

'''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


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

if is_tf_available():
			import tensorflow as tf

@dataclass
class 			SCREAMING_SNAKE_CASE					(							lowerCAmelCase_					):






						'''simple docstring'''






						SCREAMING_SNAKE_CASE__ :				List[Any]    =				'tf'
						SCREAMING_SNAKE_CASE__ :				Optional[str]    =				field(
						    default=lowerCAmelCase_      ,metadata={'''help''': '''Name of TPU'''}      ,)

						SCREAMING_SNAKE_CASE__ :				Optional[str]    =				field(
						    default=lowerCAmelCase_      ,metadata={'''help''': '''Zone of TPU'''}      ,)

						SCREAMING_SNAKE_CASE__ :				Optional[str]    =				field(
						    default=lowerCAmelCase_      ,metadata={'''help''': '''Name of Cloud TPU-enabled project'''}      ,)

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

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




						@cached_property
						def 	__UpperCAmelCase			(					self :		Any   ):



										"""simple docstring"""





										requires_backends(self     ,					['tf']   )
										logger.info('Tensorflow: setting up strategy'   )

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

																		_snake_case : Tuple												=		tf.distribute.TPUStrategy(lowerCamelCase_   )

														elif len(lowerCamelCase_   ) == 0:
																		_snake_case : Optional[Any]												=		tf.distribute.OneDeviceStrategy(device='/cpu:0'   )
														elif len(lowerCamelCase_   ) == 1:
																		_snake_case : 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`
																		_snake_case : Optional[int]												=		tf.distribute.MirroredStrategy()
														else:
																		raise ValueError('Cannot find the proper strategy, please check your environment properties.'   )

										return strategy




						@property
						def 	__UpperCAmelCase			(					self :		List[str]   ):



										"""simple docstring"""





										requires_backends(self     ,					['tf']   )
										return self._setup_strategy




						@property
						def 	__UpperCAmelCase			(					self :		Any   ):



										"""simple docstring"""





										requires_backends(self     ,					['tf']   )
										return self._setup_strategy.num_replicas_in_sync




						@property
						def 	__UpperCAmelCase			(					self :		Optional[int]   ):



										"""simple docstring"""





										return False  # TF Logging is handled by Keras not the Trainer




						@property
						def 	__UpperCAmelCase			(					self :		int   ):



										"""simple docstring"""





										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.'   )
										_snake_case : 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 :		List[str]   ):



										"""simple docstring"""





										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.'   )
										_snake_case : Optional[int]												=		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 :		Optional[Any]   ):



										"""simple docstring"""





										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






'''simple docstring'''



def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Tuple												=		0
    for ele in arr:
        total_sum += product_sum(a__			,      depth + 1) if isinstance(a__			,      a__) else ele
    return total_sum * depth

def   lowerCamelCase__       (      a__)		->    int:



    """simple docstring"""







    return product_sum(a__			,      1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



