

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class    snake_case     (    __snake_case,							__snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  """swin"""

				SCREAMING_SNAKE_CASE_       :				int       				=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(     self							:    int					,  UpperCamelCase__							:    str=2_2_4					,  UpperCamelCase__							:    Dict=4					,  UpperCamelCase__							:    Union[str, Any]=3					,  UpperCamelCase__							:    List[str]=9_6					,  UpperCamelCase__							:    int=[2, 2, 6, 2]					,  UpperCamelCase__							:    Union[str, Any]=[3, 6, 1_2, 2_4]					,  UpperCamelCase__							:    List[str]=7					,  UpperCamelCase__							:    Optional[Any]=4.0					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    Union[str, Any]=0.0					,  UpperCamelCase__							:    str=0.0					,  UpperCamelCase__							:    Any=0.1					,  UpperCamelCase__							:    Dict="gelu"					,  UpperCamelCase__							:    Any=False					,  UpperCamelCase__							:    Any=0.02					,  UpperCamelCase__							:    Tuple=1e-5					,  UpperCamelCase__							:    Tuple=3_2					,  UpperCamelCase__							:    Tuple=None					,  UpperCamelCase__							:    str=None					,  **UpperCamelCase__							:    List[str]					,  )->       Union[str, Any]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      image_size
						__lowerCAmelCase:	Union[str, Any]   				=      patch_size
						__lowerCAmelCase:	Tuple   				=      num_channels
						__lowerCAmelCase:	Any   				=      embed_dim
						__lowerCAmelCase:	Optional[Any]   				=      depths
						__lowerCAmelCase:	List[str]   				=      len(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      num_heads
						__lowerCAmelCase:	int   				=      window_size
						__lowerCAmelCase:	Dict   				=      mlp_ratio
						__lowerCAmelCase:	List[Any]   				=      qkv_bias
						__lowerCAmelCase:	Optional[int]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Any   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	str   				=      drop_path_rate
						__lowerCAmelCase:	List[Any]   				=      hidden_act
						__lowerCAmelCase:	Optional[Any]   				=      use_absolute_embeddings
						__lowerCAmelCase:	Union[str, Any]   				=      layer_norm_eps
						__lowerCAmelCase:	List[Any]   				=      initializer_range
						__lowerCAmelCase:	Union[str, Any]   				=      encoder_stride
						# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						__lowerCAmelCase:	Dict   				=      int(embed_dim * 2 ** (len(UpperCamelCase__) - 1))
						__lowerCAmelCase:	List[str]   				=      ["stem"] + [f"stage{idx}" for idx in range(1					,  len(UpperCamelCase__) + 1)]
						__lowerCAmelCase:	Tuple   				=      get_aligned_output_features_output_indices(
						    out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=self.stage_names)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  version.parse("""1.11"""  )

				@property
				def 							lowercase_					(     self							:    str)->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_					(     self							:    List[str])->       float:



						'''simple docstring'''


						return 1e-4





"""simple docstring"""


from pathlib import Path

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      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
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

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

			# add all rotated images in a list
			__A						       =      [
			    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						       =      plt.figure(1)
			__A						       =      ["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()


