




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,     UpperCAmelCase=100,     UpperCAmelCase=" "	)  ->Union[str, Any]:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Optional[int]:






					"""simple docstring"""






					__magic_name__       ,			__magic_name__ :							Union[str, Any]		       =							[], []
					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,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






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

					__magic_name__ :							Any		       =							'''data_''' + str(UpperCAmelCase	)
					__magic_name__ :							List[Any]		       =							os.path.join(UpperCAmelCase,     UpperCAmelCase	)

					__magic_name__ :							str		       =							DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''	)
					__magic_name__ :							Dict		       =							ctx_encoder.to(device=UpperCAmelCase	)

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

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

					__magic_name__ :							str		       =							data_shrad.map(
					    partial(UpperCAmelCase,     ctx_encoder=UpperCAmelCase,     ctx_tokenizer=UpperCAmelCase,     device=UpperCAmelCase	),     batched=UpperCAmelCase,     batch_size=16,     features=UpperCAmelCase,     )
					dataset.save_to_disk(UpperCAmelCase	)







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Optional[Any]:






					"""simple docstring"""






					__magic_name__ :							List[str]		       =							[]

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

					__magic_name__ :							Optional[Any]		       =							concatenate_datasets(UpperCAmelCase	)
					faiss.omp_set_num_threads(96	)

					__magic_name__ :							Tuple		       =							faiss.IndexHNSWFlat(768,     128,     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









import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase_		=							get_logger(__name__)

# Datasets
lowercase_		=							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
lowercase_		=							'''https://cdn-datasets.huggingface.co/datasets/datasets'''
lowercase_		=							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''

# Metrics
lowercase_		=							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
lowercase_		=							'''https://cdn-datasets.huggingface.co/datasets/metric'''
lowercase_		=							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''

# Hub
lowercase_		=							os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
lowercase_		=							HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowercase_		=							'''main'''

lowercase_		=							version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
lowercase_		=							{'''1''', '''ON''', '''YES''', '''TRUE'''}
lowercase_		=							ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
lowercase_		=							version.parse(importlib_metadata.version('''dill'''))
lowercase_		=							version.parse(importlib_metadata.version('''pandas'''))
lowercase_		=							version.parse(importlib_metadata.version('''pyarrow'''))

lowercase_		=							os.environ.get('''USE_TF''', '''AUTO''').upper()
lowercase_		=							os.environ.get('''USE_TORCH''', '''AUTO''').upper()
lowercase_		=							os.environ.get('''USE_JAX''', '''AUTO''').upper()

lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
				lowercase_		=							importlib.util.find_spec('''torch''') is not None
				if TORCH_AVAILABLE:
								try:
												lowercase_		=							version.parse(importlib_metadata.version('''torch'''))
												logger.info(f"PyTorch version {TORCH_VERSION} available.")
								except importlib_metadata.PackageNotFoundError:
												pass
else:
				logger.info('''Disabling PyTorch because USE_TF is set''')

lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
				lowercase_		=							importlib.util.find_spec('''tensorflow''') is not None
				if TF_AVAILABLE:
								# For the metadata, we have to look for both tensorflow and tensorflow-cpu
								for package in [
								    "tensorflow",
								    "tensorflow-cpu",
								    "tensorflow-gpu",
								    "tf-nightly",
								    "tf-nightly-cpu",
								    "tf-nightly-gpu",
								    "intel-tensorflow",
								    "tensorflow-rocm",
								    "tensorflow-macos",
								]:
												try:
																lowercase_		=							version.parse(importlib_metadata.version(package))
												except importlib_metadata.PackageNotFoundError:
																continue
												else:
																break
								else:
												lowercase_		=							False
				if TF_AVAILABLE:
								if TF_VERSION.major < 2:
												logger.info(f"TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.")
												lowercase_		=							False
								else:
												logger.info(f"TensorFlow version {TF_VERSION} available.")
else:
				logger.info('''Disabling Tensorflow because USE_TORCH is set''')


lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
				lowercase_		=							importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
				if JAX_AVAILABLE:
								try:
												lowercase_		=							version.parse(importlib_metadata.version('''jax'''))
												logger.info(f"JAX version {JAX_VERSION} available.")
								except importlib_metadata.PackageNotFoundError:
												pass
else:
				logger.info('''Disabling JAX because USE_JAX is set to False''')


lowercase_		=							os.environ.get('''USE_BEAM''', '''AUTO''').upper()
lowercase_		=							'''N/A'''
lowercase_		=							False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
				try:
								lowercase_		=							version.parse(importlib_metadata.version('''apache_beam'''))
								lowercase_		=							True
								logger.info(f"Apache Beam version {BEAM_VERSION} available.")
				except importlib_metadata.PackageNotFoundError:
								pass
else:
				logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
lowercase_		=							importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
lowercase_		=							importlib.util.find_spec('''PIL''') is not None
lowercase_		=							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
lowercase_		=							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
lowercase_		=							importlib.util.find_spec('''rarfile''') is not None
lowercase_		=							importlib.util.find_spec('''zstandard''') is not None
lowercase_		=							importlib.util.find_spec('''lz4''') is not None
lowercase_		=							importlib.util.find_spec('''py7zr''') is not None


# Cache location
lowercase_		=							'''~/.cache'''
lowercase_		=							os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
lowercase_		=							os.path.join(XDG_CACHE_HOME, '''huggingface''')
lowercase_		=							os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''datasets''')
lowercase_		=							Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''metrics''')
lowercase_		=							Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''modules''')
lowercase_		=							Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

lowercase_		=							'''downloads'''
lowercase_		=							os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowercase_		=							Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

lowercase_		=							'''extracted'''
lowercase_		=							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowercase_		=							Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowercase_		=							(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
lowercase_		=							1000

# Size of the preloaded record batch in `Dataset.__iter__`
lowercase_		=							10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
lowercase_		=							4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
lowercase_		=							'''500MB'''

# Parquet configuration
lowercase_		=							100
lowercase_		=							100
lowercase_		=							100

# Offline mode
lowercase_		=							os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
lowercase_		=							0  # Disabled
lowercase_		=							float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
lowercase_		=							'''dataset.arrow'''
lowercase_		=							'''indices.arrow'''
lowercase_		=							'''state.json'''
lowercase_		=							'''dataset_info.json'''
lowercase_		=							'''dataset_infos.json'''
lowercase_		=							'''LICENSE'''
lowercase_		=							'''metric_info.json'''
lowercase_		=							'''dataset_dict.json'''

lowercase_		=							'''datasets_modules'''

lowercase_		=							255

# Streaming
lowercase_		=							20
lowercase_		=							5

# Datasets without script
lowercase_		=							200
lowercase_		=							10
lowercase_		=							200

# Progress bars
lowercase_		=							0.0_5  # 20 progress updates per sec



