





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  __UpperCamelCase   (     a,  a=100,  a=" ")			->str:
					lowerCamelCase__      =							text.split(__SCREAMING_SNAKE_CASE)
					return [character.join(text[i : i + n]).strip() for i in range(0,  len(__SCREAMING_SNAKE_CASE),  __SCREAMING_SNAKE_CASE)]





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





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

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

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

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

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

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







def  __UpperCamelCase   (     a,  a)			->int:
					lowerCamelCase__      =							[]

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

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

					lowerCamelCase__      =							faiss.IndexHNSWFlat(768,  128,  faiss.METRIC_INNER_PRODUCT)
					concat.add_faiss_index("embeddings",  custom_index=__SCREAMING_SNAKE_CASE)
					concat.get_index("embeddings").save(
					    __SCREAMING_SNAKE_CASE)  # since we load the index in to memory,we can directly update the index in the disk



'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



