



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 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''tf'''
   SCREAMING_SNAKE_CASE__					      =  field(
       default=snake_case_							,metadata={'''help''': '''Name of TPU'''}							,)

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

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

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

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





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

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

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

         return strategy





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





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





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





   @property
   def 						__lowerCAmelCase				(						self      ):
         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			      =	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      ):
         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			      =	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      ):
         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




import math
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int					)->bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5  ,					int(math.sqrt(_SCREAMING_SNAKE_CASE					) + 1					)  ,					6					):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int = 6_0_0_8_5_1_4_7_5_1_4_3					)->int:

      try:
            _lowerCAmelCase			      =	int(_SCREAMING_SNAKE_CASE					)
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''					)
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''					)
      _lowerCAmelCase			      =	0
      if is_prime(_SCREAMING_SNAKE_CASE					):
            return n
      while n % 2 == 0:
            n //= 2
      if is_prime(_SCREAMING_SNAKE_CASE					):
            return n
      for i in range(3  ,					int(math.sqrt(_SCREAMING_SNAKE_CASE					)					) + 1  ,					2					):
            if n % i == 0:
                  if is_prime(n // i					):
                        _lowerCAmelCase			      =	n // i
                        break
                  elif is_prime(_SCREAMING_SNAKE_CASE					):
                        _lowerCAmelCase			      =	i
      return max_number


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