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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=1_00			,					lowerCamelCase__=" "       )					->Tuple:

  '''simple docstring'''




  a__  		=  text.split(lowerCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0			,					len(lowerCamelCase__       )			,					lowerCamelCase__       )]
def 					__lowercase						(    lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




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

  '''simple docstring'''




  a__  		=  load_dataset(
      "csv"			,					data_files=[csv_path]			,					split="train"			,					delimiter="\t"			,					column_names=["title", "text"]       )
  a__  		=  kb_dataset.map(
      lowerCamelCase__			,					batched=lowerCamelCase__			,					num_proc=1       )  # if you want you can load already splitted csv.
  a__  		=  [kb_dataset.shard(lowerCamelCase__			,					lowerCamelCase__			,					contiguous=lowerCamelCase__       ) for i in range(lowerCamelCase__       )]
  a__  		=  kb_list[process_num]

  a__  		=  "data_" + str(lowerCamelCase__       )
  a__  		=  os.path.join(lowerCamelCase__			,					lowerCamelCase__       )

  a__  		=  DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"       )
  a__  		=  ctx_encoder.to(device=lowerCamelCase__       )

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

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

  a__  		=  data_shrad.map(
      partial(lowerCamelCase__			,					ctx_encoder=lowerCamelCase__			,					ctx_tokenizer=lowerCamelCase__			,					device=lowerCamelCase__       )			,					batched=lowerCamelCase__			,					batch_size=16			,					features=lowerCamelCase__			,					)
  dataset.save_to_disk(lowerCamelCase__       )







def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  a__  		=  []

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

  a__  		=  concatenate_datasets(lowerCamelCase__       )
  faiss.omp_set_num_threads(96       )

  a__  		=  faiss.IndexHNSWFlat(7_68			,					1_28			,					faiss.METRIC_INNER_PRODUCT       )
  concat.add_faiss_index("embeddings"			,					custom_index=lowerCamelCase__       )
  concat.get_index("embeddings"       ).save(
      lowerCamelCase__       )  # since we load the index in to memory,we can directly update the index in the disk


import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_SCREAMING_SNAKE_CASE     =     get_logger(__name__)
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  if text.startswith(lowerCamelCase__       ):
    return text[len(lowerCamelCase__       ) :]
  return text  # or whatever
def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  return {remove_prefix(lowerCamelCase__			,					"model."       ): v for k, v in sd.items()}
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  a__  		=  {}
  for k in state_dicts[0].keys():
    a__  		=  [sd[k] for sd in state_dicts]
    a__  		=  sum(lowerCamelCase__       ) / len(lowerCamelCase__       )
    assert isinstance(lowerCamelCase__			,					torch.Tensor       )
    a__  		=  new_t
  return new_sd
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->None:

  '''simple docstring'''




  a__  		=  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )
  if os.path.isfile(lowerCamelCase__       ):
    a__  		=  [pl_ckpt_path]
  else:
    assert os.path.isdir(lowerCamelCase__       )
    a__  		=  list(Path(lowerCamelCase__       ).glob("*.ckpt"       )       )
    assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

  if len(lowerCamelCase__       ) > 1:
    logger.info(f"averaging the weights of {ckpt_files}"       )

  a__  		=  [sanitize(torch.load(lowerCamelCase__			,					map_location="cpu"       )["state_dict"]       ) for x in ckpt_files]
  a__  		=  average_state_dicts(lowerCamelCase__       )

  a__		,a__  		=  hf_model.load_state_dict(lowerCamelCase__			,					strict=lowerCamelCase__       )
  assert not missing, f"missing keys: {missing}"
  hf_model.save_pretrained(lowerCamelCase__       )
  try:
    a__  		=  AutoTokenizer.from_pretrained(lowerCamelCase__       )
    tok.save_pretrained(lowerCamelCase__       )
  except Exception:
    pass
    # dont copy tokenizer if cant


if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)

