


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


lowerCAmelCase			=				logging.get_logger(__name__)

lowerCAmelCase			=				{
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     Optional[Any]     =					'''mobilenet_v1'''

							def __init__(				self:      List[Any]       ,		UpperCamelCase_:      Any=3       ,		UpperCamelCase_:      int=224       ,		UpperCamelCase_:      Optional[int]=1.0       ,		UpperCamelCase_:      int=8       ,		UpperCamelCase_:      str="relu6"       ,		UpperCamelCase_:      Optional[Any]=True       ,		UpperCamelCase_:      List[Any]=0.999       ,		UpperCamelCase_:      List[Any]=0.02       ,		UpperCamelCase_:      Union[str, Any]=0.001       ,		**UpperCamelCase_:      Optional[int]       ,		)     ->  List[Any]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_				)

									if depth_multiplier <= 0:
											raise ValueError('''depth_multiplier must be greater than zero.'''				)

									lowercase__				= num_channels
									lowercase__				= image_size
									lowercase__				= depth_multiplier
									lowercase__				= min_depth
									lowercase__				= hidden_act
									lowercase__				= tf_padding
									lowercase__				= classifier_dropout_prob
									lowercase__				= initializer_range
									lowercase__				= layer_norm_eps


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     List[str]     =					version.parse('''1.11'''     )

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





									"""simple docstring"""







									return OrderedDict([('''pixel_values''', {0: '''batch'''})]				)

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





									"""simple docstring"""







									if self.task == "image-classification":
											return OrderedDict([('''logits''', {0: '''batch'''})]				)
									else:
											return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]				)


							@property
							def  lowerCamelCase_					(				self:      Any				)     ->  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 lowerCAmelCase_   (					UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						cva.getAffineTransform(UpperCamelCase__					, UpperCamelCase__			)
 return cva.warpAffine(UpperCamelCase__					, UpperCamelCase__					, (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__				,   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()
