


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__magic_name__       					=      get_logger(__name__)

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

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

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

__magic_name__       					=      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
__magic_name__       					=      {'''1''', '''ON''', '''YES''', '''TRUE'''}
__magic_name__       					=      ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


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

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

__magic_name__       					=      '''N/A'''
__magic_name__       					=      False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
							__magic_name__       					=      importlib.util.find_spec('''torch''') is not None
							if TORCH_AVAILABLE:
														try:
																					__magic_name__       					=      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''')

__magic_name__       					=      '''N/A'''
__magic_name__       					=      False

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


__magic_name__       					=      '''N/A'''
__magic_name__       					=      False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							__magic_name__       					=      importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
							if JAX_AVAILABLE:
														try:
																					__magic_name__       					=      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''')


__magic_name__       					=      os.environ.get('''USE_BEAM''', '''AUTO''').upper()
__magic_name__       					=      '''N/A'''
__magic_name__       					=      False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														__magic_name__       					=      version.parse(importlib_metadata.version('''apache_beam'''))
														__magic_name__       					=      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
__magic_name__       					=      importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
__magic_name__       					=      importlib.util.find_spec('''PIL''') is not None
__magic_name__       					=      importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
__magic_name__       					=      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
__magic_name__       					=      importlib.util.find_spec('''rarfile''') is not None
__magic_name__       					=      importlib.util.find_spec('''zstandard''') is not None
__magic_name__       					=      importlib.util.find_spec('''lz4''') is not None
__magic_name__       					=      importlib.util.find_spec('''py7zr''') is not None


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

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

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

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

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

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

# Download count for the website
__magic_name__       					=      (
    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)
__magic_name__       					=      1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
__magic_name__       					=      1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
__magic_name__       					=      4 << 3_0

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

# Parquet configuration
__magic_name__       					=      1_0_0
__magic_name__       					=      1_0_0
__magic_name__       					=      1_0_0

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

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

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

__magic_name__       					=      '''datasets_modules'''

__magic_name__       					=      2_5_5

# Streaming
__magic_name__       					=      2_0
__magic_name__       					=      5

# Datasets without script
__magic_name__       					=      2_0_0
__magic_name__       					=      1_0
__magic_name__       					=      2_0_0

# Progress bars
__magic_name__       					=      0.05  # 20 progress updates per sec





def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       0
	for ele in arr:
		total_sum += product_sum(lowerCAmelCase_      ,					depth + 1) if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_) else ele
	return total_sum * depth







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return product_sum(lowerCAmelCase_      ,					1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

