





'''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__						:      Dict	=    logging.get_logger(__name__)
@dataclass
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Tuple						    =   [
           'no_inference',
           'no_cuda',
           'no_tpu',
           'no_speed',
           'no_memory',
           'no_env_print',
           'no_multi_process',
       ]





       def __init__(							self       :		int	,			**lowerCAmelCase__       :		Any					)   ->					Tuple:

            '''simple docstring'''




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

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





       @cached_property
       def snake_case__						(							self       :		Optional[int]					)   ->					Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:

            '''simple docstring'''




            requires_backends(self	,			['''tf''']					)
            _UpperCamelCase       =	None
            if self.tpu:
                 try:
                      if self.tpu_name:
                           _UpperCamelCase       =	tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name					)
                      else:
                           _UpperCamelCase       =	tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                      _UpperCamelCase       =	None
            return tpu





       @cached_property
       def snake_case__						(							self       :		Tuple					)   ->					Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:

            '''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					)

                 _UpperCamelCase       =	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'''					)
                      _UpperCamelCase       =	tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""					)
                 else:
                      tf.config.set_visible_devices([]	,			'''GPU'''					)  # disable GPU
                      _UpperCamelCase       =	tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""					)

            return strategy





       @property
       def snake_case__						(							self       :		Optional[int]					)   ->					bool:

            '''simple docstring'''




            requires_backends(self	,			['''tf''']					)
            return self._setup_tpu is not None





       @property
       def snake_case__						(							self       :		Union[str, Any]					)   ->					"tf.distribute.Strategy":

            '''simple docstring'''




            requires_backends(self	,			['''tf''']					)
            return self._setup_strategy





       @property
       def snake_case__						(							self       :		Any					)   ->					int:

            '''simple docstring'''




            requires_backends(self	,			['''tf''']					)
            return tf.config.list_physical_devices('''GPU'''					)





       @property
       def snake_case__						(							self       :		List[Any]					)   ->					int:

            '''simple docstring'''




            requires_backends(self	,			['''tf''']					)
            if self.cuda:
                 return len(self.gpu_list					)
            return 0







       @property
       def snake_case__						(							self       :		List[Any]					)   ->					bool:

            '''simple docstring'''




            return self.n_gpu > 0









'''simple docstring'''

from math import isclose, sqrt







def  a__    (    lowercase :					float, lowercase :					float, lowercase :					float				)		->		tuple[float, float, float]:





     """simple docstring"""

     _UpperCamelCase       =	point_y / 4 / point_x
     _UpperCamelCase       =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
     _UpperCamelCase       =	(1 - normal_gradient * normal_gradient) / (
         1 + normal_gradient * normal_gradient
     )
     _UpperCamelCase       =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

     # to find the next point, solve the simultaeneous equations:
     # y^2 + 4x^2 = 100
     # y - b = m * (x - a)
     # ==> A x^2 + B x + C = 0
     _UpperCamelCase       =	outgoing_gradient**2 + 4
     _UpperCamelCase       =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
     _UpperCamelCase       =	(point_y - outgoing_gradient * point_x) ** 2 - 100

     _UpperCamelCase       =	(
         -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)
     _UpperCamelCase       =	(
         -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)

     # two solutions, one of which is our input point
     _UpperCamelCase       =	x_minus if isclose(lowercase, lowercase				) else x_plus
     _UpperCamelCase       =	point_y + outgoing_gradient * (next_x - point_x)

     return next_x, next_y, outgoing_gradient







def  a__    (    lowercase :					float = 1.4, lowercase :					float = -9.6				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	0
     _UpperCamelCase       =	first_x_coord
     _UpperCamelCase       =	first_y_coord
     _UpperCamelCase       =	(1_0.1 - point_y) / (0.0 - point_x)

     while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	next_point(lowercase, lowercase, lowercase				)
          num_reflections += 1

     return num_reflections


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



