'''simple docstring'''

from pathlib import Path

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


def __a (	__lowerCamelCase			:						np.ndarray ,		__lowerCamelCase			:						np.ndarray ,		__lowerCamelCase			:						np.ndarray ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int				)	->							np.ndarray:
      '''simple docstring'''


      lowercase_     =      cva.getAffineTransform(__lowerCamelCase ,		__lowerCamelCase				)
      return cva.warpAffine(__lowerCamelCase ,		__lowerCamelCase ,		(rows, cols)				)


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

       # set different points to rotate image
       lowerCAmelCase_ : str						  =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       lowerCAmelCase_ : Union[str, Any]						  =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       lowerCAmelCase_ : Dict						  =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       lowerCAmelCase_ : int						  =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       lowerCAmelCase_ : 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
       lowerCAmelCase_ : List[Any]						  =      plt.figure(1)
       lowerCAmelCase_ : Dict						  =      ["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()







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






