



"""simple docstring"""






import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__UpperCAmelCase      :      Optional[int]   					=					get_logger(__name__)

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

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

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


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

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

__UpperCAmelCase      :      str   					=					'N/A'
__UpperCAmelCase      :      str   					=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
				__UpperCAmelCase      :      List[Any]   					=					importlib.util.find_spec('torch') is not None
				if TORCH_AVAILABLE:
								try:
												__UpperCAmelCase      :      Optional[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      :      int   					=					'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      :      Tuple   					=					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[str]   					=					version.parse(importlib_metadata.version(package))
												except importlib_metadata.PackageNotFoundError:
																continue
												else:
																break
								else:
												__UpperCAmelCase      :      Union[str, 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      :      Optional[Any]   					=					False
								else:
												logger.info(F'''TensorFlow version {TF_VERSION} available.''')
else:
				logger.info('Disabling Tensorflow because USE_TORCH is set')


__UpperCAmelCase      :      str   					=					'N/A'
__UpperCAmelCase      :      Tuple   					=					False

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

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


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

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

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

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

__UpperCAmelCase      :      str   					=					'downloads'
__UpperCAmelCase      :      Any   					=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
__UpperCAmelCase      :      str   					=					Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

__UpperCAmelCase      :      List[Any]   					=					'extracted'
__UpperCAmelCase      :      Dict   					=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
__UpperCAmelCase      :      Union[str, Any]   					=					Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
__UpperCAmelCase      :      Optional[int]   					=					(
    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      :      Any   					=					10_00

# Size of the preloaded record batch in `Dataset.__iter__`
__UpperCAmelCase      :      str   					=					10

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

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

# Parquet configuration
__UpperCAmelCase      :      Optional[int]   					=					1_00
__UpperCAmelCase      :      Union[str, Any]   					=					1_00
__UpperCAmelCase      :      Optional[int]   					=					1_00

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

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

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

__UpperCAmelCase      :      List[str]   					=					'datasets_modules'

__UpperCAmelCase      :      str   					=					2_55

# Streaming
__UpperCAmelCase      :      str   					=					20
__UpperCAmelCase      :      str   					=					5

# Datasets without script
__UpperCAmelCase      :      str   					=					2_00
__UpperCAmelCase      :      Any   					=					10
__UpperCAmelCase      :      Optional[Any]   					=					2_00

# Progress bars
__UpperCAmelCase      :      List[Any]   					=					0.05  # 20 progress updates per sec






"""simple docstring"""






def      A      (			_A,     _A = 1_000							):




					"""simple docstring"""
					if (not isinstance(_A,     _A							)) or (digit_position <= 0):
										raise ValueError("Digit position must be a positive integer"							)
					elif (not isinstance(_A,     _A							)) or (precision < 0):
										raise ValueError("Precision must be a nonnegative integer"							)

					# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
					# accurate
					snake_case_						:List[Any]								=						(
					    4 * _subsum(_A,     1,     _A							)
					    - 2 * _subsum(_A,     4,     _A							)
					    - _subsum(_A,     5,     _A							)
					    - _subsum(_A,     6,     _A							)
					)

					# return the first hex digit of the fractional part of the result
					return hex(int((sum_result % 1) * 16							)							)[2:]
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:int								=						0.0
					for sum_index in range(digit_pos_to_extract + precision							):
										snake_case_						:Optional[Any]								=						8 * sum_index + denominator_addend
										if sum_index < digit_pos_to_extract:
															# if the exponential term is an integer and we mod it by the denominator
															# before dividing, only the integer part of the sum will change;
															# the fractional part will not
															snake_case_						:List[Any]								=						pow(
															    16,     digit_pos_to_extract - 1 - sum_index,     _A							)
										else:
															snake_case_						:Optional[Any]								=						pow(16,     digit_pos_to_extract - 1 - sum_index							)
										total += exponential_term / denominator
					return total


if __name__ == "__main__":
				import doctest

				doctest.testmod()

