


'''simple docstring'''







from pathlib import Path

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







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A      ,			__A    )							->		np.ndarray:


      '''simple docstring'''



      UpperCamelCase__										=				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__  :					Optional[int]        =       cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  a__ , a__  :					Union[str, Any]        =       gray_img.shape

  # set different points to rotate image
  a__  :					int        =       np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
  a__  :					Tuple        =       np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
  a__  :					Union[str, Any]        =       np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
  a__  :					Optional[Any]        =       np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)

  # add all rotated images in a list
  a__  :					List[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__  :					str        =       plt.figure(1)
  a__  :					str        =       ['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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

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




