



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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:  Optional[int]										=  logging.get_logger(__name__)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      ['''pixel_values''']

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


    """simple docstring"""






    super().__init__(**__lowerCAmelCase				)
    a							=		size if size is not None else {"height": 224, "width": 224}
    a							=		get_size_dict(__lowerCAmelCase				)
    a							=		do_resize
    a							=		do_rescale
    a							=		do_normalize
    a							=		size
    a							=		resample
    a							=		rescale_factor
    a							=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    a							=		image_std if image_std is not None else IMAGENET_STANDARD_STD

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							np.ndarray				, __lowerCAmelCase      :							Dict[str, int]				, __lowerCAmelCase      :							PILImageResampling = PILImageResampling.BILINEAR				, __lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None				, **__lowerCAmelCase      :							int				, )							->					np.ndarray:


    """simple docstring"""






    a							=		get_size_dict(__lowerCAmelCase				)
    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 resize(
        __lowerCAmelCase				, size=(size["height"], size["width"])				, resample=__lowerCAmelCase				, data_format=__lowerCAmelCase				, **__lowerCAmelCase				)

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							np.ndarray				, __lowerCAmelCase      :							float				, __lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None				, **__lowerCAmelCase      :							Tuple				)							->					np.ndarray:


    """simple docstring"""






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

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


    """simple docstring"""






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




   def 		A				(   self      :							str				, __lowerCAmelCase      :							ImageInput				, __lowerCAmelCase      :							Optional[bool] = None				, __lowerCAmelCase      :							Dict[str, int] = None				, __lowerCAmelCase      :							PILImageResampling = None				, __lowerCAmelCase      :							Optional[bool] = None				, __lowerCAmelCase      :							Optional[float] = None				, __lowerCAmelCase      :							Optional[bool] = None				, __lowerCAmelCase      :							Optional[Union[float, List[float]]] = None				, __lowerCAmelCase      :							Optional[Union[float, List[float]]] = None				, __lowerCAmelCase      :							Optional[Union[str, TensorType]] = None				, __lowerCAmelCase      :							Union[str, ChannelDimension] = ChannelDimension.FIRST				, **__lowerCAmelCase      :							Optional[int]				, )							->					List[Any]:


    """simple docstring"""






    a							=		do_resize if do_resize is not None else self.do_resize
    a							=		do_rescale if do_rescale is not None else self.do_rescale
    a							=		do_normalize if do_normalize is not None else self.do_normalize
    a							=		resample if resample is not None else self.resample
    a							=		rescale_factor if rescale_factor is not None else self.rescale_factor
    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(__lowerCAmelCase				)

    a							=		make_list_of_images(__lowerCAmelCase				)

    if not valid_images(__lowerCAmelCase				):
     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."				)

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

    if do_resize:
     a							=		[self.resize(image=__lowerCAmelCase				, size=__lowerCAmelCase				, resample=__lowerCAmelCase				) for image in images]

    if do_rescale:
     a							=		[self.rescale(image=__lowerCAmelCase				, scale=__lowerCAmelCase				) for image in images]

    if do_normalize:
     a							=		[self.normalize(image=__lowerCAmelCase				, mean=__lowerCAmelCase				, std=__lowerCAmelCase				) for image in images]

    a							=		[to_channel_dimension_format(__lowerCAmelCase				, __lowerCAmelCase				) for image in images]

    a							=		{"pixel_values": images}
    return BatchFeature(data=__lowerCAmelCase				, tensor_type=__lowerCAmelCase				)






from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       ):






 '''simple docstring'''

 if not all((nth_term, start_term_a, common_ratio_r)       ):
  return []
 a							=		[]
 a							=		1
 a							=		common_ratio_r
 for _ in range(int(UpperCAmelCase__       )       ):
  if not series:
   series.append(UpperCAmelCase__       )
  else:
   power += 1
   series.append(float(start_term_a * multiple       )       )
   a							=		pow(float(UpperCAmelCase__       )			,       UpperCAmelCase__       )
 return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  Optional[Any]										=  float(input('''Enter the last number (n term) of the Geometric Series'''))
     A_		:  Optional[Any]										=  float(input('''Enter the starting term (a) of the Geometric Series'''))
     A_		:  Optional[int]										=  float(
         input('''Enter the common ratio between two terms (r) of the Geometric Series''')
     )
     print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))

