

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,
    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_vision_available, logging


if is_vision_available():
					import PIL


a		=logging.get_logger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Dict						      =      ['''pixel_values''']
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			float = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BILINEAR   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Union[int, float] = 1 / 2_5_5   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,**SCREAMING_SNAKE_CASE__		:			Tuple   ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = size if size is not None else {'shortest_edge': 3_8_4}
								__lowerCamelCase				:				Optional[int]					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Any					       = do_resize
								__lowerCamelCase				:				Optional[Any]					       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowerCamelCase				:				Optional[int]					       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowerCamelCase				:				str					       = resample
								__lowerCamelCase				:				Optional[int]					       = do_rescale
								__lowerCamelCase				:				int					       = rescale_factor
								__lowerCamelCase				:				Union[str, Any]					       = do_normalize
								__lowerCamelCase				:				Tuple					       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowerCamelCase				:				Dict					       = image_std if image_std is not None else IMAGENET_STANDARD_STD
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			float   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Optional[int]   ,):
								__lowerCamelCase				:				Optional[Any]					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								if "shortest_edge" not in size:
									raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}")
								__lowerCamelCase				:				List[str]					       = size['shortest_edge']

								if shortest_edge < 3_8_4:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									__lowerCamelCase				:				Tuple					       = int(shortest_edge / crop_pct)
									__lowerCamelCase				:				Any					       = get_resize_output_image_size(SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				Union[str, Any]					       = resize(image=SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,resample=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
									# then crop to (shortest_edge, shortest_edge)
									return center_crop(image=SCREAMING_SNAKE_CASE__   ,size=(shortest_edge, shortest_edge)   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
								else:
									# warping (no cropping) when evaluated at 384 or larger
									return resize(
									    SCREAMING_SNAKE_CASE__   ,size=(shortest_edge, shortest_edge)   ,resample=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[int, float]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			int   ,):
								return rescale(SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[str]   ,):
								return normalize(SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			ImageInput   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			float = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			float = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, TensorType]] = None   ,SCREAMING_SNAKE_CASE__		:			ChannelDimension = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE__		:			Any   ,):
								__lowerCamelCase				:				Optional[Any]					       = do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase				:				str					       = crop_pct if crop_pct is not None else self.crop_pct
								__lowerCamelCase				:				int					       = resample if resample is not None else self.resample
								__lowerCamelCase				:				Tuple					       = do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase				:				int					       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase				:				Optional[int]					       = do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase				:				str					       = image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase				:				Any					       = image_std if image_std is not None else self.image_std

								__lowerCamelCase				:				List[Any]					       = size if size is not None else self.size
								__lowerCamelCase				:				Union[str, Any]					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = 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 or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.')

								if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
									raise ValueError('crop_pct must be specified if size < 384.')

								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.
								__lowerCamelCase				:				Tuple					       = [to_numpy_array(SCREAMING_SNAKE_CASE__) for image in images]

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

								if do_rescale:
									__lowerCamelCase				:				List[Any]					       = [self.rescale(image=SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase				:				Union[str, Any]					       = [self.normalize(image=SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				List[str]					       = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Tuple					       = {'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__   ,tensor_type=SCREAMING_SNAKE_CASE__)










def      A      (      _SCREAMING_SNAKE_CASE    )	-> list:
       if n_term == "":
              return []
       lowerCamelCase   :      list			 =					[]
       for temp in range(int(_SCREAMING_SNAKE_CASE    )    ):
              series.append(f'''1/{temp + 1}''' if series else "1"    )
       return series


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Dict  =   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))






