


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase : Optional[Any] 			=				logging.get_logger(__name__)

__UpperCAmelCase : Dict 			=				{
    "facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Union[str, Any]							=					"timesformer"






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=224 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=768 ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=3_072 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1e-6 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="divided_space_time" ,					__SCREAMING_SNAKE_CASE=0 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		image_size
              UpperCamelCase    :	str   	=		patch_size
              UpperCamelCase    :	Optional[int]   	=		num_channels
              UpperCamelCase    :	str   	=		num_frames

              UpperCamelCase    :	Tuple   	=		hidden_size
              UpperCamelCase    :	List[str]   	=		num_hidden_layers
              UpperCamelCase    :	Union[str, Any]   	=		num_attention_heads
              UpperCamelCase    :	List[Any]   	=		intermediate_size
              UpperCamelCase    :	int   	=		hidden_act
              UpperCamelCase    :	Optional[Any]   	=		hidden_dropout_prob
              UpperCamelCase    :	Tuple   	=		attention_probs_dropout_prob
              UpperCamelCase    :	int   	=		initializer_range
              UpperCamelCase    :	Any   	=		layer_norm_eps
              UpperCamelCase    :	Union[str, Any]   	=		qkv_bias

              UpperCamelCase    :	Tuple   	=		attention_type
              UpperCamelCase    :	int   	=		drop_path_rate





from pathlib import Path

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


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


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

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

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, 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
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : 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()

