


"""simple docstring"""







from pathlib import Path

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


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      np.ndarray:
  '''simple docstring'''







  a						:		Optional[int]      							=			cva.getAffineTransform(A__  ,   A__       )
  return cva.warpAffine(A__  ,   A__  ,   (rows, cols)       )


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

 # set different points to rotate image
 __lowercase    					=      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
 __lowercase    					=      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
 __lowercase    					=      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
 __lowercase    					=      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

 # add all rotated images in a list
 __lowercase    					=      [
     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
 __lowercase    					=      plt.figure(1)
 __lowercase    					=      ["""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=0.05, right=1.0, top=0.95)
 plt.show()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :       List[Any]									=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Dict									=  [
        """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """NllbMoeForConditionalGeneration""",
        """NllbMoeModel""",
        """NllbMoePreTrainedModel""",
        """NllbMoeTop2Router""",
        """NllbMoeSparseMLP""",
    ]


if TYPE_CHECKING:
    from .configuration_nllb_moe import (
        NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
        NllbMoeConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_nllb_moe import (
            NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
            NllbMoeForConditionalGeneration,
            NllbMoeModel,
            NllbMoePreTrainedModel,
            NllbMoeSparseMLP,
            NllbMoeTopaRouter,
        )


else:
    import sys

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


