






"""simple docstring"""


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_UpperCamelCase							:       Union[str, Any]       					=		get_logger(__name__)

# Datasets
_UpperCamelCase							:       Optional[Any]       					=		'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
_UpperCamelCase							:       Any       					=		'https://cdn-datasets.huggingface.co/datasets/datasets'
_UpperCamelCase							:       Optional[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							:       Any       					=		'https://cdn-datasets.huggingface.co/datasets/metric'
_UpperCamelCase							:       Optional[Any]       					=		'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

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

_UpperCamelCase							:       Dict       					=		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							:       int       					=		{'1', 'ON', 'YES', 'TRUE'}
_UpperCamelCase							:       int       					=		ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
_UpperCamelCase							:       List[Any]       					=		version.parse(importlib_metadata.version('dill'))
_UpperCamelCase							:       Union[str, Any]       					=		version.parse(importlib_metadata.version('pandas'))
_UpperCamelCase							:       List[Any]       					=		version.parse(importlib_metadata.version('pyarrow'))

_UpperCamelCase							:       List[str]       					=		os.environ.get('USE_TF', 'AUTO').upper()
_UpperCamelCase							:       int       					=		os.environ.get('USE_TORCH', 'AUTO').upper()
_UpperCamelCase							:       Dict       					=		os.environ.get('USE_JAX', 'AUTO').upper()

_UpperCamelCase							:       Union[str, Any]       					=		'N/A'
_UpperCamelCase							:       Optional[int]       					=		False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
   _UpperCamelCase							:       str       					=		importlib.util.find_spec('torch') is not None
   if TORCH_AVAILABLE:
      try:
         _UpperCamelCase							:       List[Any]       					=		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							:       List[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							:       str       					=		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							:       List[Any]       					=		version.parse(importlib_metadata.version(package))
         except importlib_metadata.PackageNotFoundError:
            continue
         else:
            break
      else:
         _UpperCamelCase							:       Optional[Any]       					=		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							:       Any       					=		'N/A'
_UpperCamelCase							:       Union[str, Any]       					=		False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
   _UpperCamelCase							:       List[str]       					=		importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
   if JAX_AVAILABLE:
      try:
         _UpperCamelCase							:       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							:       List[str]       					=		os.environ.get('USE_BEAM', 'AUTO').upper()
_UpperCamelCase							:       Tuple       					=		'N/A'
_UpperCamelCase							:       Union[str, Any]       					=		False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
   try:
      _UpperCamelCase							:       List[Any]       					=		version.parse(importlib_metadata.version('apache_beam'))
      _UpperCamelCase							:       Optional[Any]       					=		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							:       List[str]       					=		importlib.util.find_spec('sqlalchemy') is not None

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


# Cache location
_UpperCamelCase							:       Tuple       					=		'~/.cache'
_UpperCamelCase							:       Union[str, Any]       					=		os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
_UpperCamelCase							:       List[Any]       					=		os.path.join(XDG_CACHE_HOME, 'huggingface')
_UpperCamelCase							:       Union[str, Any]       					=		os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))

_UpperCamelCase							:       Optional[Any]       					=		os.path.join(HF_CACHE_HOME, 'datasets')
_UpperCamelCase							:       Dict       					=		Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

_UpperCamelCase							:       Dict       					=		os.path.join(HF_CACHE_HOME, 'metrics')
_UpperCamelCase							:       str       					=		Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

_UpperCamelCase							:       Optional[int]       					=		os.path.join(HF_CACHE_HOME, 'modules')
_UpperCamelCase							:       List[Any]       					=		Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

_UpperCamelCase							:       Union[str, Any]       					=		'downloads'
_UpperCamelCase							:       Tuple       					=		os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_UpperCamelCase							:       int       					=		Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

_UpperCamelCase							:       Any       					=		'extracted'
_UpperCamelCase							:       int       					=		os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_UpperCamelCase							:       Optional[Any]       					=		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							:       List[Any]       					=		1000

# Size of the preloaded record batch in `Dataset.__iter__`
_UpperCamelCase							:       Optional[int]       					=		10

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

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

# Parquet configuration
_UpperCamelCase							:       Union[str, Any]       					=		100
_UpperCamelCase							:       Dict       					=		100
_UpperCamelCase							:       Optional[Any]       					=		100

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

# In-memory
_UpperCamelCase							:       int       					=		0  # Disabled
_UpperCamelCase							:       Tuple       					=		float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
_UpperCamelCase							:       int       					=		'dataset.arrow'
_UpperCamelCase							:       Dict       					=		'indices.arrow'
_UpperCamelCase							:       int       					=		'state.json'
_UpperCamelCase							:       Optional[Any]       					=		'dataset_info.json'
_UpperCamelCase							:       Optional[Any]       					=		'dataset_infos.json'
_UpperCamelCase							:       Optional[int]       					=		'LICENSE'
_UpperCamelCase							:       Tuple       					=		'metric_info.json'
_UpperCamelCase							:       Dict       					=		'dataset_dict.json'

_UpperCamelCase							:       List[Any]       					=		'datasets_modules'

_UpperCamelCase							:       Union[str, Any]       					=		255

# Streaming
_UpperCamelCase							:       Optional[int]       					=		20
_UpperCamelCase							:       Optional[int]       					=		5

# Datasets without script
_UpperCamelCase							:       Any       					=		200
_UpperCamelCase							:       List[Any]       					=		10
_UpperCamelCase							:       int       					=		200

# Progress bars
_UpperCamelCase							:       Optional[int]       					=		0.05  # 20 progress updates per sec












"""simple docstring"""


from doctest import testmod
from math import sqrt




def      snake_case    (A_ :int ):







  '''simple docstring'''

  a	:     list[int]							=  []
  if num < 1:
    return facs
  facs.append(1 )
  if num == 1:
    return facs
  facs.append(A_ )
  for i in range(2      ,     int(sqrt(A_ ) ) + 1 ):
    if num % i == 0:  # If i is a factor of num
      facs.append(A_ )
      a	:     Optional[Any]							=  num // i  # num//i is the other factor of num
      if d != i:  # If d and i are distinct
        facs.append(A_ )  # we have found another factor
  facs.sort()
  return facs


if __name__ == "__main__":
   testmod(name='factors_of_a_number', verbose=True)




