


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 :  List[Any]    							=				logging.get_logger(__name__)






@dataclass
class        __snake_case	(       __a):







    """simple docstring"""



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

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

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

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

    @cached_property
    def __lowercase       (       self   :  Optional[Any]		)       ->  int:
      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		)

        lowerCAmelCase_      :				List[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"""		)
          lowerCAmelCase_      :				Optional[int]      =       tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}'		)
        else:
          tf.config.set_visible_devices([]   ,						"""GPU"""		)  # disable GPU
          lowerCAmelCase_      :				Optional[Any]      =       tf.distribute.OneDeviceStrategy(device=F'/cpu:{self.device_idx}'		)

      return strategy

    @property
    def __lowercase       (       self   :  List[str]		)       ->  List[str]:
      requires_backends(self   ,						["""tf"""]		)
      return self._setup_tpu is not None

    @property
    def __lowercase       (       self   :  int		)       ->  Optional[int]:
      requires_backends(self   ,						["""tf"""]		)
      return self._setup_strategy

    @property
    def __lowercase       (       self   :  str		)       ->  Dict:
      requires_backends(self   ,						["""tf"""]		)
      return tf.config.list_physical_devices("""GPU"""		)

    @property
    def __lowercase       (       self   :  Dict		)       ->  Union[str, Any]:
      requires_backends(self   ,						["""tf"""]		)
      if self.cuda:
        return len(self.gpu_list		)
      return 0





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







'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


if __name__ == "__main__":
  # read original image
  __A :  Dict    							=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __A :  List[Any]    							=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __A							,			__A :  Dict    							=				gray_img.shape

  # set different points to rotate image
  __A :  List[str]    							=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __A :  Tuple    							=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __A :  List[Any]    							=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __A :  Optional[Any]    							=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
  plt.show()



