






"""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


a  :						Optional[Any]					       =							logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf


@dataclass
class 	_UpperCamelCase ( a_							):



    '''simple docstring'''


    __lowercase    :   List[str]											=						"tf"
    __lowercase    :   Optional[str]											=						field(
        default=a_		,     metadata={'help': 'Name of TPU'}		,     )

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

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

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

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


    @cached_property
    def 		A__      (      self			):
     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(_snake_case			)
       tf.tpu.experimental.initialize_tpu_system(_snake_case			)

       UpperCAmelCase__ 						=				tf.distribute.TPUStrategy(_snake_case			)

      elif len(_snake_case			) == 0:
       UpperCAmelCase__ 						=				tf.distribute.OneDeviceStrategy(device="""/cpu:0"""			)
      elif len(_snake_case			) == 1:
       UpperCAmelCase__ 						=				tf.distribute.OneDeviceStrategy(device="""/gpu:0"""			)
      elif len(_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 		A__      (      self			):
     requires_backends(self   ,			["""tf"""]			)
     return self._setup_strategy


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


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


    @property
    def 		A__      (      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."""			)
     UpperCAmelCase__ 						=				self.per_gpu_train_batch_size or self.per_device_train_batch_size
     return per_device_batch_size * self.n_replicas


    @property
    def 		A__      (      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."""			)
     UpperCAmelCase__ 						=				self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
     return per_device_batch_size * self.n_replicas






    @property
    def 		A__      (      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."""   ,			_snake_case   ,			)
     return self._setup_strategy.num_replicas_in_sync






'''simple docstring'''


from itertools import combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase = 5_0	)     ->     int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1	) ,repeat=2	) ,2	)
	    if is_right(*UpperCamelCase ,*UpperCamelCase	)	)


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






