






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


def 				lowercase_					(     __A			:							str       ,   __A			:							Union[str, Any]=1_0_0       ,   __A			:							Optional[int]=" "      )							->   List[str]:



 """simple docstring"""




 lowercase       : List[Any]						=text.split(__A      )
 return [character.join(text[i : i + n]      ).strip() for i in range(0       ,   len(__A      )       ,   __A      )]


def 				lowercase_					(     __A			:							dict      )							->   dict:



 """simple docstring"""




 lowercase	,		lowercase       : str						=[], []
 for title, text in zip(documents['''title''']       ,   documents['''text''']      ):
  if text is not None:
   for passage in split_text(__A      ):
    titles.append(title if title is not None else ''''''      )
    texts.append(__A      )
 return {"title": titles, "text": texts}


def 				lowercase_					(     __A			:							dict       ,   __A			:							DPRContextEncoder       ,   __A			:							DPRContextEncoderTokenizerFast      )							->   dict:



 """simple docstring"""




 lowercase       : str						=ctx_tokenizer(
     documents['''title''']       ,   documents['''text''']       ,   truncation=__A       ,   padding='''longest'''       ,   return_tensors='''pt'''      )['''input_ids''']
 lowercase       : Dict						=ctx_encoder(input_ids.to(device=__A      )       ,   return_dict=__A      ).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}


def 				lowercase_					(     __A			:							"RagExampleArguments"       ,   __A			:							"ProcessingArguments"       ,   __A			:							"IndexHnswArguments"       ,   )							->   Optional[int]:



 """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
 lowercase       : Optional[int]						=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       : Dict						=dataset.map(__A       ,   batched=__A       ,   num_proc=processing_args.num_proc      )

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

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

 # And save the index
 lowercase       : List[str]						=os.path.join(rag_example_args.output_dir       ,   '''my_knowledge_dataset_hnsw_index.faiss'''      )
 dataset.get_index('''embeddings'''      ).save(__A      )
 # 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)

      SCREAMING_SNAKE_CASE							=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE		,  SCREAMING_SNAKE_CASE		,  SCREAMING_SNAKE_CASE							=						parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE							=						rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)











'''simple docstring'''



from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
      from .deepspeed import (
          DeepSpeedEngineWrapper,
          DeepSpeedOptimizerWrapper,
          DeepSpeedSchedulerWrapper,
          DummyOptim,
          DummyScheduler,
          HfDeepSpeedConfig,
      )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers



