




'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a__         =     get_logger(__name__)

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

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

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

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


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

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

a__         =     '''N/A'''
a__         =     False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
		a__         =     importlib.util.find_spec('''torch''') is not None
		if TORCH_AVAILABLE:
				try:
						a__         =     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__         =     '''N/A'''
a__         =     False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
		a__         =     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__         =     version.parse(importlib_metadata.version(package))
						except importlib_metadata.PackageNotFoundError:
								continue
						else:
								break
				else:
						a__         =     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__         =     False
				else:
						logger.info(F"TensorFlow version {TF_VERSION} available.")
else:
		logger.info('''Disabling Tensorflow because USE_TORCH is set''')


a__         =     '''N/A'''
a__         =     False

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

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


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

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

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

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

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

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

# Download count for the website
a__         =     (
    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__         =     1_000

# Size of the preloaded record batch in `Dataset.__iter__`
a__         =     10

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

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

# Parquet configuration
a__         =     100
a__         =     100
a__         =     100

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

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

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

a__         =     '''datasets_modules'''

a__         =     255

# Streaming
a__         =     20
a__         =     5

# Datasets without script
a__         =     200
a__         =     10
a__         =     200

# Progress bars
a__         =     0.05  # 20 progress updates per sec
'''simple docstring'''


from itertools import combinations, product


def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if xa == ya == 0 or xa == ya == 0:
          return False
     UpperCAmelCase_				:  Union[str, Any]  = xa * xa + ya * ya
     UpperCAmelCase_				:  str  = xa * xa + ya * ya
     UpperCAmelCase_				:  str  = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
     return (
         a_square + b_square == c_square
         or a_square + c_square == b_square
         or b_square + c_square == a_square
     )


def    _lowerCamelCase       ( lowerCamelCase_   :				int = 50  ):



     """simple docstring"""





     return sum(
         1
         for pta, pta in combinations(product(range(limit + 1  )      ,	repeat=2  )      ,	2  )
         if is_right(*lowerCamelCase_      ,	*lowerCamelCase_  )  )


if __name__ == "__main__":
 print(f'''{solution() = }''')






