





'''simple docstring'''






import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A :			List[str]							   =      get_logger(__name__)

# Datasets
A :			Tuple							   =      'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
A :			str							   =      'https://cdn-datasets.huggingface.co/datasets/datasets'
A :			List[str]							   =      'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

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

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

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


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

A :			Optional[Any]							   =      os.environ.get('USE_TF', 'AUTO').upper()
A :			Dict							   =      os.environ.get('USE_TORCH', 'AUTO').upper()
A :			List[Any]							   =      os.environ.get('USE_JAX', 'AUTO').upper()

A :			Any							   =      'N/A'
A :			Optional[Any]							   =      False

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

A :			int							   =      'N/A'
A :			Any							   =      False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
		A :			Optional[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:
								A :			Union[str, Any]							   =      version.parse(importlib_metadata.version(package))
						except importlib_metadata.PackageNotFoundError:
								continue
						else:
								break
				else:
						A :			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.')
						A :			Tuple							   =      False
				else:
						logger.info(F'TensorFlow version {TF_VERSION} available.')
else:
		logger.info('Disabling Tensorflow because USE_TORCH is set')


A :			int							   =      'N/A'
A :			Tuple							   =      False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
		A :			Optional[int]							   =      importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
		if JAX_AVAILABLE:
				try:
						A :			Union[str, Any]							   =      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')


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

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


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

A :			List[Any]							   =      os.path.join(HF_CACHE_HOME, 'datasets')
A :			Tuple							   =      Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

A :			Union[str, Any]							   =      os.path.join(HF_CACHE_HOME, 'metrics')
A :			Optional[int]							   =      Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

A :			Dict							   =      os.path.join(HF_CACHE_HOME, 'modules')
A :			int							   =      Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

A :			Dict							   =      'downloads'
A :			Optional[Any]							   =      os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A :			List[str]							   =      Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

A :			Optional[int]							   =      'extracted'
A :			Union[str, Any]							   =      os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A :			Optional[Any]							   =      Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A :			Optional[Any]							   =      (
    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)
A :			List[str]							   =      1_000

# Size of the preloaded record batch in `Dataset.__iter__`
A :			Union[str, Any]							   =      10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A :			List[Any]							   =      4 << 30

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

# Parquet configuration
A :			Any							   =      100
A :			int							   =      100
A :			Union[str, Any]							   =      100

# Offline mode
A :			List[str]							   =      os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES

# In-memory
A :			Optional[Any]							   =      0  # Disabled
A :			str							   =      float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

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

A :			int							   =      'datasets_modules'

A :			int							   =      255

# Streaming
A :			Optional[Any]							   =      20
A :			int							   =      5

# Datasets without script
A :			Union[str, Any]							   =      200
A :			Union[str, Any]							   =      10
A :			Union[str, Any]							   =      200

# Progress bars
A :			str							   =      0.05  # 20 progress updates per sec












def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		lowerCamelCase :			int    		=[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		lowerCamelCase :			List[Any]    		=1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(SCREAMING_SNAKE_CASE_				,							n + 1		):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

