




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     ):
					__lowerCAmelCase				   =       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__    			=			cva.imread(
					    str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
					)
					# turn image in gray scale value
					UpperCamelCase__    			=			cva.cvtColor(image, cva.COLOR_BGR2GRAY)
					# get image shape
					UpperCamelCase__    			=			gray_img.shape

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

					# add all rotated images in a list
					UpperCamelCase__    			=			[
					    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__    			=			plt.figure(1)
					UpperCamelCase__    			=			["""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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase	:	Dict		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Tuple		     =       {
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Any						=			"""swin2sr"""

      _lowercase				:					Tuple						=			{
          """hidden_size""": """embed_dim""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }







      def __init__(    self		,      lowerCAmelCase__=6_4		,      lowerCAmelCase__=1		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8_0		,      lowerCAmelCase__=[6, 6, 6, 6, 6, 6]		,      lowerCAmelCase__=[6, 6, 6, 6, 6, 6]		,      lowerCAmelCase__=8		,      lowerCAmelCase__=2.0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=False		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-5		,      lowerCAmelCase__=2		,      lowerCAmelCase__=1.0		,      lowerCAmelCase__="1conv"		,      lowerCAmelCase__="pixelshuffle"		,      **lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__	:					Optional[Any]										=image_size
        a__	:					Dict										=patch_size
        a__	:					Tuple										=num_channels
        a__	:					Union[str, Any]										=embed_dim
        a__	:					Optional[Any]										=depths
        a__	:					List[str]										=len(lowerCAmelCase__							)
        a__	:					Any										=num_heads
        a__	:					Any										=window_size
        a__	:					str										=mlp_ratio
        a__	:					List[str]										=qkv_bias
        a__	:					Dict										=hidden_dropout_prob
        a__	:					List[str]										=attention_probs_dropout_prob
        a__	:					Dict										=drop_path_rate
        a__	:					Optional[Any]										=hidden_act
        a__	:					Union[str, Any]										=use_absolute_embeddings
        a__	:					Optional[Any]										=layer_norm_eps
        a__	:					List[Any]										=initializer_range
        a__	:					int										=upscale
        a__	:					Optional[int]										=img_range
        a__	:					Any										=resi_connection
        a__	:					Optional[Any]										=upsampler






