


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
 import tensorflow as tf


__lowerCAmelCase		       =							logging.get_logger(__name__)





@dataclass
class   __a			(    A_      ):
 __lowercase :							Optional[Any]        = [
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]
 def __init__(   self				, **lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        for deprecated_arg in self.deprecated_args:
               if deprecated_arg in kwargs:
                      lowercase__:   Optional[Any]  			=   deprecated_arg[3:]
                      lowercase__:   int  			=   not kwargs.pop(lowerCAmelCase__ )
                      logger.warning(
                          F'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
                          F' {positive_arg}={kwargs[positive_arg]}' )
        lowercase__:   Any  			=   kwargs.pop('tpu_name'				, self.tpu_name )
        lowercase__:   List[Any]  			=   kwargs.pop('device_idx'				, self.device_idx )
        lowercase__:   List[Any]  			=   kwargs.pop('eager_mode'				, self.eager_mode )
        lowercase__:   List[str]  			=   kwargs.pop('use_xla'				, self.use_xla )
        super().__init__(**lowerCAmelCase__ )

 __lowercase :							str        = field(
     default=A_       ,       metadata={'help': 'Name of TPU'}       ,       )
 __lowercase :							int        = field(
     default=0       ,       metadata={'help': 'CPU / GPU device index. Defaults to 0.'}       ,       )
 __lowercase :							bool        = field(default=A_       ,       metadata={'help': 'Benchmark models in eager model.'}      )
 __lowercase :							bool        = field(
     default=A_       ,       metadata={
         'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
     }       ,       )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        requires_backends(self				, ['tf'] )
        lowercase__:   Tuple  			=   None
        if self.tpu:
               try:
                      if self.tpu_name:
                             lowercase__:   List[Any]  			=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
                      else:
                             lowercase__:   Optional[Any]  			=   tf.distribute.cluster_resolver.TPUClusterResolver()
               except ValueError:
                      lowercase__:   Optional[int]  			=   None
        return tpu
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        requires_backends(self				, ['tf'] )
        if self.is_tpu:
               tf.config.experimental_connect_to_cluster(self._setup_tpu )
               tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )

               lowercase__:   str  			=   tf.distribute.TPUStrategy(self._setup_tpu )
        else:
               # currently no multi gpu is allowed
               if self.is_gpu:
                      # TODO: Currently only single GPU is supported
                      tf.config.set_visible_devices(self.gpu_list[self.device_idx]				, 'GPU' )
                      lowercase__:   Any  			=   tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}' )
               else:
                      tf.config.set_visible_devices([]				, 'GPU' )  # disable GPU
                      lowercase__:   Dict  			=   tf.distribute.OneDeviceStrategy(device=F'/cpu:{self.device_idx}' )

        return strategy
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        requires_backends(self				, ['tf'] )
        return self._setup_tpu is not None
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        requires_backends(self				, ['tf'] )
        return self._setup_strategy
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        requires_backends(self				, ['tf'] )
        return tf.config.list_physical_devices('GPU' )
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        requires_backends(self				, ['tf'] )
        if self.cuda:
               return len(self.gpu_list )
        return 0
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return self.n_gpu > 0






from collections.abc import Sequence







def        _lowerCamelCase(	lowercase__   , lowercase__ = False	)						->	float:

		'''simple docstring'''







		if not arr:
				return 0

		__lowercase=			0 if allow_empty_subarrays else float('-inf'	)
		__lowercase=			0.0
		for num in arr:
				__lowercase=			max(0 if allow_empty_subarrays else num   , curr_sum + num	)
				__lowercase=			max(lowercase__   , lowercase__	)

		return max_sum


if __name__ == "__main__":
	from doctest import testmod

	testmod()

	lowerCAmelCase           =		[-2, 1, -3, 4, -1, 2, 1, -5, 4]
	print(F'{max_subarray_sum(nums) = }')






