'''simple docstring'''




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


_lowercase		:				Optional[int]	       =		logging.get_logger(__name__)





@dataclass
class 					__magic_name__		(					_UpperCAmelCase):
   UpperCamelCase__       = [
       '''no_inference''',
       '''no_cuda''',
       '''no_tpu''',
       '''no_speed''',
       '''no_memory''',
       '''no_env_print''',
       '''no_multi_process''',
   ]







   def __init__(			self       :						Optional[Any]       ,    **lowercase_       :						Tuple					):
          for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                        lowercase_      :    Dict       		= deprecated_arg[3:]
                        lowercase_      :    Dict       		= not kwargs.pop(lowercase_					)
                        logger.warning(
                            f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                            f''' {positive_arg}={kwargs[positive_arg]}'''					)
          lowercase_      :    Optional[Any]       		= kwargs.pop("""tpu_name"""       ,    self.tpu_name					)
          lowercase_      :    Optional[int]       		= kwargs.pop("""device_idx"""       ,    self.device_idx					)
          lowercase_      :    List[Any]       		= kwargs.pop("""eager_mode"""       ,    self.eager_mode					)
          lowercase_      :    Dict       		= kwargs.pop("""use_xla"""       ,    self.use_xla					)
          super().__init__(**lowercase_					)

   UpperCamelCase__       = field(
       default=_UpperCAmelCase,  metadata={'''help''': '''Name of TPU'''},  )
   UpperCamelCase__       = field(
       default=0,  metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''},  )
   UpperCamelCase__       = field(default=_UpperCAmelCase,  metadata={'''help''': '''Benchmark models in eager model.'''})
   UpperCamelCase__       = field(
       default=_UpperCAmelCase,  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]					):
          requires_backends(self       ,    ["""tf"""]					)
          lowercase_      :    Tuple       		= None
          if self.tpu:
                 try:
                        if self.tpu_name:
                               lowercase_      :    Optional[int]       		= tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name					)
                        else:
                               lowercase_      :    Union[str, Any]       		= tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                        lowercase_      :    Optional[int]       		= None
          return tpu







   @cached_property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
          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_      :    Union[str, 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       :						int					):
          requires_backends(self       ,    ["""tf"""]					)
          return self._setup_tpu is not None







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







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
          requires_backends(self       ,    ["""tf"""]					)
          return tf.config.list_physical_devices("""GPU"""					)







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
          requires_backends(self       ,    ["""tf"""]					)
          if self.cuda:
                 return len(self.gpu_list					)
          return 0







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
          return self.n_gpu > 0






'''simple docstring'''




from __future__ import annotations




def 							lowerCamelCase			(   UpperCAmelCase__ :      list   ,   UpperCAmelCase__ :      int | None = None   ,   UpperCAmelCase__ :      int | None = None				)    ->   None:
							if start is None:
														lowercase_      :    Any       		= 0

							if end is None:
														lowercase_      :    List[Any]       		= len(UpperCAmelCase__				) - 1

							if start >= end:
														return

							lowercase_      :    Optional[int]       		= (start + end) // 2

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
							slowsort(UpperCAmelCase__   ,   mid + 1   ,   UpperCAmelCase__				)

							if sequence[end] < sequence[mid]:
														lowercase_		,		lowercase_      :    Dict       		= sequence[mid], sequence[end]

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   end - 1				)


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

						testmod()





