

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


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


if is_vision_available():
     import PIL



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      Optional[Any]					=   ['''pixel_values''']





  def __init__(  self   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = 1 / 255   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = True   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      super().__init__(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		size if size is not None else {"""shortest_edge""": 224}
      snake_case							:		Union[str, Any]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      snake_case							:		Union[str, Any]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,default_to_square=SCREAMING_SNAKE_CASE_   ,param_name="""crop_size"""    )

      snake_case							:		Tuple        =		do_resize
      snake_case							:		int        =		size
      snake_case							:		int        =		resample
      snake_case							:		int        =		do_center_crop
      snake_case							:		Any        =		crop_size
      snake_case							:		Optional[int]        =		do_rescale
      snake_case							:		Dict        =		rescale_factor
      snake_case							:		Dict        =		do_normalize
      snake_case							:		Optional[int]        =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      snake_case							:		Any        =		image_std if image_std is not None else OPENAI_CLIP_STD
      snake_case							:		Any        =		do_convert_rgb





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		List[str]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      if "shortest_edge" not in size:
          raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
      snake_case							:		Any        =		get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,size=size["""shortest_edge"""]   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      return resize(SCREAMING_SNAKE_CASE_   ,size=SCREAMING_SNAKE_CASE_   ,resample=SCREAMING_SNAKE_CASE_   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		List[Any]        =		get_size_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_   ,size=(size["""height"""], size["""width"""])   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      return rescale(SCREAMING_SNAKE_CASE_   ,scale=SCREAMING_SNAKE_CASE_   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      return normalize(SCREAMING_SNAKE_CASE_   ,mean=SCREAMING_SNAKE_CASE_   ,std=SCREAMING_SNAKE_CASE_   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		Any        =		do_resize if do_resize is not None else self.do_resize
      snake_case							:		Optional[int]        =		size if size is not None else self.size
      snake_case							:		Union[str, Any]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,param_name="""size"""   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		resample if resample is not None else self.resample
      snake_case							:		int        =		do_center_crop if do_center_crop is not None else self.do_center_crop
      snake_case							:		Tuple        =		crop_size if crop_size is not None else self.crop_size
      snake_case							:		List[str]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,param_name="""crop_size"""   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Union[str, Any]        =		do_rescale if do_rescale is not None else self.do_rescale
      snake_case							:		List[Any]        =		rescale_factor if rescale_factor is not None else self.rescale_factor
      snake_case							:		List[str]        =		do_normalize if do_normalize is not None else self.do_normalize
      snake_case							:		List[Any]        =		image_mean if image_mean is not None else self.image_mean
      snake_case							:		Optional[Any]        =		image_std if image_std is not None else self.image_std
      snake_case							:		Optional[Any]        =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      snake_case							:		Dict        =		make_list_of_images(SCREAMING_SNAKE_CASE_    )

      if not valid_images(SCREAMING_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:
          snake_case							:		List[str]        =		[convert_to_rgb(SCREAMING_SNAKE_CASE_    ) for image in images]

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

      if do_resize:
          snake_case							:		str        =		[self.resize(image=SCREAMING_SNAKE_CASE_   ,size=SCREAMING_SNAKE_CASE_   ,resample=SCREAMING_SNAKE_CASE_    ) for image in images]

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

      if do_rescale:
          snake_case							:		int        =		[self.rescale(image=SCREAMING_SNAKE_CASE_   ,scale=SCREAMING_SNAKE_CASE_    ) for image in images]

      if do_normalize:
          snake_case							:		str        =		[self.normalize(image=SCREAMING_SNAKE_CASE_   ,mean=SCREAMING_SNAKE_CASE_   ,std=SCREAMING_SNAKE_CASE_    ) for image in images]

      snake_case							:		Optional[Any]        =		[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) for image in images]

      snake_case							:		List[Any]        =		{"""pixel_values""": images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,tensor_type=SCREAMING_SNAKE_CASE_    )









def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			list:





				"""simple docstring"""


				if n_term == "":
								return []
				__lowerCamelCase									=       []
				for temp in range(int(UpperCamelCase__ ) ):
								series.append(F"""1/{temp + 1}""" if series else '1' )
				return series


if __name__ == "__main__":
					__A								=       input("Enter the last number (nth term) of the Harmonic Series")
					print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
					print(harmonic_series(nth_term))


