
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


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       logging.get_logger(__name__)

if is_tf_available():
		import tensorflow as tf


@dataclass
class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"tf"
		_lowerCAmelCase						=		field(
		    default=snake_case,			metadata={"help": "Name of TPU"},			)

		_lowerCAmelCase						=		field(
		    default=snake_case,			metadata={"help": "Zone of TPU"},			)

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

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

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





		@cached_property
		def  _A							(							self      :		List[str]			):
							requires_backends(self  ,					['''tf''']			)
							logger.info('''Tensorflow: setting up strategy'''			)

							lowerCAmelCase						:   List[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						:   Dict							=       tf.distribute.OneDeviceStrategy(device='''/cpu:0'''			)
							else:
												try:
																	if self.tpu_name:
																						lowerCAmelCase						:   Dict							=       tf.distribute.cluster_resolver.TPUClusterResolver(
																						    self.tpu_name  ,					zone=self.tpu_zone  ,					project=self.gcp_project			)
																	else:
																						lowerCAmelCase						:   Tuple							=       tf.distribute.cluster_resolver.TPUClusterResolver()
												except ValueError:
																	if self.tpu_name:
																						raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''			)
																	else:
																						lowerCAmelCase						:   Tuple							=       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(lowerCamelCase__			)
																	tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__			)

																	lowerCAmelCase						:   List[str]							=       tf.distribute.TPUStrategy(lowerCamelCase__			)

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

							return strategy





		@property
		def  _A							(							self      :		List[str]			):
							requires_backends(self  ,					['''tf''']			)
							return self._setup_strategy





		@property
		def  _A							(							self      :		str			):
							requires_backends(self  ,					['''tf''']			)
							return self._setup_strategy.num_replicas_in_sync





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





		@property
		def  _A							(							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						:   Any							=       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      :		Tuple			):
							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						:   Any							=       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]			):
							requires_backends(self  ,					['''tf''']			)
							warnings.warn(
							    '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'''  ,					lowerCamelCase__  ,					)
							return self._setup_strategy.num_replicas_in_sync


def       UpperCAmelCase__							(    __magic_name__  :       int = 99_80_01					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       0
					for i in range(9_99							,					99							,					-1					):  # 3 digit numbers range from 999 down to 100
										for j in range(9_99							,					99							,					-1					):
															lowerCAmelCase						:   Any							=       str(i * j					)
															if product_string == product_string[::-1] and i * j < n:
																				lowerCAmelCase						:   Optional[int]							=       max(__magic_name__							,					i * j					)
					return answer


if __name__ == "__main__":
		print(f"""{solution() = }""")
