
'''simple docstring'''
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowerCAmelCase :  int      =      get_logger(__name__)

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

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

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

lowerCAmelCase :  str      =      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 :  str      =      {'1', 'ON', 'YES', 'TRUE'}
lowerCAmelCase :  int      =      ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
lowerCAmelCase :  List[Any]      =      version.parse(importlib_metadata.version('dill'))
lowerCAmelCase :  str      =      version.parse(importlib_metadata.version('pandas'))
lowerCAmelCase :  Tuple      =      version.parse(importlib_metadata.version('pyarrow'))

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

lowerCAmelCase :  str      =      'N/A'
lowerCAmelCase :  List[str]      =      False

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

lowerCAmelCase :  Tuple      =      'N/A'
lowerCAmelCase :  Dict      =      False

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


lowerCAmelCase :  str      =      'N/A'
lowerCAmelCase :  Dict      =      False

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


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

# Optional tools for feature decoding
lowerCAmelCase :  Optional[Any]      =      importlib.util.find_spec('PIL') is not None
lowerCAmelCase :  List[str]      =      importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
lowerCAmelCase :  Union[str, Any]      =      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 :  Optional[Any]      =      importlib.util.find_spec('rarfile') is not None
lowerCAmelCase :  Tuple      =      importlib.util.find_spec('zstandard') is not None
lowerCAmelCase :  Tuple      =      importlib.util.find_spec('lz4') is not None
lowerCAmelCase :  Any      =      importlib.util.find_spec('py7zr') is not None


# Cache location
lowerCAmelCase :  str      =      '~/.cache'
lowerCAmelCase :  Optional[int]      =      os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
lowerCAmelCase :  int      =      os.path.join(XDG_CACHE_HOME, 'huggingface')
lowerCAmelCase :  Optional[Any]      =      os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))

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

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

lowerCAmelCase :  Any      =      os.path.join(HF_CACHE_HOME, 'modules')
lowerCAmelCase :  str      =      Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

lowerCAmelCase :  List[str]      =      'downloads'
lowerCAmelCase :  Any      =      os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowerCAmelCase :  int      =      Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

lowerCAmelCase :  Any      =      'extracted'
lowerCAmelCase :  Tuple      =      os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowerCAmelCase :  str      =      Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowerCAmelCase :  Any      =      (
    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 :  Optional[int]      =      10_00

# Size of the preloaded record batch in `Dataset.__iter__`
lowerCAmelCase :  Dict      =      10

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

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

# Parquet configuration
lowerCAmelCase :  int      =      1_00
lowerCAmelCase :  Any      =      1_00
lowerCAmelCase :  int      =      1_00

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

# In-memory
lowerCAmelCase :  Optional[Any]      =      0  # Disabled
lowerCAmelCase :  int      =      float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

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

lowerCAmelCase :  Optional[int]      =      'datasets_modules'

lowerCAmelCase :  Dict      =      2_55

# Streaming
lowerCAmelCase :  List[str]      =      20
lowerCAmelCase :  List[Any]      =      5

# Datasets without script
lowerCAmelCase :  List[str]      =      2_00
lowerCAmelCase :  Any      =      10
lowerCAmelCase :  str      =      2_00

# Progress bars
lowerCAmelCase :  Optional[Any]      =      0.05  # 20 progress updates per sec









from __future__ import annotations



def        __a     (    lowerCAmelCase_   :					int				)      -> int | None:






						'''simple docstring'''



						for r in range(1			,lowerCAmelCase_				):
												UpperCAmelCase_=   []
												for x in range(n - 1				):
																		UpperCAmelCase_=   pow(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)
																		if val in li:
																								break
																		li.append(lowerCAmelCase_				)
												else:
																		return r
						return None


if __name__ == "__main__":
	__A				    =						int(input('''Enter a prime number q: '''))
	__A				    =						find_primitive(q)
	if a is None:
		print(f'Cannot find the primitive for the value: {a!r}')
	else:
		__A				    =						int(input('''Enter private key of A: '''))
		__A				    =						pow(a, a_private, q)
		__A				    =						int(input('''Enter private key of B: '''))
		__A				    =						pow(a, b_private, q)

		__A				    =						pow(b_public, a_private, q)
		__A				    =						pow(a_public, b_private, q)

		print('''The key value generated by A is: ''', a_secret)
		print('''The key value generated by B is: ''', b_secret)




