



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__  =			logging.get_logger(__name__)

if is_tf_available():
							import tensorflow as tf







@dataclass
class      __lowerCAmelCase       (     snake_case_		):
			UpperCamelCase     =	'tf'
			UpperCamelCase     =	field(
			    default=snake_case_							,		metadata={'''help''': '''Name of TPU'''}							,		)

			UpperCamelCase     =	field(
			    default=snake_case_							,		metadata={'''help''': '''Zone of TPU'''}							,		)

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

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

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



			@cached_property
			def        _lowerCamelCase			(		self  :    str)  ->       Tuple:



						"""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(lowerCamelCase_)
												tf.tpu.experimental.initialize_tpu_system(lowerCamelCase_)

												_UpperCAmelCase									=  tf.distribute.TPUStrategy(lowerCamelCase_)

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

						return strategy



			@property
			def        _lowerCamelCase			(		self  :    Any)  ->       Tuple:



						"""simple docstring"""






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



			@property
			def        _lowerCamelCase			(		self  :    Tuple)  ->       List[str]:



						"""simple docstring"""






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



			@property
			def        _lowerCamelCase			(		self  :    str)  ->       int:



						"""simple docstring"""






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



			@property
			def        _lowerCamelCase			(		self  :    Optional[int])  ->       Optional[Any]:



						"""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  :    str)  ->       Dict:



						"""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  :    Any)  ->       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"""
from __future__ import annotations






def 			lowerCamelCase_    (		_lowerCamelCase							):
 lowerCamelCase__	:     int
 lowerCamelCase__	:     list[str]								=		list(_lowerCamelCase							)
 lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		_vector[:-1], int(_vector[-1]							)
 lowerCamelCase__	:     list[int]								=		[int(_lowerCamelCase							) for digit in __vector]

 vector.reverse()
 for i, digit in enumerate(_lowerCamelCase							):
  if i & 1 == 0:
   lowerCamelCase__	:     int								=		digit * 2
   if doubled > 9:
    doubled -= 9
   check_digit += doubled
  else:
   check_digit += digit

 return check_digit % 10 == 0


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      assert is_luhn("79927398713")
      assert not is_luhn("79927398714")

