
"""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


_a		       =							logging.get_logger(__name__)


@dataclass
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		[
      """no_inference""",
      """no_cuda""",
      """no_tpu""",
      """no_speed""",
      """no_memory""",
      """no_env_print""",
      """no_multi_process""",
  ]







  def __init__(		self				,   **lowercase_  ):

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

  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default=lowercase__				,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=lowercase__				,metadata={"""help""": """Benchmark models in eager model."""}      )
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={
          """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
      }				,)







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     requires_backends(self				,   ["tf"]  )
     UpperCAmelCase_					:					Optional[int]			     =    None
     if self.tpu:
        try:
           if self.tpu_name:
              UpperCAmelCase_					:					Optional[int]			     =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name  )
           else:
              UpperCAmelCase_					:					str			     =    tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
           UpperCAmelCase_					:					Dict			     =    None
     return tpu







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """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_					:					List[Any]			     =    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_					:					str			     =    tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""  )
        else:
           tf.config.set_visible_devices([]				,   "GPU"  )  # disable GPU
           UpperCAmelCase_					:					Union[str, Any]			     =    tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""  )

     return strategy







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     requires_backends(self				,   ["tf"]  )
     return self._setup_tpu is not None







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     requires_backends(self				,   ["tf"]  )
     return self._setup_strategy







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     requires_backends(self				,   ["tf"]  )
     return tf.config.list_physical_devices("GPU"  )







  @property
  def        UpperCamelCase__					(		self  ):

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







  @property
  def        UpperCamelCase__					(		self  ):

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




"""simple docstring"""





from __future__ import annotations

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    int(input("enter the numbers of values: "  )  )
   UpperCAmelCase_					:					list[list[float]]			     =    []
   for _ in range(__lowerCamelCase  ):
      y.append([]  )
   for i in range(__lowerCamelCase  ):
      for j in range(__lowerCamelCase  ):
         y[i].append(__lowerCamelCase  )
         UpperCAmelCase_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (value - x[0]) / (x[1] - x[0])

   # for calculating forward difference table

   for i in range(1,		__lowerCamelCase  ):
      for j in range(n - i  ):
         UpperCAmelCase_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    y[0][0]
   for i in range(1,		__lowerCamelCase  ):
      summ += (ucal(__lowerCamelCase,		__lowerCamelCase  ) * y[0][i]) / math.factorial(__lowerCamelCase  )

   print(f"""the value at {value} is {summ}"""  )


if __name__ == "__main__":
    main()


