






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


UpperCamelCase_	:    Tuple  					=       logging.get_logger(__name__)

if is_tf_available():
							import tensorflow as tf




@dataclass
class      __lowercase	(	a_					):
	_A   			=      'tf'
	_A   			=      field(
	    default=a_      ,   metadata={"help": "Name of TPU"}      ,   )

	_A   			=      field(
	    default=a_      ,   metadata={"help": "Zone of TPU"}      ,   )

	_A   			=      field(
	    default=a_      ,   metadata={"help": "Name of Cloud TPU-enabled project"}      ,   )

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

	_A   			=      field(default=a_      ,   metadata={"help": "Whether to activate the XLA compilation or not"}					)






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

							_lowercase					:  Dict    			=      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:
													_lowercase					:  Any    			=      tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
							else:
													try:
																			if self.tpu_name:
																									_lowercase					:  str    			=      tf.distribute.cluster_resolver.TPUClusterResolver(
																									    self.tpu_name			,  zone=self.tpu_zone			,  project=self.gcp_project						)
																			else:
																									_lowercase					:  str    			=      tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																			if self.tpu_name:
																									raise RuntimeError(F"""Couldn\'t connect to TPU {self.tpu_name}!"""						)
																			else:
																									_lowercase					:  Dict    			=      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(_snake_case						)
																			tf.tpu.experimental.initialize_tpu_system(_snake_case						)

																			_lowercase					:  Dict    			=      tf.distribute.TPUStrategy(_snake_case						)

													elif len(_snake_case						) == 0:
																			_lowercase					:  List[Any]    			=      tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
													elif len(_snake_case						) == 1:
																			_lowercase					:  Optional[int]    			=      tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
													elif len(_snake_case						) > 1:
																			# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																			_lowercase					:  Optional[Any]    			=      tf.distribute.MirroredStrategy()
													else:
																			raise ValueError("Cannot find the proper strategy, please check your environment properties."						)

							return strategy






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






	@property
	def 			_a(self							:   str						)   -> int:
							requires_backends(self			,  ["tf"]						)
							return self._setup_strategy.num_replicas_in_sync






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






	@property
	def 			_a(self							:   Optional[int]						)   -> 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."						)
							_lowercase					:  Tuple    			=      self.per_gpu_train_batch_size or self.per_device_train_batch_size
							return per_device_batch_size * self.n_replicas






	@property
	def 			_a(self							:   Any						)   -> 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."						)
							_lowercase					:  str    			=      self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
							return per_device_batch_size * self.n_replicas






	@property
	def 			_a(self							:   Optional[Any]						)   -> 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."			,  _snake_case			,  )
							return self._setup_strategy.num_replicas_in_sync






import math



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> list:






						'''simple docstring'''

						if n <= 0:
												raise ValueError("Only positive integers have prime factors"     )
						_lowercase					:  Optional[int]    			=      []
						while n % 2 == 0:
												pf.append(2     )
												_lowercase					:  Tuple    			=      int(n / 2     )
						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
												while n % i == 0:
																		pf.append(_UpperCAmelCase     )
																		_lowercase					:  Optional[int]    			=      int(n / i     )
						if n > 2:
												pf.append(_UpperCAmelCase     )
						return pf



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						if n <= 0:
												raise ValueError("Only positive numbers are accepted"     )
						_lowercase					:  str    			=      1
						_lowercase					:  Tuple    			=      1
						while n % 2 == 0:
												temp += 1
												_lowercase					:  List[Any]    			=      int(n / 2     )
						div *= temp
						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
												_lowercase					:  Optional[Any]    			=      1
												while n % i == 0:
																		temp += 1
																		_lowercase					:  Optional[Any]    			=      int(n / i     )
												div *= temp
						if n > 1:
												div *= 2
						return div



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						if n <= 0:
												raise ValueError("Only positive numbers are accepted"     )
						_lowercase					:  int    			=      1
						_lowercase					:  List[str]    			=      1
						while n % 2 == 0:
												temp += 1
												_lowercase					:  str    			=      int(n / 2     )
						if temp > 1:
												s *= (2**temp - 1) / (2 - 1)
						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
												_lowercase					:  int    			=      1
												while n % i == 0:
																		temp += 1
																		_lowercase					:  List[Any]    			=      int(n / i     )
												if temp > 1:
																		s *= (i**temp - 1) / (i - 1)
						return int(_UpperCAmelCase     )



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						_lowercase					:  int    			=      n
						for x in set(prime_factors(_UpperCAmelCase     )     ):
												s *= (x - 1) / x
						return int(_UpperCAmelCase     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




