

'''simple docstring'''







import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_lowerCamelCase    =  get_logger(__name__)

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

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

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

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


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

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

_lowerCamelCase    =  'N/A'
_lowerCamelCase    =  False

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

_lowerCamelCase    =  'N/A'
_lowerCamelCase    =  False

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


_lowerCamelCase    =  'N/A'
_lowerCamelCase    =  False

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


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

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


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

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

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

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

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

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

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

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

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

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

# Parquet configuration
_lowerCamelCase    =  1_0_0
_lowerCamelCase    =  1_0_0
_lowerCamelCase    =  1_0_0

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

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

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

_lowerCamelCase    =  'datasets_modules'

_lowerCamelCase    =  2_5_5

# Streaming
_lowerCamelCase    =  2_0
_lowerCamelCase    =  5

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

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







'''simple docstring'''







from collections import namedtuple

_lowerCamelCase    =  namedtuple('from_to', 'from_ to')

_lowerCamelCase    =  {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

_lowerCamelCase    =  {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     from_type.lower().rstrip("""s"""	)
	_lowercase							=     TYPE_CONVERSION.get(snake_case_							,       snake_case_	)
	_lowercase							=     to_type.lower().rstrip("""s"""	)
	_lowercase							=     TYPE_CONVERSION.get(snake_case_							,       snake_case_	)
	if new_from not in METRIC_CONVERSION:
		_lowercase							=     (
		    F"""Invalid 'from_type' value: {from_type!r}.\n"""
		    F"""Conversion abbreviations are: {", ".join(snake_case_	)}"""
		)
		raise ValueError(snake_case_	)
	if new_to not in METRIC_CONVERSION:
		_lowercase							=     (
		    F"""Invalid 'to_type' value: {to_type!r}.\n"""
		    F"""Conversion abbreviations are: {", ".join(snake_case_	)}"""
		)
		raise ValueError(snake_case_	)
	return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
							import doctest

							doctest.testmod()




