

'''simple docstring'''






from pathlib import Path

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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = cva.getAffineTransform(A__     ,				A__      )
 return cva.warpAffine(A__     ,				A__     ,				(rows, cols)      )


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

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

 # add all rotated images in a list
 lowerCAmelCase__											=	[
     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__											=	plt.figure(1)
 lowerCAmelCase__											=	['''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'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase__											=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__											=	[
     '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTNeoXJapaneseForCausalLM''',
     '''GPTNeoXJapaneseLayer''',
     '''GPTNeoXJapaneseModel''',
     '''GPTNeoXJapanesePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase__											=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

