ,					)
			snake_case						   =					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"""







			snake_case						   =					field(
			    default=7_68  ,					metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}  ,					)
			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)

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






"""simple docstring"""





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				A_  (__a					, __a					, __a					, __a					, __a					):

					'''simple docstring'''





					with open(__a					) as metadata_file:
										A_								=    json.load(__a					)
					A_								=    LukeConfig(use_entity_aware_attention=__a					, **metadata["model_config"]					)

					# Load in the weights from the checkpoint_path
					A_								=    torch.load(__a					, map_location="cpu"					)

					# Load the entity vocab file
					A_								=    load_entity_vocab(__a					)

					A_								=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]					)

					# Add special tokens to the token vocabulary for downstream tasks
					A_								=    AddedToken("<ent>"					, lstrip=__a					, rstrip=__a					)
					A_								=    AddedToken("<ent2>"					, lstrip=__a					, rstrip=__a					)
					tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}					)
					config.vocab_size += 2

					print(f'Saving tokenizer to {pytorch_dump_folder_path}'					)
					tokenizer.save_pretrained(__a					)
					with open(os.path.join(__a					, LukeTokenizer.vocab_files_names["entity_vocab_file"]					)					, "w"					) as f:
										json.dump(__a					, __a					)

					A_								=    LukeTokenizer.from_pretrained(__a					)

					# Initialize the embeddings of the special tokens
					A_								=    state_dict["embeddings.word_embeddings.weight"]
					A_								=    word_emb[tokenizer.convert_tokens_to_ids(["@"]					)[0]].unsqueeze(0					)
					A_								=    word_emb[tokenizer.convert_tokens_to_ids(["#"]					)[0]].unsqueeze(0					)
					A_								=    torch.cat([word_emb, ent_emb, enta_emb]					)

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers					):
										for matrix_name in ["query.weight", "query.bias"]:
															A_								=    f'encoder.layer.{layer_index}.attention.self.'
															A_								=    state_dict[prefix + matrix_name]
															A_								=    state_dict[prefix + matrix_name]
															A_								=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					A_								=    state_dict["entity_embeddings.entity_embeddings.weight"]
					A_								=    entity_emb[entity_vocab["[MASK]"]]

					A_								=    LukeModel(config=__a					).eval()

					A_			,     A_								=    model.load_state_dict(__a					, strict=__a					)
					if not (len(__a					) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(f'Missing keys {", ".join(__a					)}. Expected only missing embeddings.position_ids'					)
					if not (all(key.startswith("entity_predictions"					) or key.startswith("lm_head"					) for key in unexpected_keys					)):
										raise ValueError(
										    "Unexpected keys"
										    f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"					) or key.startswith("lm_head"					))]					)}'					)

					# Check outputs
					A_								=    LukeTokenizer.from_pretrained(__a					, task="entity_classification"					)

					A_								=    (
					    "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
					    " new world number one avoid a humiliating second- round exit at Wimbledon ."
					)
					A_								=    (39, 42)
					A_								=    tokenizer(__a					, entity_spans=[span]					, add_prefix_space=__a					, return_tensors="pt"					)

					A_								=    model(**__a					)

					# Verify word hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 42, 1024)					)
										A_								=    torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]					)
					else:  # base
										A_								=    torch.Size((1, 42, 768)					)
										A_								=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]					)

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'					)
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3]					, __a					, atol=1e-4					):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 1, 1024)					)
										A_								=    torch.tensor([[0.0466, -0.0106, -0.0179]]					)
					else:  # base
										A_								=    torch.Size((1, 1, 768)					)
										A_								=    torch.tensor([[0.1457, 0.1044, 0.0174]]					)

					if not (outputs.entity_last_hidden_state.shape != expected_shape):
										raise ValueError(
										    f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
										    f' {expected_shape}'					)
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]					, __a					, atol=1e-4					):
										raise ValueError

					# Finally, save our PyTorch model and tokenizer
					print("Saving PyTorch model to {}".format(__a					)					)
					model.save_pretrained(__a					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    {}
					with open(__a					, "r"					, encoding="utf-8"					) as f:
										for index, line in enumerate(__a					):
															A_			,     A_								=    line.rstrip().split("\t"					)
															A_								=    index

					return entity_vocab


if __name__ == "__main__":
		UpperCamelCase_       :    Dict						      =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
		parser.add_argument(
		    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
		)
		parser.add_argument(
		    '''--entity_vocab_path''',
		    default=None,
		    type=str,
		    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
		)
		UpperCamelCase_       :    List[str]						      =			parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)




