


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					 =	0.0_5  # 20 progress updates per sec










def  _lowerCamelCase(			lowercase__			)		->			str:

		'''simple docstring'''







		if len(lowercase__			) == 0 or not all(len(lowercase__			) == 2 for pair in coordinates			):
				raise ValueError('The program cannot work out a fitting polynomial.'			)

		if len({tuple(lowercase__			) for pair in coordinates}			) != len(lowercase__			):
				raise ValueError('The program cannot work out a fitting polynomial.'			)

		__lowercase=    {x for x, _ in coordinates}
		if len(lowercase__			) == 1:
				return F'x={coordinates[0][0]}'

		if len(lowercase__			) != len(lowercase__			):
				raise ValueError('The program cannot work out a fitting polynomial.'			)

		__lowercase=    len(lowercase__			)

		__lowercase=    0
		__lowercase=    []
		# put the x and x to the power values in a matrix
		while count_of_line < x:
				__lowercase=    0
				__lowercase=    coordinates[count_of_line][0]
				__lowercase=    []
				while count_in_line < x:
						count_line.append(a ** (x - (count_in_line + 1))			)
						count_in_line += 1
				matrix.append(lowercase__			)
				count_of_line += 1

		__lowercase=    0
		# put the y values into a vector
		__lowercase=    []
		while count_of_line < x:
				vector.append(coordinates[count_of_line][1]			)
				count_of_line += 1

		__lowercase=    0

		while count < x:
				__lowercase=    0
				while zahlen < x:
						if count == zahlen:
								zahlen += 1
						if zahlen == x:
								break
						__lowercase=    matrix[zahlen][count] / matrix[count][count]
						for counting_columns, item in enumerate(matrix[count]			):
								# manipulating all the values in the matrix
								matrix[zahlen][counting_columns] -= item * bruch
						# manipulating the values in the vector
						vector[zahlen] -= vector[count] * bruch
						zahlen += 1
				count += 1

		__lowercase=    0
		# make solutions
		__lowercase=    []
		while count < x:
				solution.append(str(vector[count] / matrix[count][count]			)			)
				count += 1

		__lowercase=    0
		__lowercase=    'f(x)='

		while count < x:
				__lowercase=    solution[count].split('E'			)
				if len(lowercase__			) > 1:
						__lowercase=    F'{remove_e[0]}*10^{remove_e[1]}'
				solved += F'x^{x - (count + 1)}*{solution[count]}'
				if count + 1 != x:
						solved += "+"
				count += 1

		return solved


if __name__ == "__main__":
	print(points_to_polynomial([]))
	print(points_to_polynomial([[]]))
	print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
	print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
	print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
	print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
	print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))






