






'''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     :   List[Any]	      =		logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase     :   List[Any]	      =		'cuda' if torch.cuda.is_available() else 'cpu'
def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      int=100 ,     __lowerCAmelCase		:      Optional[int]=" "    ):
						lowerCamelCase__								=	text.split(SCREAMING_SNAKE_CASE_    )
						return [character.join(text[i : i + n]    ).strip() for i in range(0 ,     len(SCREAMING_SNAKE_CASE_    ) ,     SCREAMING_SNAKE_CASE_    )]
def     A__      (					__lowerCAmelCase		:      dict    ):
						lowerCamelCase__							,      lowerCamelCase__								=	[], []
						for title, text in zip(documents["""title"""] ,     documents["""text"""]    ):
												if text is not None:
																		for passage in split_text(SCREAMING_SNAKE_CASE_    ):
																								titles.append(title if title is not None else """"""    )
																								texts.append(SCREAMING_SNAKE_CASE_    )
						return {"title": titles, "text": texts}
def     A__      (					__lowerCAmelCase		:      dict ,     __lowerCAmelCase		:      DPRContextEncoder ,     __lowerCAmelCase		:      DPRContextEncoderTokenizerFast    ):
						lowerCamelCase__								=	ctx_tokenizer(
						    documents["""title"""] ,     documents["""text"""] ,     truncation=SCREAMING_SNAKE_CASE_ ,     padding="""longest""" ,     return_tensors="""pt"""    )["""input_ids"""]
						lowerCamelCase__								=	ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE_    ) ,     return_dict=SCREAMING_SNAKE_CASE_    ).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}
def     A__      (					__lowerCAmelCase		:      "RagExampleArguments" ,     __lowerCAmelCase		:      "ProcessingArguments" ,     __lowerCAmelCase		:      "IndexHnswArguments" ,     ):
						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
						lowerCamelCase__								=	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
						lowerCamelCase__								=	dataset.map(SCREAMING_SNAKE_CASE_ ,     batched=SCREAMING_SNAKE_CASE_ ,     num_proc=processing_args.num_proc    )

						# And compute the embeddings
						lowerCamelCase__								=	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    ).to(device=SCREAMING_SNAKE_CASE_    )
						lowerCamelCase__								=	DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    )
						lowerCamelCase__								=	Features(
						    {"""text""": Value("""string"""    ), """title""": Value("""string"""    ), """embeddings""": Sequence(Value("""float32"""    )    )}    )  # optional, save as float32 instead of float64 to save space
						lowerCamelCase__								=	dataset.map(
						    partial(SCREAMING_SNAKE_CASE_ ,     ctx_encoder=SCREAMING_SNAKE_CASE_ ,     ctx_tokenizer=SCREAMING_SNAKE_CASE_    ) ,     batched=SCREAMING_SNAKE_CASE_ ,     batch_size=processing_args.batch_size ,     features=SCREAMING_SNAKE_CASE_ ,     )

						# And finally save your dataset
						lowerCamelCase__								=	os.path.join(rag_example_args.output_dir ,     """my_knowledge_dataset"""    )
						dataset.save_to_disk(SCREAMING_SNAKE_CASE_    )
						# 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
						lowerCamelCase__								=	faiss.IndexHNSWFlat(index_hnsw_args.d ,     index_hnsw_args.m ,     faiss.METRIC_INNER_PRODUCT    )
						dataset.add_faiss_index("""embeddings""" ,     custom_index=SCREAMING_SNAKE_CASE_    )

						# And save the index
						lowerCamelCase__								=	os.path.join(rag_example_args.output_dir ,     """my_knowledge_dataset_hnsw_index.faiss"""    )
						dataset.get_index("""embeddings"""    ).save(SCREAMING_SNAKE_CASE_    )
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index




@dataclass
class 		UpperCamelCase__     :




		'''simple docstring'''





		_UpperCamelCase						=       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\''} ,)
		_UpperCamelCase						=       field(
		    default=a ,metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} ,)
		_UpperCamelCase						=       field(
		    default='facebook/rag-sequence-nq' ,metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} ,)
		_UpperCamelCase						=       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						=       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 		UpperCamelCase__     :




		'''simple docstring'''





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




@dataclass
class 		UpperCamelCase__     :




		'''simple docstring'''





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

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








'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "blenderbot-small"
 __lowerCamelCase    :				Optional[Any]								=       ["past_key_values"]
 __lowerCamelCase    :				str								=       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




 def __init__(						self      ,  _lowerCAmelCase=50265      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=2      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   d_model
     _lowerCAmelCase						      =   encoder_ffn_dim
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   encoder_attention_heads
     _lowerCAmelCase						      =   decoder_ffn_dim
     _lowerCAmelCase						      =   decoder_layers
     _lowerCAmelCase						      =   decoder_attention_heads
     _lowerCAmelCase						      =   dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   init_std
     _lowerCAmelCase						      =   encoder_layerdrop
     _lowerCAmelCase						      =   decoder_layerdrop
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  is_encoder_decoder=_lowerCAmelCase      ,  decoder_start_token_id=_lowerCAmelCase      ,  forced_eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]		)

         if self.use_past:
             _lowerCAmelCase						      =   {0: "batch"}
             _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
             _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}
             _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
             self.fill_with_past_key_values_(_lowerCAmelCase      ,  direction="inputs"		)
     elif self.task == "causal-lm":
         # TODO: figure this case out.
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]		)
         if self.use_past:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
             for i in range(_lowerCAmelCase		):
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
     else:
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                 ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
             ]		)

     return common_inputs




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super().outputs
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		).outputs
         if self.use_past:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
             for i in range(_lowerCAmelCase		):
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
     return common_outputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

     # Generate decoder inputs
     _lowerCAmelCase						      =   seq_length if not self.use_past else 1
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     _lowerCAmelCase						      =   dict(**_lowerCAmelCase      ,  **_lowerCAmelCase		)

     if self.use_past:
         if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
         else:
             import torch
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   common_inputs["input_ids"].shape
         _lowerCAmelCase						      =   common_inputs["decoder_input_ids"].shape[1]
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_attention_heads
         _lowerCAmelCase						      =   (
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _lowerCAmelCase						      =   decoder_seq_length + 3
         _lowerCAmelCase						      =   (
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         _lowerCAmelCase						      =   torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(_lowerCAmelCase      ,  _lowerCAmelCase		)]      ,  dim=1		)

         _lowerCAmelCase						      =   []
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
         _lowerCAmelCase						      =   min(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   max(_lowerCAmelCase      ,  _lowerCAmelCase		) - min_num_layers
         _lowerCAmelCase						      =   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

         for _ in range(_lowerCAmelCase		):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(_lowerCAmelCase		),
                     torch.zeros(_lowerCAmelCase		),
                     torch.zeros(_lowerCAmelCase		),
                     torch.zeros(_lowerCAmelCase		),
                 )		)
         # TODO: test this.
         _lowerCAmelCase						      =   encoder_shape if remaining_side_name == "encoder" else decoder_shape
         for _ in range(_lowerCAmelCase      ,  _lowerCAmelCase		):
             common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase		), torch.zeros(_lowerCAmelCase		))		)
     return common_inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

     if self.use_past:
         if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
         else:
             import torch
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   common_inputs["input_ids"].shape
         # Not using the same length for past_key_values
         _lowerCAmelCase						      =   seqlen + 2
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_attention_heads
         _lowerCAmelCase						      =   (
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         _lowerCAmelCase						      =   common_inputs["attention_mask"].dtype
         _lowerCAmelCase						      =   torch.cat(
             [common_inputs["attention_mask"], torch.ones(_lowerCAmelCase      ,  _lowerCAmelCase      ,  dtype=_lowerCAmelCase		)]      ,  dim=1		)
         _lowerCAmelCase						      =   [
             (torch.zeros(_lowerCAmelCase		), torch.zeros(_lowerCAmelCase		)) for _ in range(_lowerCAmelCase		)
         ]
     return common_inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  fixed_dimension=OnnxConfig.default_fixed_batch      ,  num_token_to_add=0		)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     _lowerCAmelCase						      =   tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  fixed_dimension=OnnxConfig.default_fixed_sequence      ,  num_token_to_add=_lowerCAmelCase		)

     # Generate dummy inputs according to compute batch and sequence
     _lowerCAmelCase						      =   [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
     _lowerCAmelCase						      =   dict(tokenizer(_lowerCAmelCase      ,  return_tensors=_lowerCAmelCase		)		)
     return common_inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase		)

     elif self.task == "causal-lm":
         _lowerCAmelCase						      =   self._generate_dummy_inputs_for_causal_lm(
             _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase		)

     return common_inputs





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super()._flatten_past_key_values_(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		)._flatten_past_key_values_(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)


