






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      []
				lowercase__       			=      len(lowerCamelCase_    )
				for i in range(lowerCamelCase_    ):
								lowercase__       			=      []
								for j in range(lowerCamelCase_    ):
												lowercase__       			=      0
												for k in range(lowerCamelCase_    ):
																lowercase__       			=      val + matrix_a[i][k] * matrix_b[k][j]
												list_a.append(lowerCamelCase_    )
								matrix_c.append(lowerCamelCase_    )
				return matrix_c






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return [[int(row == column    ) for column in range(lowerCamelCase_    )] for row in range(lowerCamelCase_    )]






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if n <= 1:
								return n
				lowercase__       			=      identity(2    )
				lowercase__       			=      [[1, 1], [1, 0]]
				lowercase__       			=      n - 1
				while n > 0:
								if n % 2 == 1:
												lowercase__       			=      multiply(lowerCamelCase_		,       lowerCamelCase_    )
								lowercase__       			=      multiply(lowerCamelCase_		,       lowerCamelCase_    )
								lowercase__       			=      int(n / 2    )
				return res_matrix[0][0]






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if n <= 1:
								return n
				lowercase__       			=      0
				lowercase__       			=      1
				for _ in range(2		,       n + 1    ):
								lowercase__			,						lowercase__       			=      fiba, fiba + fiba
				return fiba






def   a      (						):






				'''simple docstring'''
				for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
								lowercase__       			=      int(''''''.join(c for c in ordinal if c in '''0123456789'''    )    )  # 1000th --> 1000
								print(
								    F"""{ordinal} fibonacci number using matrix exponentiation is """
								    F"""{nth_fibonacci_matrix(lowerCamelCase_    )} and using bruteforce is """
								    F"""{nth_fibonacci_bruteforce(lowerCamelCase_    )}\n"""    )
				# from timeit import timeit
				# print(timeit("nth_fibonacci_matrix(1000000)",
				#              "from main import nth_fibonacci_matrix", number=5))
				# print(timeit("nth_fibonacci_bruteforce(1000000)",
				#              "from main import nth_fibonacci_bruteforce", number=5))
				# 2.3342058970001744
				# 57.256506615000035


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()












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




