

'''simple docstring'''





import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Any		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		Optional[Any]=13		,					UpperCAmelCase__     :		Union[str, Any]=7		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		Tuple=99		,					UpperCAmelCase__     :		Tuple=32		,					UpperCAmelCase__     :		List[Any]=5		,					UpperCAmelCase__     :		List[Any]=4		,					UpperCAmelCase__     :		Optional[int]=37		,					UpperCAmelCase__     :		Optional[Any]="gelu"		,					UpperCAmelCase__     :		Dict=0.1		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		Optional[int]=512		,					UpperCAmelCase__     :		Optional[int]=16		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		Union[str, Any]=0.02		,					UpperCAmelCase__     :		List[Any]=4		,					):
										'''simple docstring'''
										lowercase :							str    		=parent
										lowercase :							Tuple    		=batch_size
										lowercase :							List[Any]    		=seq_length
										lowercase :							str    		=is_training
										lowercase :							Optional[Any]    		=use_attention_mask
										lowercase :							Union[str, Any]    		=use_token_type_ids
										lowercase :							int    		=use_labels
										lowercase :							Optional[int]    		=vocab_size
										lowercase :							Any    		=hidden_size
										lowercase :							Dict    		=num_hidden_layers
										lowercase :							Optional[Any]    		=num_attention_heads
										lowercase :							List[str]    		=intermediate_size
										lowercase :							Dict    		=hidden_act
										lowercase :							List[Any]    		=hidden_dropout_prob
										lowercase :							Union[str, Any]    		=attention_probs_dropout_prob
										lowercase :							Any    		=max_position_embeddings
										lowercase :							str    		=type_vocab_size
										lowercase :							List[str]    		=type_sequence_label_size
										lowercase :							Optional[int]    		=initializer_range
										lowercase :							Any    		=num_choices







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size   )

										lowercase :							Optional[int]    		=None
										if self.use_attention_mask:
																lowercase :							str    		=random_attention_mask([self.batch_size, self.seq_length]   )

										lowercase :							Union[str, Any]    		=None
										if self.use_token_type_ids:
																lowercase :							List[str]    		=ids_tensor([self.batch_size, self.seq_length]		,					self.type_vocab_size   )

										lowercase :							Any    		=RobertaConfig(
										    vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					intermediate_size=self.intermediate_size		,					hidden_act=self.hidden_act		,					hidden_dropout_prob=self.hidden_dropout_prob		,					attention_probs_dropout_prob=self.attention_probs_dropout_prob		,					max_position_embeddings=self.max_position_embeddings		,					type_vocab_size=self.type_vocab_size		,					is_decoder=UpperCAmelCase__		,					initializer_range=self.initializer_range		,					)

										return config, input_ids, token_type_ids, attention_mask







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase		,	lowercase		,	lowercase :							List[Any]    		=config_and_inputs
										lowercase :							List[Any]    		={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							str    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase		,	lowercase		,	lowercase :							Dict    		=config_and_inputs

										lowercase :							Any    		=True
										lowercase :							List[Any]    		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
										lowercase :							Union[str, Any]    		=ids_tensor([self.batch_size, self.seq_length]		,					vocab_size=2   )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)





@require_flax
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    True

				lowerCamelCase_		       =    (
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=FlaxRobertaModelTester(self   )







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																lowercase :							str    		=model_class_name.from_pretrained('''roberta-base'''		,					from_pt=UpperCAmelCase__   )
																lowercase :							Optional[int]    		=model(np.ones((1, 1)   )   )
																self.assertIsNotNone(UpperCAmelCase__   )








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






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






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






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






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						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 :							Tuple    		=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 :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

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

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# 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 :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

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





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    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\''}						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/rag-sequence-nq'						,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,							)
				lowerCamelCase_		       =    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_		       =    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 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={
				        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=16						,							metadata={
				        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
				    }						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    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_    						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_    						=       parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														UpperCamelCase_    						=       rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





