
'''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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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







def       _A	(			snake_case	,							snake_case=1_00	,							snake_case=" "  )    ->			List[str]:
   _lowercase    :   int      =     text.split(_UpperCAmelCase  )
   return [character.join(text[i : i + n]  ).strip() for i in range(0	,							len(_UpperCAmelCase  )	,							_UpperCAmelCase  )]







def       _A	(			snake_case  )    ->			dict:
   _lowercase  ,   _lowercase    :   Dict      =     [], []
   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	(			snake_case	,							snake_case	,							snake_case  )    ->			dict:
   _lowercase    :   Union[str, Any]      =     ctx_tokenizer(
       documents["title"]	,							documents["text"]	,							truncation=_UpperCAmelCase	,							padding="longest"	,							return_tensors="pt"  )["input_ids"]
   _lowercase    :   List[Any]      =     ctx_encoder(input_ids.to(device=_UpperCAmelCase  )	,							return_dict=_UpperCAmelCase  ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}







def       _A	(			snake_case	,							snake_case	,							snake_case	,							)    ->			Union[str, Any]:
   ######################################
   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    :   List[str]      =     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[Any]      =     dataset.map(_UpperCAmelCase	,							batched=_UpperCAmelCase	,							num_proc=processing_args.num_proc  )

   # And compute the embeddings
   _lowercase    :   str      =     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=_UpperCAmelCase  )
   _lowercase    :   int      =     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
   _lowercase    :   Union[str, Any]      =     Features(
       {"text": Value("string"  ), "title": Value("string"  ), "embeddings": Sequence(Value("float32"  )  )}  )  # optional, save as float32 instead of float64 to save space
   _lowercase    :   Dict      =     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
   _lowercase    :   List[Any]      =     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
   _lowercase    :   Any      =     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
   _lowercase    :   int      =     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

   ######################################
   logger.info("Step 3 - Load RAG"  )
   ######################################

   # Easy way to load the model
   _lowercase    :   List[Any]      =     RagRetriever.from_pretrained(
       rag_example_args.rag_model_name	,							index_name="custom"	,							indexed_dataset=_UpperCAmelCase  )
   _lowercase    :   int      =     RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name	,							retriever=_UpperCAmelCase  )
   _lowercase    :   str      =     RagTokenizer.from_pretrained(rag_example_args.rag_model_name  )

   # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
   # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

   ######################################
   logger.info("Step 4 - Have fun"  )
   ######################################

   _lowercase    :   Any      =     rag_example_args.question or "What does Moses\' rod turn into ?"
   _lowercase    :   List[str]      =     tokenizer.question_encoder(_UpperCAmelCase	,							return_tensors="pt"  )["input_ids"]
   _lowercase    :   Any      =     model.generate(_UpperCAmelCase  )
   _lowercase    :   str      =     tokenizer.batch_decode(_UpperCAmelCase	,							skip_special_tokens=_UpperCAmelCase  )[0]
   logger.info("Q: " + question  )
   logger.info("A: " + generated_string  )


@dataclass
class      a__						:
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							field(
       default=str(Path(lowerCamelCase__						).parent / 'test_data' / 'my_knowledge_dataset.csv'						)						,   metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						,   )
   _SCREAMING_SNAKE_CASE					:  Optional[Any]			 =							field(
       default=lowerCamelCase__						,   metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,   )
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							field(
       default='facebook/rag-sequence-nq'						,   metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,   )
   _SCREAMING_SNAKE_CASE					:  Dict			 =							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\''
           )
       }						,   )
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							field(
       default=lowerCamelCase__						,   metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						,   )




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




@dataclass
class      a__						:
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							field(
       default=768						,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,   )
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							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     						=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    _snake_case						,			_snake_case						,			_snake_case     						=						parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        _snake_case     						=						rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)









'''simple docstring'''
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Zone of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of Cloud TPU-enabled project'}						,   )

   _SCREAMING_SNAKE_CASE					:  float			 =							field(
       default=1.0						,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}						,   )

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   metadata={'help': 'Whether to activate the XLA compilation or not'}						)


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      logger.info("Tensorflow: setting up strategy"						)

      _lowercase    :   Optional[int]      =     tf.config.list_physical_devices("GPU"						)

      # Set to float16 at first
      if self.fpaa:
         tf.keras.mixed_precision.set_global_policy("mixed_float16"						)

      if self.no_cuda:
         _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     tf.distribute.cluster_resolver.TPUClusterResolver(
                   self.tpu_name    ,       zone=self.tpu_zone    ,       project=self.gcp_project						)
            else:
               _lowercase    :   Any      =     tf.distribute.cluster_resolver.TPUClusterResolver()
         except ValueError:
            if self.tpu_name:
               raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
            else:
               _lowercase    :   Tuple      =     None

         if tpu:
            # Set to bfloat16 in case of TPU
            if self.fpaa:
               tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"						)

            tf.config.experimental_connect_to_cluster(_UpperCamelCase						)
            tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
         elif len(_UpperCamelCase						) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            _lowercase    :   Any      =     tf.distribute.MirroredStrategy()
         else:
            raise ValueError("Cannot find the proper strategy, please check your environment properties."						)

      return strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy.num_replicas_in_sync


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return False  # TF Logging is handled by Keras not the Trainer


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.per_gpu_train_batch_size:
         logger.warning(
             "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
             "version. Using `--per_device_train_batch_size` is preferred."						)
      _lowercase    :   str      =     self.per_gpu_train_batch_size or self.per_device_train_batch_size
      return per_device_batch_size * self.n_replicas


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.per_gpu_eval_batch_size:
         logger.warning(
             "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
             "version. Using `--per_device_eval_batch_size` is preferred."						)
      _lowercase    :   List[Any]      =     self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
      return per_device_batch_size * self.n_replicas






   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      warnings.warn(
          "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."    ,       _UpperCamelCase    ,       )
      return self._setup_strategy.num_replicas_in_sync






