

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			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





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

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       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]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			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			)

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

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





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





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





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





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0







"""simple docstring"""







from __future__ import annotations

from scipy.special import comb  # type: ignore


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :list[tuple[float, float]]				)      ->str:
										lowercase    :      List[Any]       = list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										lowercase    :      Optional[int]       = len(__magic_name__				) - 1






						def      __snake_case      (		self  :List[str]			, __magic_name__  :float				)      ->list[float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										lowercase    :      list[float]       = []
										for i in range(len(self.list_of_points				)				):
														# basis function for each i
														output_values.append(
														    comb(self.degree			, __magic_name__				) * ((1 - t) ** (self.degree - i)) * (t**i)				)
										# the basis must sum up to 1 for it to produce a valid Bezier curve.
										assert round(sum(__magic_name__				)			, 5				) == 1
										return output_values






						def      __snake_case      (		self  :List[str]			, __magic_name__  :float				)      ->tuple[float, float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										lowercase    :      str       = self.basis_function(__magic_name__				)
										lowercase    :      Optional[int]       = 0.0
										lowercase    :      List[str]       = 0.0
										for i in range(len(self.list_of_points				)				):
														# For all points, sum up the product of i-th basis function and i-th point.
														x += basis_function[i] * self.list_of_points[i][0]
														y += basis_function[i] * self.list_of_points[i][1]
										return (x, y)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :float = 0.01				)      ->List[str]:
										from matplotlib import pyplot as plt  # type: ignore

										lowercase    :      list[float]       = []  # x coordinates of points to plot
										lowercase    :      list[float]       = []  # y coordinates of points to plot

										lowercase    :      int       = 0.0
										while t <= 1:
														lowercase    :      List[Any]       = self.bezier_curve_function(__magic_name__				)
														to_plot_x.append(value[0]				)
														to_plot_y.append(value[1]				)
														t += step_size

										lowercase    :      Union[str, Any]       = [i[0] for i in self.list_of_points]
										lowercase    :      Optional[int]       = [i[1] for i in self.list_of_points]

										plt.plot(
										    __magic_name__			, __magic_name__			, color="""blue"""			, label="""Curve of Degree """ + str(self.degree				)			, )
										plt.scatter(__magic_name__			, __magic_name__			, color="""red"""			, label="""Control Points"""				)
										plt.legend()
										plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
				BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
				BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3
