






import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


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

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

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

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

A_			:Any	    =		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_			:Optional[int]	    =		{'''1''', '''ON''', '''YES''', '''TRUE'''}
A_			:List[Any]	    =		ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
A_			:List[str]	    =		version.parse(importlib_metadata.version('''dill'''))
A_			:str	    =		version.parse(importlib_metadata.version('''pandas'''))
A_			:Optional[int]	    =		version.parse(importlib_metadata.version('''pyarrow'''))

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

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

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

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

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


A_			:Tuple	    =		'''N/A'''
A_			:List[str]	    =		False

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


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

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


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

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

A_			:Tuple	    =		os.path.join(HF_CACHE_HOME, '''metrics''')
A_			:Optional[int]	    =		Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

A_			:Union[str, Any]	    =		os.path.join(HF_CACHE_HOME, '''modules''')
A_			:Union[str, Any]	    =		Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

A_			:List[str]	    =		'''downloads'''
A_			:Dict	    =		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_			:int	    =		os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A_			:List[str]	    =		Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A_			: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)
A_			:Tuple	    =		1000

# Size of the preloaded record batch in `Dataset.__iter__`
A_			:int	    =		10

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

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

# Parquet configuration
A_			:int	    =		100
A_			:Optional[int]	    =		100
A_			:int	    =		100

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

# In-memory
A_			:List[Any]	    =		0  # Disabled
A_			:Dict	    =		float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

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

A_			:Union[str, Any]	    =		'''datasets_modules'''

A_			:List[str]	    =		255

# Streaming
A_			:Any	    =		20
A_			:Union[str, Any]	    =		5

# Datasets without script
A_			:Tuple	    =		200
A_			:str	    =		10
A_			:List[Any]	    =		200

# Progress bars
A_			:Dict	    =		0.05  # 20 progress updates per sec









import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return (self.x**2 + self.y**2) <= 1







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

									return cls(x=random.random()				,    y=random.random()       )






def 				A	(			a_   )	->		float:
					if number_of_simulations < 1:
										raise ValueError('At least one simulation is necessary to estimate PI.'   )

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=Point.random_unit_square()

										if random_point.is_in_unit_circle():
															number_in_unit_circle += 1

					return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							A_			:Optional[int]	    =		'''Please enter the desired number of Monte Carlo simulations: '''
							A_			:int	    =		estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")

