



"""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


UpperCAmelCase				:							Tuple								=logging.get_logger(__name__)

if is_tf_available():
	import tensorflow as tf


@dataclass
class  _lowercase				(SCREAMING_SNAKE_CASE_ ):
				'''simple docstring'''







				lowercase__				  =  """tf"""
				lowercase__				  =  field(
				    default=SCREAMING_SNAKE_CASE_ ,				metadata={"""help""": """Name of TPU"""} ,				)

				lowercase__				  =  field(
				    default=SCREAMING_SNAKE_CASE_ ,				metadata={"""help""": """Zone of TPU"""} ,				)

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

				@cached_property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

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

																			UpperCamelCase_											=				tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_      )

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

									return strategy

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				@property
				def  _lowerCamelCase   (	self      ):

									'''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."      )
									UpperCamelCase_											=				self.per_gpu_train_batch_size or self.per_device_train_batch_size
									return per_device_batch_size * self.n_replicas

				@property
				def  _lowerCamelCase   (	self      ):

									'''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."      )
									UpperCamelCase_											=				self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
									return per_device_batch_size * self.n_replicas

				@property
				def  _lowerCamelCase   (	self      ):

									'''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."       ,	SCREAMING_SNAKE_CASE_       ,	)
									return self._setup_strategy.num_replicas_in_sync





def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				0
					for ele in arr:
										total_sum += product_sum(_lowerCAmelCase				,		depth + 1) if isinstance(_lowerCAmelCase				,		_lowerCAmelCase) else ele
					return total_sum * depth



def 				_lowerCAmelCase      (_lowerCAmelCase):
					return product_sum(_lowerCAmelCase				,		1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
