


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						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		List[Any]=1_0_0				,      SCREAMING_SNAKE_CASE_   :		Dict=" "							):






       """simple docstring"""





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


def   a						(				SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		[], []
       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   a						(				SCREAMING_SNAKE_CASE_   :		List[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]				,      SCREAMING_SNAKE_CASE_   :		List[Any]				,      SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						Dict 						=		load_dataset(
           '''csv'''				,      data_files=[csv_path]				,      split='''train'''				,      delimiter='''\t'''				,      column_names=['''title''', '''text''']							)
       UpperCamelCase     :						int 						=		kb_dataset.map(
           __A				,      batched=__A				,      num_proc=1							)  # if you want you can load already splitted csv.
       UpperCamelCase     :						str 						=		[kb_dataset.shard(__A				,      __A				,      contiguous=__A							) for i in range(__A							)]
       UpperCamelCase     :						int 						=		kb_list[process_num]

       UpperCamelCase     :						List[str] 						=		"""data_""" + str(__A							)
       UpperCamelCase     :						List[str] 						=		os.path.join(__A				,      __A							)

       UpperCamelCase     :						Tuple 						=		DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''							)
       UpperCamelCase     :						List[str] 						=		ctx_encoder.to(device=__A							)

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

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

       UpperCamelCase     :						int 						=		data_shrad.map(
           partial(__A				,      ctx_encoder=__A				,      ctx_tokenizer=__A				,      device=__A							)				,      batched=__A				,      batch_size=1_6				,      features=__A				,      )
       dataset.save_to_disk(__A							)


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		[]

       for shard_address in glob(str(__A							) + '''/*/'''							):
              data_shard_list.append(load_from_disk(__A							)							)

       UpperCamelCase     :						Optional[Any] 						=		concatenate_datasets(__A							)
       faiss.omp_set_num_threads(9_6							)

       UpperCamelCase     :						Optional[Any] 						=		faiss.IndexHNSWFlat(7_6_8				,      1_2_8				,      faiss.METRIC_INNER_PRODUCT							)
       concat.add_faiss_index('''embeddings'''				,      custom_index=__A							)
       concat.get_index('''embeddings'''							).save(
           __A							)  # since we load the index in to memory,we can directly update the index in the disk


import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def     A__	(       __A ,			__A ,			__A ,			__A=None						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    None
    # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
    if str(weight.dtype						) == "torch.bfloat16":
        # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
        _lowerCamelCase  :		int			    =    weight.view(torch.intaa						)
        _lowerCamelCase  :		List[Any]			    =    """bfloat16"""
    _lowerCamelCase  :		Tuple			    =    weight.cpu().numpy()
    _lowerCamelCase  :		Tuple			    =    os.path.join(__A ,			F"""{weight_name}.dat"""						)
    if index is not None:
        if dtype is None:
            _lowerCamelCase  :		Union[str, Any]			    =    str(array.dtype						)
        _lowerCamelCase  :		Optional[Any]			    =    {"""dtype""": dtype, """shape""": list(array.shape						)}
    if array.ndim == 0:
        _lowerCamelCase  :		int			    =    array[None]
    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=array.dtype ,			mode="""w+""" ,			shape=array.shape						)
    _lowerCamelCase  :		Union[str, Any]			    =    array[:]
    file_array.flush()
    return index







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    tuple(weight_info["""shape"""]						)
    if shape == ():
        # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
        _lowerCamelCase  :		Union[str, Any]			    =    (1,)

    _lowerCamelCase  :		str			    =    weight_info["""dtype"""]
    if dtype == "bfloat16":
        # NumPy does not support bfloat16 so this was saved as a int16
        _lowerCamelCase  :		Optional[int]			    =    """int16"""

    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=__A ,			shape=__A ,			mode="""r"""						)

    if len(weight_info["""shape"""]						) == 0:
        _lowerCamelCase  :		List[str]			    =    weight[0]
    _lowerCamelCase  :		List[str]			    =    torch.tensor(__A						)
    if weight_info["dtype"] == "bfloat16":
        _lowerCamelCase  :		List[Any]			    =    weight.view(torch.bfloataa						)

    return weight







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    if index is None or len(__A						) == 0:
        # Nothing to save
        return

    _lowerCamelCase  :		List[Any]			    =    os.path.join(__A ,			"""index.json"""						)
    if os.path.isfile(__A						):
        with open(__A ,			"""r""" ,			encoding="""utf-8"""						) as f:
            _lowerCamelCase  :		List[str]			    =    json.load(__A						)
    else:
        _lowerCamelCase  :		List[str]			    =    {}
    current_index.update(__A						)

    with open(__A ,			"""w""" ,			encoding="""utf-8"""						) as f:
        json.dump(__A ,			__A ,			indent=2						)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    os.makedirs(__A ,			exist_ok=__A						)
    _lowerCamelCase  :		Dict			    =    {}
    for name, parameter in state_dict.items():
        _lowerCamelCase  :		Union[str, Any]			    =    offload_weight(__A ,			__A ,			__A ,			index=__A						)

    # Update index
    save_offload_index(__A ,			__A						)




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					str     ,	_UpperCamelCase  :					Mapping     ,	_UpperCamelCase  :					str)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    dataset
          _lowerCamelCase  :		Tuple			    =    prefix



      def __getitem__(       self  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          return self.dataset[F"""{self.prefix}{key}"""]



      def __iter__(       self  :					List[str])					->Any:


          """simple docstring"""


          return iter([key for key in self.dataset if key.startswith(self.prefix)])




      def __len__(       self  :					Any)					->int:


          """simple docstring"""


          return len(self.dataset)





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Dict[str, torch.Tensor] = None     ,	_UpperCamelCase  :					Optional[Union[str, os.PathLike]] = None     ,	_UpperCamelCase  :					Mapping = None     ,	_UpperCamelCase  :					Union[str, Any]=None     ,	)					->Any:


          """simple docstring"""


          if state_dict is None and save_folder is None:
              raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights.""")

          _lowerCamelCase  :		int			    =    {} if state_dict is None else state_dict
          _lowerCamelCase  :		Optional[int]			    =    save_folder
          if index is None and save_folder is not None:
              with open(os.path.join(_UpperCamelCase     ,	"""index.json""")) as f:
                  _lowerCamelCase  :		Union[str, Any]			    =    json.load(_UpperCamelCase)
          _lowerCamelCase  :		str			    =    {} if index is None else index
          _lowerCamelCase  :		Optional[int]			    =    list(self.state_dict.keys())
          self.all_keys.extend([key for key in self.index if key not in self.all_keys])
          _lowerCamelCase  :		str			    =    device



      def __getitem__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str)					->Tuple:


          """simple docstring"""


          if key in self.state_dict:
              return self.state_dict[key]
          _lowerCamelCase  :		int			    =    self.index[key]
          if weight_info.get("""safetensors_file""") is not None:
              if not is_safetensors_available():
                  raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`.""")

              from safetensors import safe_open

              _lowerCamelCase  :		Any			    =    """cpu""" if self.device is None else self.device
              with safe_open(weight_info["""safetensors_file"""]     ,	framework="""pt"""     ,	device=_UpperCamelCase) as f:
                  _lowerCamelCase  :		Tuple			    =    f.get_tensor(weight_info.get("""weight_name"""     ,	_UpperCamelCase))

              if "dtype" in weight_info:
                  return tensor.to(getattr(_UpperCamelCase     ,	weight_info["""dtype"""]))
              else:
                  return tensor

          _lowerCamelCase  :		Optional[Any]			    =    os.path.join(self.save_folder     ,	F"""{key}.dat""")
          return load_offloaded_weight(_UpperCamelCase     ,	_UpperCamelCase)



      def __iter__(       self  :					Dict)					->List[Any]:


          """simple docstring"""


          return iter(self.all_keys)




      def __len__(       self  :					int)					->Any:


          """simple docstring"""


          return len(self.all_keys)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		str			    =    {}
    for module_name in submodule_names:
        # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
        # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
        result.update(
            {
                key: param
                for key, param in state_dict.items()
                if key == module_name or key.startswith(module_name + """."""						)
            }						)
    return result




