
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


_snake_case		  =   logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf


@dataclass
class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







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

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

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

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

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







      @cached_property
      def lowercase__		( 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(_UpperCAmelCase)
                        tf.tpu.experimental.initialize_tpu_system(_UpperCAmelCase)

                        lowerCAmelCase_						=      tf.distribute.TPUStrategy(_UpperCAmelCase)

                  elif len(_UpperCAmelCase) == 0:
                        lowerCAmelCase_						=      tf.distribute.OneDeviceStrategy(device='''/cpu:0''')
                  elif len(_UpperCAmelCase) == 1:
                        lowerCAmelCase_						=      tf.distribute.OneDeviceStrategy(device='''/gpu:0''')
                  elif len(_UpperCAmelCase) > 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 lowercase__		( self):
            requires_backends(self					,					['''tf'''])
            return self._setup_strategy







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







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







      @property
      def lowercase__		( 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 lowercase__		( 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 lowercase__		( 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.'''					,					_UpperCAmelCase					,					)
            return self._setup_strategy.num_replicas_in_sync





_snake_case		  =   {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      return c.isdigit()
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(A						):
                  stack.append(int(A						)						)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowerCAmelCase_						=      stack.pop()
                  lowerCAmelCase_						=      stack.pop()
                  stack.append(calc[c](A		,					A						)						)

      return stack.pop()


# Driver code
if __name__ == "__main__":
 _snake_case		  =   "+ 9 * 2 6"
 print(evaluate(test_expression))

 _snake_case		  =   "/ * 10 2 + 4 1 "
 print(evaluate(test_expression))



