






"""simple docstring"""





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							=  logging.get_logger(__name__)


if is_vision_available():
							import PIL



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["pixel_values"]

					def __init__(			self  :      Optional[int]     ,		__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_5_5     ,		__snake_case  :      bool = True     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      bool = True     ,		**__snake_case  :      List[str]     ,		)			->							None:
											super().__init__(**__snake_case					)
											__magic_name__:	Tuple							=       size if size is not None else {"""shortest_edge""": 2_2_4}
											__magic_name__:	str							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											__magic_name__:	Dict							=       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											__magic_name__:	Union[str, Any]							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case     ,		param_name="""crop_size"""					)

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

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      PILImageResampling = PILImageResampling.BICUBIC     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      str     ,		)			->							np.ndarray:
											__magic_name__:	Union[str, Any]							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											if "shortest_edge" not in size:
																	raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'					)
											__magic_name__:	List[str]							=       get_resize_output_image_size(__snake_case     ,		size=size["""shortest_edge"""]     ,		default_to_square=__snake_case					)
											return resize(__snake_case     ,		size=__snake_case     ,		resample=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Tuple     ,		)			->							np.ndarray:
											__magic_name__:	str							=       get_size_dict(__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(__snake_case     ,		size=(size["""height"""], size["""width"""])     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[int, float]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      List[str]     ,		)			->							Optional[Any]:
											return rescale(__snake_case     ,		scale=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      str     ,		__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  :      Optional[int]     ,		)			->							np.ndarray:
											return normalize(__snake_case     ,		mean=__snake_case     ,		std=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)






					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      int = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      float = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		__snake_case  :      Optional[ChannelDimension] = ChannelDimension.FIRST     ,		**__snake_case  :      Optional[Any]     ,		)			->							PIL.Image.Image:
											__magic_name__:	List[str]							=       do_resize if do_resize is not None else self.do_resize
											__magic_name__:	Tuple							=       size if size is not None else self.size
											__magic_name__:	Any							=       get_size_dict(__snake_case     ,		param_name="""size"""     ,		default_to_square=__snake_case					)
											__magic_name__:	Dict							=       resample if resample is not None else self.resample
											__magic_name__:	Optional[int]							=       do_center_crop if do_center_crop is not None else self.do_center_crop
											__magic_name__:	int							=       crop_size if crop_size is not None else self.crop_size
											__magic_name__:	List[Any]							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""     ,		default_to_square=__snake_case					)
											__magic_name__:	List[Any]							=       do_rescale if do_rescale is not None else self.do_rescale
											__magic_name__:	Tuple							=       rescale_factor if rescale_factor is not None else self.rescale_factor
											__magic_name__:	int							=       do_normalize if do_normalize is not None else self.do_normalize
											__magic_name__:	Optional[int]							=       image_mean if image_mean is not None else self.image_mean
											__magic_name__:	Any							=       image_std if image_std is not None else self.image_std
											__magic_name__:	str							=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											__magic_name__:	str							=       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."""					)

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																	__magic_name__:	int							=       [convert_to_rgb(__snake_case					) for image in images]

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

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

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

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

											if do_normalize:
																	__magic_name__:	int							=       [self.normalize(image=__snake_case     ,		mean=__snake_case     ,		std=__snake_case					) for image in images]

											__magic_name__:	List[Any]							=       [to_channel_dimension_format(__snake_case     ,		__snake_case					) for image in images]

											__magic_name__:	Tuple							=       {"""pixel_values""": images}
											return BatchFeature(data=__snake_case     ,		tensor_type=__snake_case					)








'''simple docstring'''





from __future__ import annotations

import math




def 							lowerCamelCase	(					_snake_case	:    float  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__									=    u
    for i in range(1  ,_snake_case							):
        lowercase__									=    temp * (u - i)
    return temp




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    int(input("enter the numbers of values: "							)							)
    lowercase__									=    []
    for _ in range(_snake_case							):
        y.append([]							)
    for i in range(_snake_case							):
        for j in range(_snake_case							):
            y[i].append(_snake_case							)
            lowercase__									=    0

    print("enter the values of parameters in a list: "							)
    lowercase__									=    list(map(_snake_case  ,input().split()							)							)

    print("enter the values of corresponding parameters: "							)
    for i in range(_snake_case							):
        lowercase__									=    float(input()							)

    lowercase__									=    int(input("enter the value to interpolate: "							)							)
    lowercase__									=    (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1  ,_snake_case							):
        for j in range(n - i							):
            lowercase__									=    y[j + 1][i - 1] - y[j][i - 1]

    lowercase__									=    y[0][0]
    for i in range(1  ,_snake_case							):
        summ += (ucal(_snake_case  ,_snake_case							) * y[0][i]) / math.factorial(_snake_case							)

    print(f'''the value at {value} is {summ}'''							)


if __name__ == "__main__":
 main()




