



"""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    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Tuple    ,		lowerCamelCase_:       Optional[Any]=1_0_0    ,		lowerCamelCase_:       Tuple=" "   ):
						"""simple docstring"""






						snake_case      : Union[str, Any]							=   text.split(lowerCamelCase_   )
						return [character.join(text[i : i + n]   ).strip() for i in range(0    ,		len(lowerCamelCase_   )    ,		lowerCamelCase_   )]

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[int]   ):
						"""simple docstring"""






						snake_case						, snake_case      : Union[str, Any]							=   [], []
						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    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Any    ,		lowerCamelCase_:       List[str]    ,		lowerCamelCase_:       Tuple    ,		lowerCamelCase_:       List[Any]    ,		lowerCamelCase_:       Optional[Any]    ,		lowerCamelCase_:       Union[str, Any]   ):
						"""simple docstring"""






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

						snake_case      : str							=   "data_" + str(lowerCamelCase_   )
						snake_case      : Optional[int]							=   os.path.join(lowerCamelCase_    ,		lowerCamelCase_   )

						snake_case      : Optional[Any]							=   DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"   )
						snake_case      : str							=   ctx_encoder.to(device=lowerCamelCase_   )

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

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

						snake_case      : Optional[Any]							=   data_shrad.map(
						    partial(lowerCamelCase_    ,		ctx_encoder=lowerCamelCase_    ,		ctx_tokenizer=lowerCamelCase_    ,		device=lowerCamelCase_   )    ,		batched=lowerCamelCase_    ,		batch_size=1_6    ,		features=lowerCamelCase_    ,		)
						dataset.save_to_disk(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str    ,		lowerCamelCase_:       Optional[int]   ):
						"""simple docstring"""






						snake_case      : List[Any]							=   []

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

						snake_case      : str							=   concatenate_datasets(lowerCamelCase_   )
						faiss.omp_set_num_threads(9_6   )

						snake_case      : str							=   faiss.IndexHNSWFlat(7_6_8    ,		1_2_8    ,		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








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A    =	{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'GPTJForCausalLM',
			    'GPTJForQuestionAnswering',
			    'GPTJForSequenceClassification',
			    'GPTJModel',
			    'GPTJPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TFGPTJForCausalLM',
			    'TFGPTJForQuestionAnswering',
			    'TFGPTJForSequenceClassification',
			    'TFGPTJModel',
			    'TFGPTJPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'FlaxGPTJForCausalLM',
			    'FlaxGPTJModel',
			    'FlaxGPTJPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gptj import (
						    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTJForCausalLM,
						    GPTJForQuestionAnswering,
						    GPTJForSequenceClassification,
						    GPTJModel,
						    GPTJPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    TFGPTJPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



