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 		__lowerCamelCase							(UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							Optional[Any]=1_0_0					,       UpperCAmelCase__   :							Union[str, Any]=" "     ):
					SCREAMING_SNAKE_CASE				=				text.split(UpperCAmelCase__     )
					return [character.join(text[i : i + n]     ).strip() for i in range(0					,       len(UpperCAmelCase__     )					,       UpperCAmelCase__     )]





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				[], []
					for title, text in zip(documents["title"]					,       documents["text"]     ):
										if text is not None:
															for passage in split_text(UpperCAmelCase__     ):
																				titles.append(title if title is not None else ""     )
																				texts.append(UpperCAmelCase__     )
					return {"title": titles, "text": texts}





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

					SCREAMING_SNAKE_CASE				=				"data_" + str(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				os.path.join(UpperCAmelCase__					,       UpperCAmelCase__     )

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

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

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

					SCREAMING_SNAKE_CASE				=				data_shrad.map(
					    partial(UpperCAmelCase__					,       ctx_encoder=UpperCAmelCase__					,       ctx_tokenizer=UpperCAmelCase__					,       device=UpperCAmelCase__     )					,       batched=UpperCAmelCase__					,       batch_size=1_6					,       features=UpperCAmelCase__					,       )
					dataset.save_to_disk(UpperCAmelCase__     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							Optional[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]

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

					SCREAMING_SNAKE_CASE				=				concatenate_datasets(UpperCAmelCase__     )
					faiss.omp_set_num_threads(9_6     )

					SCREAMING_SNAKE_CASE				=				faiss.IndexHNSWFlat(7_6_8					,       1_2_8					,       faiss.METRIC_INNER_PRODUCT     )
					concat.add_faiss_index("embeddings"					,       custom_index=UpperCAmelCase__     )
					concat.get_index("embeddings"     ).save(
					    UpperCAmelCase__     )  # since we load the index in to memory,we can directly update the index in the disk












"""simple docstring"""







import importlib.metadata
import subprocess
import sys





def 						lowercase__(A = False						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      Any=       False
						if "IPython" in sys.modules:
												lowercase__				:      Tuple=       "google.colab" in str(sys.modules["IPython"].get_ipython()						)

						if in_colab:
												if upgrade:
																		lowercase__				:      Optional[int]=       ["pip", "install", "-U", "torch"]
																		subprocess.run(A					,		check=A						)
												# get the current version of torch
												lowercase__				:      Tuple=       importlib.metadata.version("torch"						)
												lowercase__				:      Dict=       torch_version[: torch_version.rindex("."						)]
												lowercase__				:      Dict=       f'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
												lowercase__				:      Union[str, Any]=       ["pip", "install", xla_wheel]
												subprocess.run(A					,		check=A						)
						else:
												raise RuntimeError("`install_xla` utility works only on google colab."						)






