
"""simple docstring"""

import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_A            = logging.get_logger(__name__)

if is_tf_available():
					import tensorflow as tf






@dataclass
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	'tf'
	lowercase_        =	field(
	    default=__UpperCAmelCase				,       metadata={'help': 'Name of TPU'}				,       )

	lowercase_        =	field(
	    default=__UpperCAmelCase				,       metadata={'help': 'Zone of TPU'}				,       )

	lowercase_        =	field(
	    default=__UpperCAmelCase				,       metadata={'help': 'Name of Cloud TPU-enabled project'}				,       )

	lowercase_        =	field(
	    default=1.0				,       metadata={'help': 'Power for the Polynomial decay LR scheduler.'}				,       )

	lowercase_        =	field(default=__UpperCAmelCase				,       metadata={'help': 'Whether to activate the XLA compilation or not'}		)




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Tuple["tf.distribute.Strategy", int]:
			requires_backends(self		, ['tf']					)
			logger.info('Tensorflow: setting up strategy'					)

			lowerCamelCase     :			Union[str, Any]		=    tf.config.list_physical_devices('GPU'					)

			# Set to float16 at first
			if self.fpaa:
					tf.keras.mixed_precision.set_global_policy('mixed_float16'					)

			if self.no_cuda:
					lowerCamelCase     :			Optional[int]		=    tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
			else:
					try:
							if self.tpu_name:
									lowerCamelCase     :			int		=    tf.distribute.cluster_resolver.TPUClusterResolver(
									    self.tpu_name		, zone=self.tpu_zone		, project=self.gcp_project					)
							else:
									lowerCamelCase     :			Optional[int]		=    tf.distribute.cluster_resolver.TPUClusterResolver()
					except ValueError:
							if self.tpu_name:
									raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""					)
							else:
									lowerCamelCase     :			Optional[int]		=    None

					if tpu:
							# Set to bfloat16 in case of TPU
							if self.fpaa:
									tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'					)

							tf.config.experimental_connect_to_cluster(UpperCAmelCase_					)
							tf.tpu.experimental.initialize_tpu_system(UpperCAmelCase_					)

							lowerCamelCase     :			List[str]		=    tf.distribute.TPUStrategy(UpperCAmelCase_					)

					elif len(UpperCAmelCase_					) == 0:
							lowerCamelCase     :			str		=    tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
					elif len(UpperCAmelCase_					) == 1:
							lowerCamelCase     :			Dict		=    tf.distribute.OneDeviceStrategy(device='/gpu:0'					)
					elif len(UpperCAmelCase_					) > 1:
							# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
							lowerCamelCase     :			Any		=    tf.distribute.MirroredStrategy()
					else:
							raise ValueError('Cannot find the proper strategy, please check your environment properties.'					)

			return strategy




	@property
	def 			_UpperCamelCase    ( self					)		->							"tf.distribute.Strategy":
			requires_backends(self		, ['tf']					)
			return self._setup_strategy




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			requires_backends(self		, ['tf']					)
			return self._setup_strategy.num_replicas_in_sync




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			return False  # TF Logging is handled by Keras not the Trainer




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			if self.per_gpu_train_batch_size:
					logger.warning(
					    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
					    'version. Using `--per_device_train_batch_size` is preferred.'					)
			lowerCamelCase     :			str		=    self.per_gpu_train_batch_size or self.per_device_train_batch_size
			return per_device_batch_size * self.n_replicas




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			if self.per_gpu_eval_batch_size:
					logger.warning(
					    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
					    'version. Using `--per_device_eval_batch_size` is preferred.'					)
			lowerCamelCase     :			List[Any]		=    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
			return per_device_batch_size * self.n_replicas




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			requires_backends(self		, ['tf']					)
			warnings.warn(
			    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'		, UpperCAmelCase_		, )
			return self._setup_strategy.num_replicas_in_sync








"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		for r in range(1,						a_			):
				lowerCamelCase     :			Tuple		=    []
				for x in range(n - 1			):
						lowerCamelCase     :			List[str]		=    pow(a_,						a_,						a_			)
						if val in li:
								break
						li.append(a_			)
				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)






