





from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
lowercase_		=							20

# Time between time steps in seconds
lowercase_		=							INTERVAL / 1000





class 		A__    :






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 1.0    ,					lowerCamelCase = 1.0    ,					lowerCamelCase = "blue"    ,					)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							position_x
						__magic_name__ :							Tuple		       =							position_y
						__magic_name__ :							Optional[int]		       =							velocity_x
						__magic_name__ :							Tuple		       =							velocity_y
						__magic_name__ :							List[str]		       =							mass
						__magic_name__ :							int		       =							size
						__magic_name__ :							List[str]		       =							color






	@property
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return self.position_x, self.position_y






	@property
	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						return self.velocity_x, self.velocity_y






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Union[str, Any]:




						"""simple docstring"""






						self.velocity_x += force_x * delta_time
						self.velocity_y += force_y * delta_time






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       int:




						"""simple docstring"""






						self.position_x += self.velocity_x * delta_time
						self.position_y += self.velocity_y * delta_time





class 		A__    :






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase = 1.0    ,					lowerCamelCase = 1.0    ,					lowerCamelCase = 0.0    ,					)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							str		       =							bodies
						__magic_name__ :							List[str]		       =							gravitation_constant
						__magic_name__ :							Optional[int]		       =							time_factor
						__magic_name__ :							List[Any]		       =							softening_factor






	def __len__(     self						)				->       Optional[Any]:




						"""simple docstring"""






						return len(self.bodies						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						for bodya in self.bodies:
											__magic_name__ :							List[str]		       =							0.0
											__magic_name__ :							List[str]		       =							0.0
											for bodya in self.bodies:
																if bodya != bodya:
																					__magic_name__ :							Any		       =							bodya.position_x - bodya.position_x
																					__magic_name__ :							Union[str, Any]		       =							bodya.position_y - bodya.position_y

																					# Calculation of the distance using Pythagoras's theorem
																					# Extra factor due to the softening technique
																					__magic_name__ :							str		       =							(dif_x**2 + dif_y**2 + self.softening_factor) ** (
																					    1 / 2
																					)

																					# Newton's law of universal gravitation.
																					force_x += (
																					    self.gravitation_constant * bodya.mass * dif_x / distance**3
																					)
																					force_y += (
																					    self.gravitation_constant * bodya.mass * dif_y / distance**3
																					)

            # Update the body's velocity once all the force components have been added
											bodya.update_velocity(lowerCamelCase    ,					lowerCamelCase    ,					delta_time * self.time_factor						)

						# Update the positions only after all the velocities have been updated
						for body in self.bodies:
											body.update_position(delta_time * self.time_factor						)







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->None:






					"""simple docstring"""






					body_system.update_system(UpperCAmelCase	)

					# Update the positions of the patches
					for patch, body in zip(UpperCAmelCase,     body_system.bodies	):
										__magic_name__ :							Union[str, Any]		       =							(body.position_x, body.position_y)







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase = -1,     UpperCAmelCase = 1,     UpperCAmelCase = -1,     UpperCAmelCase = 1,     )  ->None:






					"""simple docstring"""






					__magic_name__ :							List[str]		       =							plt.figure()
					fig.canvas.set_window_title(UpperCAmelCase	)
					__magic_name__ :							Optional[Any]		       =							plt.axes(
					    xlim=(x_start, x_end),     ylim=(y_start, y_end)	)  # Set section to be plotted
					plt.gca().set_aspect('''equal'''	)  # Fix aspect ratio

					# Each body is drawn as a patch by the plt-function
					__magic_name__ :							List[Any]		       =							[
					    plt.Circle((body.position_x, body.position_y),     body.size,     fc=body.color	)
					    for body in body_system.bodies
					]

					for patch in patches:
										ax.add_patch(UpperCAmelCase	)

					# Function called at each step of the animation
					def update(UpperCAmelCase	) -> list[plt.Circle]:
										update_step(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)
										return patches

					__magic_name__ :							int		       =							animation.FuncAnimation(  # noqa: F841
					    UpperCAmelCase,     UpperCAmelCase,     interval=UpperCAmelCase,     blit=UpperCAmelCase	)

					plt.show()







def 			lowerCAmelCase  (     )  ->BodySystem:






					"""simple docstring"""






					__magic_name__ :							Any		       =							0.9_70_04_36
					__magic_name__ :							Optional[Any]		       =							-0.24_30_87_53
					__magic_name__ :							List[str]		       =							0.4_66_20_36_85
					__magic_name__ :							Union[str, Any]		       =							0.43_23_65_73

					__magic_name__ :							int		       =							[
					    Body(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     size=0.2,     color='''red'''	),
					    Body(-position_x,     -position_y,     UpperCAmelCase,     UpperCAmelCase,     size=0.2,     color='''green'''	),
					    Body(0,     0,     -2 * velocity_x,     -2 * velocity_y,     size=0.2,     color='''blue'''	),
					]
					return BodySystem(UpperCAmelCase,     time_factor=3	)







def 			lowerCAmelCase  (     )  ->BodySystem:






					"""simple docstring"""






					__magic_name__ :							List[Any]		       =							7.34_76E22
					__magic_name__ :							Optional[Any]		       =							5.9_72E24
					__magic_name__ :							Union[str, Any]		       =							1022
					__magic_name__ :							int		       =							3_8439_9000
					__magic_name__ :							Dict		       =							6.6_74E-11

					# Calculation of the respective velocities so that total impulse is zero,
					# i.e. the two bodies together don't move
					__magic_name__ :							Dict		       =							earth_mass * velocity_dif / (earth_mass + moon_mass)
					__magic_name__ :							Optional[Any]		       =							moon_velocity - velocity_dif

					__magic_name__ :							List[str]		       =							Body(-earth_moon_distance,     0,     0,     UpperCAmelCase,     UpperCAmelCase,     1000_0000,     '''grey'''	)
					__magic_name__ :							List[Any]		       =							Body(0,     0,     0,     UpperCAmelCase,     UpperCAmelCase,     5000_0000,     '''blue'''	)
					return BodySystem([earth, moon],     UpperCAmelCase,     time_factor=100_0000	)







def 			lowerCAmelCase  (     )  ->BodySystem:






					"""simple docstring"""






					__magic_name__ :							Optional[Any]		       =							[]
					for _ in range(10	):
										__magic_name__ :							str		       =							random.uniform(-0.5,     0.5	)
										__magic_name__ :							Optional[int]		       =							random.uniform(-0.5,     0.5	)

										# Bodies are created pairwise with opposite velocities so that the
										# total impulse remains zero
										bodies.append(
										    Body(
										        random.uniform(-0.5,     0.5	),     random.uniform(-0.5,     0.5	),     UpperCAmelCase,     UpperCAmelCase,     size=0.05,     )	)
										bodies.append(
										    Body(
										        random.uniform(-0.5,     0.5	),     random.uniform(-0.5,     0.5	),     -velocity_x,     -velocity_y,     size=0.05,     )	)
					return BodySystem(UpperCAmelCase,     0.01,     10,     0.1	)


if __name__ == "__main__":
				plot('''Figure-8 solution to the 3-body-problem''', example_a(), -2, 2, -2, 2)
				plot(
				    '''Moon\'s orbit around the earth''',
				    example_a(),
				    -430000000,
				    430000000,
				    -430000000,
				    430000000,
				)
				plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)









import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase_		=							get_logger(__name__)

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

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

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

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


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

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

lowercase_		=							'''N/A'''
lowercase_		=							False

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

lowercase_		=							'''N/A'''
lowercase_		=							False

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


lowercase_		=							'''N/A'''
lowercase_		=							False

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


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

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


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

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

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

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

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

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

# Download count for the website
lowercase_		=							(
    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)
lowercase_		=							1000

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

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

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

# Parquet configuration
lowercase_		=							100
lowercase_		=							100
lowercase_		=							100

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

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

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

lowercase_		=							'''datasets_modules'''

lowercase_		=							255

# Streaming
lowercase_		=							20
lowercase_		=							5

# Datasets without script
lowercase_		=							200
lowercase_		=							10
lowercase_		=							200

# Progress bars
lowercase_		=							0.0_5  # 20 progress updates per sec



