




'''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


_snake_case				:				Optional[int]       							=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case				:				Union[str, Any]       							=		'cuda' if torch.cuda.is_available() else 'cpu'
def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  Any=100			,						UpperCamelCase	:  Optional[int]=" "				):





							'''simple docstring'''


							_a     					=    text.split(UpperCamelCase				)
							return [character.join(text[i : i + n]				).strip() for i in range(0			,						len(UpperCamelCase				)			,						UpperCamelCase				)]
def 		snake_case_   (UpperCamelCase	:  dict				):





							'''simple docstring'''


							_a					,							_a     					=    [], []
							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 		snake_case_   (UpperCamelCase	:  dict			,						UpperCamelCase	:  DPRContextEncoder			,						UpperCamelCase	:  DPRContextEncoderTokenizerFast				):





							'''simple docstring'''


							_a     					=    ctx_tokenizer(
							    documents['''title''']			,						documents['''text''']			,						truncation=UpperCamelCase			,						padding='''longest'''			,						return_tensors='''pt'''				)['''input_ids''']
							_a     					=    ctx_encoder(input_ids.to(device=UpperCamelCase				)			,						return_dict=UpperCamelCase				).pooler_output
							return {"embeddings": embeddings.detach().cpu().numpy()}
def 		snake_case_   (UpperCamelCase	:  "RagExampleArguments"			,						UpperCamelCase	:  "ProcessingArguments"			,						UpperCamelCase	:  "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
							_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(UpperCamelCase			,						batched=UpperCamelCase			,						num_proc=processing_args.num_proc				)

							# And compute the embeddings
							_a     					=    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				).to(device=UpperCamelCase				)
							_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(UpperCamelCase			,						ctx_encoder=UpperCamelCase			,						ctx_tokenizer=UpperCamelCase				)			,						batched=UpperCamelCase			,						batch_size=processing_args.batch_size			,						features=UpperCamelCase			,						)

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



@dataclass
class       A   :
							lowercase_          =    field(
							    default=_a					,metadata={
							        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
							    }					,)
							lowercase_          =    field(
							    default=16					,metadata={
							        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
							    }					,)



@dataclass
class       A   :
							lowercase_          =    field(
							    default=768					,metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}					,)
							lowercase_          =    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)

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







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

