




'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


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







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Union[str, Any]   							=					['''pixel_values''']






      def __init__( self   :  str						,						lowercase__   :  bool = True						,						lowercase__   :  Dict[str, int] = None						,						lowercase__   :  PILImageResampling = PILImageResampling.BILINEAR						,						lowercase__   :  bool = True						,						lowercase__   :  Union[int, float] = 1 / 255						,						lowercase__   :  bool = True						,						lowercase__   :  Dict[str, int] = None						,						lowercase__   :  bool = True						,						**lowercase__   :  Optional[Any]						,						):


           '''simple docstring'''

           super().__init__(**lowercase__       )
           a_     :							List[Any]     						=  size if size is not None else {"""shortest_edge""": 224}
           a_     :							int     						=  get_size_dict(lowercase__						,						default_to_square=lowercase__       )
           a_     :							Any     						=  crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
           a_     :							Optional[int]     						=  get_size_dict(lowercase__						,						param_name="""crop_size"""       )

           a_     :							Optional[int]     						=  do_resize
           a_     :							int     						=  size
           a_     :							int     						=  resample
           a_     :							List[Any]     						=  do_rescale
           a_     :							Union[str, Any]     						=  rescale_factor
           a_     :							List[Any]     						=  do_center_crop
           a_     :							List[Any]     						=  crop_size
           a_     :							List[Any]     						=  do_flip_channel_order






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  np.ndarray						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  PILImageResampling = PIL.Image.BILINEAR						,						lowercase__   :  Optional[Union[str, ChannelDimension]] = None						,						**lowercase__   :  int						,						):


           '''simple docstring'''

           a_     :							Tuple     						=  get_size_dict(lowercase__						,						default_to_square=lowercase__       )
           if "shortest_edge" not in size:
                raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"       )
           a_     :							str     						=  get_resize_output_image_size(lowercase__						,						size=size["""shortest_edge"""]						,						default_to_square=lowercase__       )
           return resize(lowercase__						,						size=lowercase__						,						resample=lowercase__						,						data_format=lowercase__						,						**lowercase__       )






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


           '''simple docstring'''

           a_     :							List[Any]     						=  get_size_dict(lowercase__       )
           if "height" not in size or "width" not in size:
                raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
           return center_crop(lowercase__						,						size=(size["""height"""], size["""width"""])						,						data_format=lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  np.ndarray						,						lowercase__   :  Union[int, float]						,						lowercase__   :  Optional[Union[str, ChannelDimension]] = None						,						**lowercase__   :  Tuple						,						):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  np.ndarray						,						lowercase__   :  Optional[Union[str, ChannelDimension]] = None       ):


           '''simple docstring'''

           return flip_channel_order(lowercase__						,						data_format=lowercase__       )






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


           '''simple docstring'''

           a_     :							Tuple     						=  do_resize if do_resize is not None else self.do_resize
           a_     :							Union[str, Any]     						=  resample if resample is not None else self.resample
           a_     :							Dict     						=  do_rescale if do_rescale is not None else self.do_rescale
           a_     :							Optional[int]     						=  rescale_factor if rescale_factor is not None else self.rescale_factor
           a_     :							Union[str, Any]     						=  do_center_crop if do_center_crop is not None else self.do_center_crop
           a_     :							int     						=  (
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           a_     :							int     						=  size if size is not None else self.size
           a_     :							Dict     						=  get_size_dict(lowercase__						,						default_to_square=lowercase__       )
           a_     :							List[str]     						=  crop_size if crop_size is not None else self.crop_size
           a_     :							Dict     						=  get_size_dict(lowercase__						,						param_name="""crop_size"""       )

           a_     :							Optional[Any]     						=  make_list_of_images(lowercase__       )

           if not valid_images(lowercase__       ):
                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_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

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

           # All transformations expect numpy arrays.
           a_     :							List[str]     						=  [to_numpy_array(lowercase__       ) for image in images]

           if do_resize:
                a_     :							Tuple     						=  [self.resize(image=lowercase__						,						size=lowercase__						,						resample=lowercase__       ) for image in images]

           if do_center_crop:
                a_     :							int     						=  [self.center_crop(image=lowercase__						,						size=lowercase__       ) for image in images]

           if do_rescale:
                a_     :							Dict     						=  [self.rescale(image=lowercase__						,						scale=lowercase__       ) for image in images]

           # the pretrained checkpoints assume images are BGR, not RGB
           if do_flip_channel_order:
                a_     :							Optional[int]     						=  [self.flip_channel_order(image=lowercase__       ) for image in images]

           a_     :							Tuple     						=  [to_channel_dimension_format(lowercase__						,						lowercase__       ) for image in images]

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






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  str						,						lowercase__   :  List[Tuple] = None       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
                if len(lowercase__       ) != len(lowercase__       ):
                     raise ValueError(
                         """Make sure that you pass in as many target sizes as the batch dimension of the logits"""       )

                if is_torch_tensor(lowercase__       ):
                     a_     :							Union[str, Any]     						=  target_sizes.numpy()

                a_     :							Optional[int]     						=  []

                for idx in range(len(lowercase__       )       ):
                     a_     :							Optional[int]     						=  torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0       )						,						size=target_sizes[idx]						,						mode="""bilinear"""						,						align_corners=lowercase__       )
                     a_     :							int     						=  resized_logits[0].argmax(dim=0       )
                     semantic_segmentation.append(lowercase__       )
           else:
                a_     :							Optional[Any]     						=  logits.argmax(dim=1       )
                a_     :							List[Any]     						=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

           return semantic_segmentation






'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
