

'''simple docstring'''
import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







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







def       _A	(			snake_case  )    ->			Optional[int]:
   _lowercase  ,   _lowercase    :   Optional[int]      =     [], []
   for title, text in zip(documents["title"]	,							documents["text"]  ):
      if text is not None:
         for passage in split_text(snake_case  ):
            titles.append(title if title is not None else ""  )
            texts.append(snake_case  )
   return {"title": titles, "text": texts}







def       _A	(			snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case  )    ->			int:
   _lowercase    :   Optional[Any]      =     load_dataset(
       "csv"	,							data_files=[csv_path]	,							split="train"	,							delimiter="\t"	,							column_names=["title", "text"]  )
   _lowercase    :   Optional[Any]      =     kb_dataset.map(
       snake_case	,							batched=snake_case	,							num_proc=1  )  # if you want you can load already splitted csv.
   _lowercase    :   List[str]      =     [kb_dataset.shard(snake_case	,							snake_case	,							contiguous=snake_case  ) for i in range(snake_case  )]
   _lowercase    :   Any      =     kb_list[process_num]

   _lowercase    :   List[Any]      =     "data_" + str(snake_case  )
   _lowercase    :   List[str]      =     os.path.join(snake_case	,							snake_case  )

   _lowercase    :   Tuple      =     DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"  )
   _lowercase    :   Optional[int]      =     ctx_encoder.to(device=snake_case  )

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

   _lowercase    :   Optional[Any]      =     Features(
       {"text": Value("string"  ), "title": Value("string"  ), "embeddings": Sequence(Value("float32"  )  )}  )  # optional, save as float32 instead of float64 to save space

   _lowercase    :   Any      =     data_shrad.map(
       partial(snake_case	,							ctx_encoder=snake_case	,							ctx_tokenizer=snake_case	,							device=snake_case  )	,							batched=snake_case	,							batch_size=16	,							features=snake_case	,							)
   dataset.save_to_disk(snake_case  )







def       _A	(			snake_case	,							snake_case  )    ->			Dict:
   _lowercase    :   Tuple      =     []

   for shard_address in glob(str(snake_case  ) + "/*/"  ):
      data_shard_list.append(load_from_disk(snake_case  )  )

   _lowercase    :   str      =     concatenate_datasets(snake_case  )
   faiss.omp_set_num_threads(96  )

   _lowercase    :   List[str]      =     faiss.IndexHNSWFlat(7_68	,							1_28	,							faiss.METRIC_INNER_PRODUCT  )
   concat.add_faiss_index("embeddings"	,							custom_index=snake_case  )
   concat.get_index("embeddings"  ).save(
       snake_case  )  # since we load the index in to memory,we can directly update the index in the disk









'''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






