



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

import numpy as np

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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Dict			       =    ["pixel_values"]



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






								"""simple docstring"""


								super().__init__(**_snake_case					)
								A__       =    size if size is not None else {'shortest_edge': 2_24}
								A__       =    get_size_dict(_snake_case				,					default_to_square=_snake_case					)
								A__       =    crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								A__       =    get_size_dict(_snake_case				,					param_name='crop_size'					)

								A__       =    do_resize
								A__       =    size
								A__       =    resample
								A__       =    do_center_crop
								A__       =    crop_size
								A__       =    do_rescale
								A__       =    rescale_factor
								A__       =    do_normalize
								A__       =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								A__       =    image_std if image_std is not None else IMAGENET_DEFAULT_STD



					def     _a							(     self					:       str				,					_snake_case					:       np.ndarray				,					_snake_case					:       Dict[str, int]				,					_snake_case					:       PILImageResampling = PILImageResampling.BICUBIC				,					_snake_case					:       Optional[Union[str, ChannelDimension]] = None				,					**_snake_case					:       List[Any]				,					):






								"""simple docstring"""


								A__       =    get_size_dict(_snake_case				,					default_to_square=_snake_case					)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
											A__       =    int((2_56 / 2_24) * size['shortest_edge']					)
											A__       =    get_resize_output_image_size(_snake_case				,					size=_snake_case				,					default_to_square=_snake_case					)
											A__       =    {'height': output_size[0], 'width': output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
											raise ValueError(
											    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''					)
								return resize(
								    _snake_case				,					size=(size_dict['height'], size_dict['width'])				,					resample=_snake_case				,					data_format=_snake_case				,					**_snake_case					)



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






								"""simple docstring"""


								A__       =    get_size_dict(_snake_case					)
								if "height" not in size or "width" not in size:
											raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
								return center_crop(_snake_case				,					size=(size['height'], size['width'])				,					data_format=_snake_case				,					**_snake_case					)



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






								"""simple docstring"""


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



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






								"""simple docstring"""


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



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






								"""simple docstring"""


								A__       =    do_resize if do_resize is not None else self.do_resize
								A__       =    resample if resample is not None else self.resample
								A__       =    do_center_crop if do_center_crop is not None else self.do_center_crop
								A__       =    do_rescale if do_rescale is not None else self.do_rescale
								A__       =    rescale_factor if rescale_factor is not None else self.rescale_factor
								A__       =    do_normalize if do_normalize is not None else self.do_normalize
								A__       =    image_mean if image_mean is not None else self.image_mean
								A__       =    image_std if image_std is not None else self.image_std

								A__       =    size if size is not None else self.size
								A__       =    get_size_dict(_snake_case				,					default_to_square=_snake_case					)
								A__       =    crop_size if crop_size is not None else self.crop_size
								A__       =    get_size_dict(_snake_case				,					param_name='crop_size'					)

								A__       =    make_list_of_images(_snake_case					)

								if not valid_images(_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.'					)

								# All transformations expect numpy arrays.
								A__       =    [to_numpy_array(_snake_case					) for image in images]

								if do_resize:
											A__       =    [self.resize(_snake_case				,					_snake_case				,					_snake_case					) for image in images]

								if do_center_crop:
											A__       =    [self.center_crop(_snake_case				,					_snake_case					) for image in images]

								if do_rescale:
											A__       =    [self.rescale(_snake_case				,					_snake_case					) for image in images]

								if do_normalize:
											A__       =    [self.normalize(_snake_case				,					_snake_case				,					_snake_case					) for image in images]

								A__       =    [to_channel_dimension_format(_snake_case				,					_snake_case					) for image in images]

								A__       =    {'pixel_values': images}
								return BatchFeature(data=_snake_case				,					tensor_type=_snake_case					)









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    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()

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




