





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


snake_case								=							logging.get_logger(__name__)





@dataclass
class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Any  			=	[
           '''no_inference''',
           '''no_cuda''',
           '''no_tpu''',
           '''no_speed''',
           '''no_memory''',
           '''no_env_print''',
           '''no_multi_process''',
       ]







       def __init__(     self						:						str    ,**__A						:						List[str]	)    ->       int:
         for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
             _lowercase						    =			deprecated_arg[3:]
             _lowercase						    =			not kwargs.pop(__A	)
             logger.warning(
                 F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                 F""" {positive_arg}={kwargs[positive_arg]}"""	)
         _lowercase						    =			kwargs.pop('tpu_name'    ,self.tpu_name	)
         _lowercase						    =			kwargs.pop('device_idx'    ,self.device_idx	)
         _lowercase						    =			kwargs.pop('eager_mode'    ,self.eager_mode	)
         _lowercase						    =			kwargs.pop('use_xla'    ,self.use_xla	)
         super().__init__(**__A	)

       SCREAMING_SNAKE_CASE_   :			str  			=	field(
           default=UpperCAmelCase       ,						metadata={'''help''': '''Name of TPU'''}       ,						)
       SCREAMING_SNAKE_CASE_   :			int  			=	field(
           default=0       ,						metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}       ,						)
       SCREAMING_SNAKE_CASE_   :			bool  			=	field(default=UpperCAmelCase       ,						metadata={'''help''': '''Benchmark models in eager model.'''}						)
       SCREAMING_SNAKE_CASE_   :			bool  			=	field(
           default=UpperCAmelCase       ,						metadata={
               '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
           }       ,						)







       @cached_property
       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
         requires_backends(self    ,['tf']	)
         _lowercase						    =			None
         if self.tpu:
           try:
             if self.tpu_name:
               _lowercase						    =			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name	)
             else:
               _lowercase						    =			tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
             _lowercase						    =			None
         return tpu







       @cached_property
       def  __UpperCAmelCase			(     self						:						str	)    ->       Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
         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						    =			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						    =			tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""	)
           else:
             tf.config.set_visible_devices([]    ,'GPU'	)  # disable GPU
             _lowercase						    =			tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""	)

         return strategy







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       bool:
         requires_backends(self    ,['tf']	)
         return self._setup_tpu is not None







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       "tf.distribute.Strategy":
         requires_backends(self    ,['tf']	)
         return self._setup_strategy







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       str:
         requires_backends(self    ,['tf']	)
         return tf.config.list_physical_devices('GPU'	)







       @property
       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       int:
         requires_backends(self    ,['tf']	)
         if self.cuda:
           return len(self.gpu_list	)
         return 0







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


'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


