




import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_UpperCAmelCase						:    List[Any]      =  get_logger(__name__)

# Datasets
_UpperCAmelCase						:    str      =  "https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
_UpperCAmelCase						:    Any      =  "https://cdn-datasets.huggingface.co/datasets/datasets"
_UpperCAmelCase						:    int      =  "https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
_UpperCAmelCase						:    List[Any]      =  "https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
_UpperCAmelCase						:    Optional[Any]      =  "https://cdn-datasets.huggingface.co/datasets/metric"
_UpperCAmelCase						:    Union[str, Any]      =  "https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
_UpperCAmelCase						:    str      =  os.environ.get("HF_ENDPOINT", "https://huggingface.co")
_UpperCAmelCase						:    Union[str, Any]      =  HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
_UpperCAmelCase						:    Optional[int]      =  "main"

_UpperCAmelCase						:    Union[str, Any]      =  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
_UpperCAmelCase						:    str      =  {"1", "ON", "YES", "TRUE"}
_UpperCAmelCase						:    List[Any]      =  ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
_UpperCAmelCase						:    Optional[Any]      =  version.parse(importlib_metadata.version("dill"))
_UpperCAmelCase						:    List[str]      =  version.parse(importlib_metadata.version("pandas"))
_UpperCAmelCase						:    int      =  version.parse(importlib_metadata.version("pyarrow"))

_UpperCAmelCase						:    Union[str, Any]      =  os.environ.get("USE_TF", "AUTO").upper()
_UpperCAmelCase						:    Union[str, Any]      =  os.environ.get("USE_TORCH", "AUTO").upper()
_UpperCAmelCase						:    Tuple      =  os.environ.get("USE_JAX", "AUTO").upper()

_UpperCAmelCase						:    Union[str, Any]      =  "N/A"
_UpperCAmelCase						:    Any      =  False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
     _UpperCAmelCase						:    Union[str, Any]      =  importlib.util.find_spec("torch") is not None
     if TORCH_AVAILABLE:
          try:
               _UpperCAmelCase						:    List[str]      =  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")

_UpperCAmelCase						:    Any      =  "N/A"
_UpperCAmelCase						:    List[Any]      =  False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
     _UpperCAmelCase						:    Union[str, Any]      =  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:
                    _UpperCAmelCase						:    Optional[int]      =  version.parse(importlib_metadata.version(package))
               except importlib_metadata.PackageNotFoundError:
                    continue
               else:
                    break
          else:
               _UpperCAmelCase						:    Optional[int]      =  False
     if TF_AVAILABLE:
          if TF_VERSION.major < 2:
               logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
               _UpperCAmelCase						:    Union[str, Any]      =  False
          else:
               logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
     logger.info("Disabling Tensorflow because USE_TORCH is set")


_UpperCAmelCase						:    int      =  "N/A"
_UpperCAmelCase						:    Any      =  False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
     _UpperCAmelCase						:    Optional[int]      =  importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
     if JAX_AVAILABLE:
          try:
               _UpperCAmelCase						:    List[str]      =  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")


_UpperCAmelCase						:    int      =  os.environ.get("USE_BEAM", "AUTO").upper()
_UpperCAmelCase						:    Any      =  "N/A"
_UpperCAmelCase						:    List[str]      =  False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
     try:
          _UpperCAmelCase						:    Any      =  version.parse(importlib_metadata.version("apache_beam"))
          _UpperCAmelCase						:    Dict      =  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
_UpperCAmelCase						:    Union[str, Any]      =  importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
_UpperCAmelCase						:    Optional[int]      =  importlib.util.find_spec("PIL") is not None
_UpperCAmelCase						:    int      =  importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
_UpperCAmelCase						:    Optional[int]      =  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
_UpperCAmelCase						:    Optional[int]      =  importlib.util.find_spec("rarfile") is not None
_UpperCAmelCase						:    str      =  importlib.util.find_spec("zstandard") is not None
_UpperCAmelCase						:    Tuple      =  importlib.util.find_spec("lz4") is not None
_UpperCAmelCase						:    Union[str, Any]      =  importlib.util.find_spec("py7zr") is not None


# Cache location
_UpperCAmelCase						:    Dict      =  "~/.cache"
_UpperCAmelCase						:    Any      =  os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
_UpperCAmelCase						:    Optional[int]      =  os.path.join(XDG_CACHE_HOME, "huggingface")
_UpperCAmelCase						:    int      =  os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

_UpperCAmelCase						:    int      =  os.path.join(HF_CACHE_HOME, "datasets")
_UpperCAmelCase						:    Union[str, Any]      =  Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

_UpperCAmelCase						:    Any      =  os.path.join(HF_CACHE_HOME, "metrics")
_UpperCAmelCase						:    Tuple      =  Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

_UpperCAmelCase						:    str      =  os.path.join(HF_CACHE_HOME, "modules")
_UpperCAmelCase						:    List[str]      =  Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

_UpperCAmelCase						:    Union[str, Any]      =  "downloads"
_UpperCAmelCase						:    str      =  os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_UpperCAmelCase						:    List[str]      =  Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

_UpperCAmelCase						:    int      =  "extracted"
_UpperCAmelCase						:    Optional[Any]      =  os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_UpperCAmelCase						:    Tuple      =  Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_UpperCAmelCase						:    Tuple      =  (
    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)
_UpperCAmelCase						:    Tuple      =  1000

# Size of the preloaded record batch in `Dataset.__iter__`
_UpperCAmelCase						:    Dict      =  10

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

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_UpperCAmelCase						:    int      =  "500MB"

# Parquet configuration
_UpperCAmelCase						:    str      =  100
_UpperCAmelCase						:    List[Any]      =  100
_UpperCAmelCase						:    str      =  100

# Offline mode
_UpperCAmelCase						:    Dict      =  os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
_UpperCAmelCase						:    Optional[int]      =  0  # Disabled
_UpperCAmelCase						:    Optional[int]      =  float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
_UpperCAmelCase						:    Optional[int]      =  "dataset.arrow"
_UpperCAmelCase						:    Dict      =  "indices.arrow"
_UpperCAmelCase						:    Tuple      =  "state.json"
_UpperCAmelCase						:    Union[str, Any]      =  "dataset_info.json"
_UpperCAmelCase						:    Optional[int]      =  "dataset_infos.json"
_UpperCAmelCase						:    Optional[Any]      =  "LICENSE"
_UpperCAmelCase						:    Optional[Any]      =  "metric_info.json"
_UpperCAmelCase						:    Any      =  "dataset_dict.json"

_UpperCAmelCase						:    Dict      =  "datasets_modules"

_UpperCAmelCase						:    Dict      =  255

# Streaming
_UpperCAmelCase						:    Optional[Any]      =  20
_UpperCAmelCase						:    Dict      =  5

# Datasets without script
_UpperCAmelCase						:    int      =  200
_UpperCAmelCase						:    Dict      =  10
_UpperCAmelCase						:    int      =  200

# Progress bars
_UpperCAmelCase						:    Optional[int]      =  0.0_5  # 20 progress updates per sec









def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[int]   						=    len(lowerCamelCase )
  if n in (0, 1):
    return 0
  return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCamelCase ) ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



