





'''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   ):


  """simple docstring"""

  _UpperCamelCase       			=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
      __lowerCamelCase						: List[Any]		=   cva.imread(
          str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
      )
      # turn image in gray scale value
      __lowerCamelCase						: List[Any]		=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      __lowerCamelCase		, __lowerCamelCase						: Any		=   gray_img.shape

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

      # add all rotated images in a list
      __lowerCamelCase						: int		=   [
          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
      __lowerCamelCase						: Optional[Any]		=   plt.figure(1)
      __lowerCamelCase						: Tuple		=   ['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=0.0_5, right=1.0, top=0.9_5)
      plt.show()













'''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


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)

__lowerCamelCase						: int		=   {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""mobilenet_v2"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Union[str, Any]=3							,	UpperCamelCase__    :					Dict=224							,	UpperCamelCase__    :					str=1.0							,	UpperCamelCase__    :					List[Any]=8							,	UpperCamelCase__    :					Union[str, Any]=8							,	UpperCamelCase__    :					str=6							,	UpperCamelCase__    :					str=32							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					str="relu6"							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					List[str]=0.8							,	UpperCamelCase__    :					List[str]=0.02							,	UpperCamelCase__    :					str=0.001							,	UpperCamelCase__    :					Dict=255							,	**UpperCamelCase__    :					Tuple							,	)     ->						List[Any]:
    super().__init__(**UpperCamelCase__  )

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

    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=depth_multiplier
    _UpperCamelCase       			=depth_divisible_by
    _UpperCamelCase       			=min_depth
    _UpperCamelCase       			=expand_ratio
    _UpperCamelCase       			=output_stride
    _UpperCamelCase       			=first_layer_is_expansion
    _UpperCamelCase       			=finegrained_output
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=tf_padding
    _UpperCamelCase       			=classifier_dropout_prob
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=layer_norm_eps
    _UpperCamelCase       			=semantic_loss_ignore_index







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				version.parse("""1.11""")


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						Mapping[str, Mapping[int, str]]:
    return OrderedDict([('''pixel_values''', {0: '''batch'''})]  )


  @property
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Mapping[str, Mapping[int, str]]:
    if self.task == "image-classification":
      return OrderedDict([('''logits''', {0: '''batch'''})]  )
    else:
      return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]  )




  @property
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						float:
    return 1E-4






