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


lowerCamelCase__   = logging.get_logger(__name__)

if is_tf_available():
   import tensorflow as tf




@dataclass
class  _UpperCAmelCase       (			lowerCAmelCase__   ):



 '''simple docstring'''




 __A           = "tf"
 __A           = field(
     default=lowerCAmelCase__,       metadata={'''help''': '''Name of TPU'''},       )

 __A           = field(
     default=lowerCAmelCase__,       metadata={'''help''': '''Zone of TPU'''},       )

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

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

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







 @cached_property
 def 						__UpperCAmelCase							(						self							:					Tuple)		->     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(a__)
       tf.tpu.experimental.initialize_tpu_system(a__)

       _UpperCamelCase				=  tf.distribute.TPUStrategy(a__)

     elif len(a__) == 0:
       _UpperCamelCase				=  tf.distribute.OneDeviceStrategy(device="/cpu:0")
     elif len(a__) == 1:
       _UpperCamelCase				=  tf.distribute.OneDeviceStrategy(device="/gpu:0")
     elif len(a__) > 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 						__UpperCAmelCase							(						self							:					Optional[Any])		->     int:


   """simple docstring"""







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







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


   """simple docstring"""







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







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


   """simple docstring"""







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







 @property
 def 						__UpperCAmelCase							(						self							:					List[str])		->     Optional[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.")
   _UpperCamelCase				=  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])		->     int:


   """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 						__UpperCAmelCase							(						self							:					List[str])		->     Optional[int]:


   """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."				,      a__				,      )
   return self._setup_strategy.num_replicas_in_sync











"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       )->		Tuple:
       _lowerCamelCase    	=					1

       while True:
              if (
                  sorted(str(snake_case		)		)
                  == sorted(str(2 * i		)		)
                  == sorted(str(3 * i		)		)
                  == sorted(str(4 * i		)		)
                  == sorted(str(5 * i		)		)
                  == sorted(str(6 * i		)		)
              ):
                     return i

              i += 1


if __name__ == "__main__":
      print(solution())






