





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


_A :   List[Any]								=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			__SCREAMING_SNAKE_CASE ):
       _UpperCAmelCase				:  Optional[Any]			   =   ['pixel_values']







       def __init__(				self					:     Optional[int]		, A					:     Any = True		, A					:     List[Any] = None		, A					:     Optional[Any] = PILImageResampling.BILINEAR		, A					:     str = True		, A					:     Optional[Any] = 1 / 2_5_5		, A					:     List[Any] = True		, A					:     Any = None		, A					:     List[str] = True		, **A					:     Any		, )       ->Optional[int]:
          super().__init__(**_a      )
          lowerCamelCase__       :						List[str]        =							size if size is not None else {'''shortest_edge''': 2_2_4}
          lowerCamelCase__       :						Tuple        =							get_size_dict(_a		, default_to_square=_a      )
          lowerCamelCase__       :						Optional[Any]        =							crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
          lowerCamelCase__       :						List[Any]        =							get_size_dict(_a		, param_name='''crop_size'''      )

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







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     List[str]		, A					:     Tuple = PIL.Image.BILINEAR		, A					:     Dict = None		, **A					:     Dict		, )       ->Optional[Any]:
          lowerCamelCase__       :						List[Any]        =							get_size_dict(_a		, default_to_square=_a      )
          if "shortest_edge" not in size:
             raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"      )
          lowerCamelCase__       :						Optional[Any]        =							get_resize_output_image_size(_a		, size=size['''shortest_edge''']		, default_to_square=_a      )
          return resize(_a		, size=_a		, resample=_a		, data_format=_a		, **_a      )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     str		, A					:     str		, A					:     Optional[int] = None		, **A					:     str		, )       ->Optional[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							get_size_dict(_a      )
          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(_a		, size=(size['''height'''], size['''width'''])		, data_format=_a		, **_a      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     Optional[Any]		, A					:     int		, A					:     Union[str, Any] = None		, **A					:     Tuple		, )       ->Optional[int]:
          return rescale(_a		, scale=_a		, data_format=_a		, **_a      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     Dict		, A					:     Any = None      )       ->Any:
          return flip_channel_order(_a		, data_format=_a      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Optional[Any]		, A					:     int = None		, A					:     List[Any] = None		, A					:     List[Any] = None		, A					:     Optional[int] = None		, A					:     List[Any] = None		, A					:     List[str] = None		, A					:     Optional[int] = None		, A					:     str = None		, A					:     Union[str, Any] = None		, A					:     str = ChannelDimension.FIRST		, **A					:     Dict		, )       ->List[str]:
          lowerCamelCase__       :						Any        =							do_resize if do_resize is not None else self.do_resize
          lowerCamelCase__       :						int        =							resample if resample is not None else self.resample
          lowerCamelCase__       :						Optional[Any]        =							do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase__       :						Any        =							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase__       :						List[Any]        =							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowerCamelCase__       :						Optional[int]        =							(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          lowerCamelCase__       :						str        =							size if size is not None else self.size
          lowerCamelCase__       :						Optional[Any]        =							get_size_dict(_a		, default_to_square=_a      )
          lowerCamelCase__       :						Dict        =							crop_size if crop_size is not None else self.crop_size
          lowerCamelCase__       :						Dict        =							get_size_dict(_a		, param_name='''crop_size'''      )

          lowerCamelCase__       :						Optional[int]        =							make_list_of_images(_a      )

          if not valid_images(_a      ):
             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.
          lowerCamelCase__       :						Union[str, Any]        =							[to_numpy_array(_a      ) for image in images]

          if do_resize:
             lowerCamelCase__       :						Any        =							[self.resize(image=_a		, size=_a		, resample=_a      ) for image in images]

          if do_center_crop:
             lowerCamelCase__       :						Any        =							[self.center_crop(image=_a		, size=_a      ) for image in images]

          if do_rescale:
             lowerCamelCase__       :						str        =							[self.rescale(image=_a		, scale=_a      ) for image in images]

          # the pretrained checkpoints assume images are BGR, not RGB
          if do_flip_channel_order:
             lowerCamelCase__       :						List[Any]        =							[self.flip_channel_order(image=_a      ) for image in images]

          lowerCamelCase__       :						int        =							[to_channel_dimension_format(_a		, _a      ) for image in images]

          lowerCamelCase__       :						Union[str, Any]        =							{'''pixel_values''': images}
          return BatchFeature(data=_a		, tensor_type=_a      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     List[Any]		, A					:     int = None      )       ->List[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							outputs.logits

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

             if is_torch_tensor(_a      ):
                lowerCamelCase__       :						List[Any]        =							target_sizes.numpy()

             lowerCamelCase__       :						int        =							[]

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

          return semantic_segmentation










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







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


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, 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()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



