






import numpy as np
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   int=5,  lowerCamelCase						:   Optional[Any]=5  ):

								'''simple docstring'''
								lowercase__       			=      depth
								lowercase__       			=      0
								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      min_leaf_size
								lowercase__       			=      None




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								if labels.ndim != 1:
												print('''Error: Input labels must be one dimensional'''  )

								return np.mean((labels - prediction) ** 2  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if x.ndim != 1:
												print('''Error: Input data set must be one dimensional'''  )
												return
								if len(lowerCamelCase  ) != len(lowerCamelCase  ):
												print('''Error: X and y have different lengths'''  )
												return
								if y.ndim != 1:
												print('''Error: Data set labels must be one dimensional'''  )
												return

								if len(lowerCamelCase  ) < 2 * self.min_leaf_size:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								if self.depth == 1:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								lowercase__       			=      0
								lowercase__       			=      self.mean_squared_error(lowerCamelCase,  np.mean(lowerCamelCase  )  ) * 2
								for i in range(len(lowerCamelCase  )  ):
												if len(x[:i]  ) < self.min_leaf_size:
																continue
												elif len(x[i:]  ) < self.min_leaf_size:
																continue
												else:
																lowercase__       			=      self.mean_squared_error(x[:i],  np.mean(y[:i]  )  )
																lowercase__       			=      self.mean_squared_error(x[i:],  np.mean(y[i:]  )  )
																lowercase__       			=      error_left + error_right
																if error < min_error:
																				lowercase__       			=      i
																				lowercase__       			=      error

								if best_split != 0:
												lowercase__       			=      x[:best_split]
												lowercase__       			=      y[:best_split]
												lowercase__       			=      x[best_split:]
												lowercase__       			=      y[best_split:]

												lowercase__       			=      x[best_split]
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												self.left.train(lowerCamelCase,  lowerCamelCase  )
												self.right.train(lowerCamelCase,  lowerCamelCase  )
								else:
												lowercase__       			=      np.mean(lowerCamelCase  )

								return




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if self.prediction is not None:
												return self.prediction
								elif self.left or self.right is not None:
												if x >= self.decision_boundary:
																return self.right.predict(lowerCamelCase  )
												else:
																return self.left.predict(lowerCamelCase  )
								else:
												print('''Error: Decision tree not yet trained'''  )
												return None



class 				_UpperCAmelCase :

				"""simple docstring"""




				@staticmethod
				def     lowercase__       (					lowerCamelCase						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      float(0  )
								for label in labels:
												squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size  )






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      np.arange(-1.0		,       1.0		,       0.0_05    )
				lowercase__       			=      np.sin(lowerCamelCase_    )

				lowercase__       			=      DecisionTree(depth=10		,       min_leaf_size=10    )
				tree.train(lowerCamelCase_		,       lowerCamelCase_    )

				lowercase__       			=      (np.random.rand(10    ) * 2) - 1
				lowercase__       			=      np.array([tree.predict(lowerCamelCase_    ) for x in test_cases]    )
				lowercase__       			=      np.mean((predictions - test_cases) ** 2    )

				print('''Test values: ''' + str(lowerCamelCase_    )    )
				print('''Predictions: ''' + str(lowerCamelCase_    )    )
				print('''Average error: ''' + str(lowerCamelCase_    )    )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod(name='mean_squarred_error', verbose=True)












import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A__     :      List[Any]             =			get_logger(__name__)

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

# Metrics
A__     :      Optional[int]             =			'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
A__     :      Optional[Any]             =			'https://cdn-datasets.huggingface.co/datasets/metric'
A__     :      Tuple             =			'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

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

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


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

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

A__     :      List[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__     :      List[str]             =			importlib.util.find_spec('torch') is not None
							if TORCH_AVAILABLE:
														try:
																					A__     :      Union[str, 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__     :      int             =			'N/A'
A__     :      Optional[int]             =			False

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


A__     :      Union[str, Any]             =			'N/A'
A__     :      str             =			False

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

# Optional tools for feature decoding
A__     :      List[str]             =			importlib.util.find_spec('PIL') is not None
A__     :      Optional[Any]             =			importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
A__     :      Tuple             =			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__     :      int             =			importlib.util.find_spec('py7zr') is not None


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

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

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

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

A__     :      Optional[int]             =			'downloads'
A__     :      Optional[int]             =			os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A__     :      int             =			Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

A__     :      Optional[int]             =			'extracted'
A__     :      Union[str, Any]             =			os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A__     :      Tuple             =			Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A__     :      List[str]             =			(
    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__     :      List[Any]             =			10_00

# 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__     :      str             =			4 << 30

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

# Parquet configuration
A__     :      Optional[Any]             =			1_00
A__     :      List[Any]             =			1_00
A__     :      Tuple             =			1_00

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

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

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

A__     :      Optional[Any]             =			'datasets_modules'

A__     :      Dict             =			2_55

# Streaming
A__     :      Optional[Any]             =			20
A__     :      str             =			5

# Datasets without script
A__     :      Optional[int]             =			2_00
A__     :      Optional[Any]             =			10
A__     :      Any             =			2_00

# Progress bars
A__     :      Any             =			0.05  # 20 progress updates per sec




