


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Dict   						="donut-swin"

						_UpperCAmelCase		:       Tuple   						={
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}






						def __init__(  self							:      str ,  lowerCAmelCase							:      str=2_24 ,  lowerCAmelCase							:      List[Any]=4 ,  lowerCAmelCase							:      Optional[Any]=3 ,  lowerCAmelCase							:      Dict=96 ,  lowerCAmelCase							:      Tuple=[2, 2, 6, 2] ,  lowerCAmelCase							:      Tuple=[3, 6, 12, 24] ,  lowerCAmelCase							:      Optional[Any]=7 ,  lowerCAmelCase							:      str=4.0 ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      Union[str, Any]=0.0 ,  lowerCAmelCase							:      Dict=0.0 ,  lowerCAmelCase							:      Dict=0.1 ,  lowerCAmelCase							:      Any="gelu" ,  lowerCAmelCase							:      Optional[int]=False ,  lowerCAmelCase							:      Tuple=0.0_2 ,  lowerCAmelCase							:      int=1e-5 ,  **lowerCAmelCase							:      Optional[int] ,  ):
													super().__init__(**lowerCAmelCase	)

													A_			    =		image_size
													A_			    =		patch_size
													A_			    =		num_channels
													A_			    =		embed_dim
													A_			    =		depths
													A_			    =		len(lowerCAmelCase	)
													A_			    =		num_heads
													A_			    =		window_size
													A_			    =		mlp_ratio
													A_			    =		qkv_bias
													A_			    =		hidden_dropout_prob
													A_			    =		attention_probs_dropout_prob
													A_			    =		drop_path_rate
													A_			    =		hidden_act
													A_			    =		use_absolute_embeddings
													A_			    =		layer_norm_eps
													A_			    =		initializer_range
													# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													A_			    =		int(embed_dim * 2 ** (len(lowerCAmelCase	) - 1)	)









'''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					:				Optional[int]									=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE					:				Dict									=		'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







def       a_				(	UpperCamelCase_ ):
							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       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							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       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							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 					__lowerCAmelCase   :

						"""simple docstring"""






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






						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			metadata={
						        "help": "The number of processes to use to split the documents into passages. Default is single process."
						    }				,			)
						_UpperCAmelCase		:       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"""






						_UpperCAmelCase		:       int   						=field(
						    default=768				,			metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}				,			)
						_UpperCAmelCase		:       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					:				List[str]									=		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														__SCREAMING_SNAKE_CASE					:				List[str]									=		rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





