






'''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


__A							:							Optional[int]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"mobilenet_v1"

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Tuple=3				,     __lowerCamelCase     :     Any=224				,     __lowerCamelCase     :     Tuple=1.0				,     __lowerCamelCase     :     Optional[int]=8				,     __lowerCamelCase     :     str="relu6"				,     __lowerCamelCase     :     Any=True				,     __lowerCamelCase     :     Union[str, Any]=0.9_9_9				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     str=0.0_0_1				,     **__lowerCamelCase     :     str				,     )							->							Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

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

     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     depth_multiplier
     lowerCamelCase__  =     min_depth
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     tf_padding
     lowerCamelCase__  =     classifier_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

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

     '''simple docstring'''




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

    @property
    def    a__				(					self     :     str     )							->							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    a__				(					self     :     List[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_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


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

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], 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							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""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()

