






'''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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


if is_vision_available():
      import PIL
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






      def __init__( self			:		str     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BICUBIC     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[int, float] = 1 / 255     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[Union[float, List[float]]] = None     ,      UpperCAmelCase			:		Optional[Union[float, List[float]]] = None     ,      UpperCAmelCase			:		bool = True     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =size if size is not None else {'''shortest_edge''': 224}
       lowercase					:   List[str]				  =get_size_dict(UpperCAmelCase     ,      default_to_square=UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase					:   int				  =get_size_dict(UpperCAmelCase     ,      default_to_square=UpperCAmelCase     ,      param_name='''crop_size'''		)

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






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


       '''simple docstring'''

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Dict[str, int]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Dict     ,      )  ->  np.ndarray:


       '''simple docstring'''

       lowercase					:   Dict				  =get_size_dict(UpperCAmelCase		)
       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(UpperCAmelCase     ,      size=(size['''height'''], size['''width'''])     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[int, float]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		str     ,      )  ->  List[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[float, List[float]]     ,      UpperCAmelCase			:		Union[float, List[float]]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		List[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

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






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		ImageInput     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		PILImageResampling = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		int = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		float = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[Union[float, List[float]]] = None     ,      UpperCAmelCase			:		Optional[Union[float, List[float]]] = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		Optional[ChannelDimension] = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		Dict     ,      )  ->  PIL.Image.Image:


       '''simple docstring'''

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

       lowercase					:   Optional[int]				  =make_list_of_images(UpperCAmelCase		)

       if not valid_images(UpperCAmelCase		):
        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:
        lowercase					:   Dict				  =[convert_to_rgb(UpperCAmelCase		) for image in images]

       # All transformations expect numpy arrays.
       lowercase					:   Union[str, Any]				  =[to_numpy_array(UpperCAmelCase		) for image in images]

       if do_resize:
        lowercase					:   Optional[Any]				  =[self.resize(image=UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase		) for image in images]

       if do_center_crop:
        lowercase					:   Optional[int]				  =[self.center_crop(image=UpperCAmelCase     ,      size=UpperCAmelCase		) for image in images]

       if do_rescale:
        lowercase					:   Tuple				  =[self.rescale(image=UpperCAmelCase     ,      scale=UpperCAmelCase		) for image in images]

       if do_normalize:
        lowercase					:   Dict				  =[self.normalize(image=UpperCAmelCase     ,      mean=UpperCAmelCase     ,      std=UpperCAmelCase		) for image in images]

       lowercase					:   Tuple				  =[to_channel_dimension_format(UpperCAmelCase     ,      UpperCAmelCase		) for image in images]

       lowercase					:   Optional[Any]				  ={'''pixel_values''': images}
       return BatchFeature(data=UpperCAmelCase     ,      tensor_type=UpperCAmelCase		)











'''simple docstring'''



from __future__ import annotations

import math


def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 lowercase					:   str				  =u
 for i in range(1					,      __A			):
  lowercase					:   Any				  =temp * (u - i)
 return temp


def 							lowercase_				(    )       ->      None:



 """simple docstring"""




 lowercase					:   List[str]				  =int(input('''enter the numbers of values: '''			)			)
 lowercase					:   list[list[float]]				  =[]
 for _ in range(__A			):
  y.append([]			)
 for i in range(__A			):
  for j in range(__A			):
   y[i].append(__A			)
   lowercase					:   List[Any]				  =0

 print('''enter the values of parameters in a list: '''			)
 lowercase					:   Optional[int]				  =list(map(__A					,      input().split()			)			)

 print('''enter the values of corresponding parameters: '''			)
 for i in range(__A			):
  lowercase					:   str				  =float(input()			)

 lowercase					:   int				  =int(input('''enter the value to interpolate: '''			)			)
 lowercase					:   Union[str, Any]				  =(value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1					,      __A			):
  for j in range(n - i			):
   lowercase					:   str				  =y[j + 1][i - 1] - y[j][i - 1]

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

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


if __name__ == "__main__":
      main()



