






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
 import PIL





class 			__a  (    A__       ):
 _lowerCAmelCase       :       int        = ['''pixel_values''']







 def __init__(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BICUBIC							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Union[int, float] = 1 / 2_55							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   **SCREAMING_SNAKE_CASE					:   Tuple							,   ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  size if size is not None else {"shortest_edge": 2_24}
      UpperCamelCase__			:   Any       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
      UpperCamelCase__			:   int       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   Optional[Any]       				=  do_resize
      UpperCamelCase__			:   Dict       				=  size
      UpperCamelCase__			:   str       				=  resample
      UpperCamelCase__			:   Optional[int]       				=  do_center_crop
      UpperCamelCase__			:   Any       				=  crop_size
      UpperCamelCase__			:   str       				=  do_rescale
      UpperCamelCase__			:   List[str]       				=  rescale_factor
      UpperCamelCase__			:   Optional[int]       				=  do_normalize
      UpperCamelCase__			:   Dict       				=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      UpperCamelCase__			:   str       				=  image_std if image_std is not None else OPENAI_CLIP_STD
      UpperCamelCase__			:   Union[str, Any]       				=  do_convert_rgb







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BICUBIC							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   str							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      if "shortest_edge" not in size:
           raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
      UpperCamelCase__			:   Optional[int]       				=  get_resize_output_image_size(SCREAMING_SNAKE_CASE							,   size=size["shortest_edge"]							,   default_to_square=SCREAMING_SNAKE_CASE						)
      return resize(SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  get_size_dict(SCREAMING_SNAKE_CASE						)
      if "height" not in size or "width" not in size:
           raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'						)
      return center_crop(SCREAMING_SNAKE_CASE							,   size=(size["height"], size["width"])							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[int, float]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   str							,   ):


      '''simple docstring'''






      return rescale(SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[float, List[float]]							,   SCREAMING_SNAKE_CASE					:   Union[float, List[float]]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Any							,   ):


      '''simple docstring'''






      return normalize(SCREAMING_SNAKE_CASE							,   mean=SCREAMING_SNAKE_CASE							,   std=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   ImageInput							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   int = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   float = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[float, List[float]]] = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, TensorType]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[ChannelDimension] = ChannelDimension.FIRST							,   **SCREAMING_SNAKE_CASE					:   Dict							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__			:   Optional[Any]       				=  size if size is not None else self.size
      UpperCamelCase__			:   List[str]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="size"							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  resample if resample is not None else self.resample
      UpperCamelCase__			:   List[str]       				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__			:   List[Any]       				=  crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__			:   Optional[int]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__			:   int       				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__			:   List[Any]       				=  do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__			:   Tuple       				=  image_mean if image_mean is not None else self.image_mean
      UpperCamelCase__			:   Optional[Any]       				=  image_std if image_std is not None else self.image_std
      UpperCamelCase__			:   Optional[int]       				=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      UpperCamelCase__			:   Optional[Any]       				=  make_list_of_images(SCREAMING_SNAKE_CASE						)

      if not valid_images(SCREAMING_SNAKE_CASE						):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."						)

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."						)

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."						)

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."						)

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."						)

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
           UpperCamelCase__			:   Union[str, Any]       				=  [convert_to_rgb(SCREAMING_SNAKE_CASE						) for image in images]

      # All transformations expect numpy arrays.
      UpperCamelCase__			:   int       				=  [to_numpy_array(SCREAMING_SNAKE_CASE						) for image in images]

      if do_resize:
           UpperCamelCase__			:   Union[str, Any]       				=  [self.resize(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE						) for image in images]

      if do_center_crop:
           UpperCamelCase__			:   Tuple       				=  [self.center_crop(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE						) for image in images]

      if do_rescale:
           UpperCamelCase__			:   Union[str, Any]       				=  [self.rescale(image=SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE						) for image in images]

      if do_normalize:
           UpperCamelCase__			:   Union[str, Any]       				=  [self.normalize(image=SCREAMING_SNAKE_CASE							,   mean=SCREAMING_SNAKE_CASE							,   std=SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Union[str, Any]       				=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   List[Any]       				=  {"pixel_values": images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE							,   tensor_type=SCREAMING_SNAKE_CASE						)







import numpy as np







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return 1 / (1 + np.exp(-vector			))







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return vector * sigmoid(1.7_0_2 * vector			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()