



def 							A			(  _UpperCAmelCase       :     str					)       ->				list:







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


if __name__ == "__main__":
							UpperCAmelCase__     = 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))





from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Any				,	A     :						bool = True				,	A     :						Optional[Dict[str, int]] = None				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						bool = True				,	A     :						Dict[str, int] = None				,	A     :						bool = True				,	A     :						Union[int, float] = 1 / 2_55				,	A     :						bool = True				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	**A     :						Union[str, Any]				,	)     ->					None:



						"""simple docstring"""






						super().__init__(**A)
						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 2_56}
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_center_crop
						_UpperCAmelCase	 =     crop_size
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCAmelCase	 =     image_std if image_std is not None else IMAGENET_STANDARD_STD



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Union[str, Any]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A)
						if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(A				,	size=(size['height'], size['width'])				,	data_format=A				,	**A)



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



						"""simple docstring"""






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



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



						"""simple docstring"""






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



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



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     resample if resample is not None else self.resample
						_UpperCAmelCase	 =     do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCAmelCase	 =     crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase	 =     rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCAmelCase	 =     do_normalize if do_normalize is not None else self.do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else self.image_mean
						_UpperCAmelCase	 =     image_std if image_std is not None else self.image_std

						_UpperCAmelCase	 =     make_list_of_images(A)

						if not valid_images(A):
									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.
						_UpperCAmelCase	 =     [to_numpy_array(A) for image in images]

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

						if do_center_crop:
									_UpperCAmelCase	 =     [self.center_crop(image=A				,	size=A) for image in images]

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

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

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {'pixel_values': images}
						return BatchFeature(data=A				,	tensor_type=A)



			def 					_lowerCamelCase (		self     :						str				,	A     :						Any				,	A     :						List[Tuple] = None)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(A) != len(A):
												raise ValueError(
												    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

									if is_torch_tensor(A):
												_UpperCAmelCase	 =     target_sizes.numpy()

									_UpperCAmelCase	 =     []

									for idx in range(len(A)):
												_UpperCAmelCase	 =     torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0)				,	size=target_sizes[idx]				,	mode='bilinear'				,	align_corners=A)
												_UpperCAmelCase	 =     resized_logits[0].argmax(dim=0)
												semantic_segmentation.append(A)
						else:
									_UpperCAmelCase	 =     logits.argmax(dim=1)
									_UpperCAmelCase	 =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

						return semantic_segmentation
