





import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


snake_case_				=      get_logger(__name__)

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

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

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

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


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

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

snake_case_				=      '''N/A'''
snake_case_				=      False

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

snake_case_				=      '''N/A'''
snake_case_				=      False

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


snake_case_				=      '''N/A'''
snake_case_				=      False

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


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

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


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

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

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

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

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

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

# Download count for the website
snake_case_				=      (
    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)
snake_case_				=      1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
snake_case_				=      1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
snake_case_				=      4 << 3_0

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

# Parquet configuration
snake_case_				=      1_0_0
snake_case_				=      1_0_0
snake_case_				=      1_0_0

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

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

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

snake_case_				=      '''datasets_modules'''

snake_case_				=      2_5_5

# Streaming
snake_case_				=      2_0
snake_case_				=      5

# Datasets without script
snake_case_				=      2_0_0
snake_case_				=      1_0
snake_case_				=      2_0_0

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








def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

		if n % 2 == 1:
				return (bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n - 1				,							SCREAMING_SNAKE_CASE_		) * a) % b

		lowerCamelCase :			Tuple    		=bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n / 2				,							SCREAMING_SNAKE_CASE_		)
		return (r * r) % b


if __name__ == "__main__":
				try:
								snake_case_				=      int(input('''Enter Base : ''').strip())
								snake_case_				=      int(input('''Enter Power : ''').strip())
								snake_case_				=      int(input('''Enter Modulo : ''').strip())
				except ValueError:
								print('''Invalid literal for integer''')

				print(bin_exp_mod(BASE, POWER, MODULO))

