




"""simple docstring"""




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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


lowerCAmelCase							:    Tuple  							=							logging.get_logger(__name__)






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		["pixel_values"]





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





							"""simple docstring"""
							super().__init__(**_a				)
							lowerCamelCase       =      size if size is not None else {"""shortest_edge""": 224}
							lowerCamelCase       =      get_size_dict(_a	,				default_to_square=_a				)
							lowerCamelCase       =      crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
							lowerCamelCase       =      get_size_dict(_a	,				param_name="""crop_size"""				)

							lowerCamelCase       =      do_resize
							lowerCamelCase       =      size
							lowerCamelCase       =      resample
							lowerCamelCase       =      do_rescale
							lowerCamelCase       =      rescale_factor
							lowerCamelCase       =      do_center_crop
							lowerCamelCase       =      crop_size
							lowerCamelCase       =      do_flip_channel_order





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = PIL.Image.BILINEAR	,				_a = None	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      get_size_dict(_a	,				default_to_square=_a				)
							if "shortest_edge" not in size:
										raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'				)
							lowerCamelCase       =      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	,				_a	,				_a	,				_a = None	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      get_size_dict(_a				)
							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 center_crop(_a	,				size=(size["""height"""], size["""width"""])	,				data_format=_a	,				**_a				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = None	,				**_a	,				):





							"""simple docstring"""
							return rescale(_a	,				scale=_a	,				data_format=_a	,				**_a				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = None				):





							"""simple docstring"""
							return flip_channel_order(_a	,				data_format=_a				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = None	,				_a = None	,				_a = None	,				_a = None	,				_a = None	,				_a = None	,				_a = None	,				_a = None	,				_a = None	,				_a = ChannelDimension.FIRST	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      do_resize if do_resize is not None else self.do_resize
							lowerCamelCase       =      resample if resample is not None else self.resample
							lowerCamelCase       =      do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase       =      rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase       =      do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase       =      (
							    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
							)

							lowerCamelCase       =      size if size is not None else self.size
							lowerCamelCase       =      get_size_dict(_a	,				default_to_square=_a				)
							lowerCamelCase       =      crop_size if crop_size is not None else self.crop_size
							lowerCamelCase       =      get_size_dict(_a	,				param_name="""crop_size"""				)

							lowerCamelCase       =      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_rescale and rescale_factor is None:
										raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

							if do_center_crop and crop_size is None:
										raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

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

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

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

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

							# the pretrained checkpoints assume images are BGR, not RGB
							if do_flip_channel_order:
										lowerCamelCase       =      [self.flip_channel_order(image=_a				) for image in images]

							lowerCamelCase       =      [to_channel_dimension_format(_a	,				_a				) for image in images]

							lowerCamelCase       =      {"""pixel_values""": images}
							return BatchFeature(data=_a	,				tensor_type=_a				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = None				):





							"""simple docstring"""
							lowerCamelCase       =      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				):
													lowerCamelCase       =      target_sizes.numpy()

										lowerCamelCase       =      []

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

							return semantic_segmentation









"""simple docstring"""




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


if __name__ == "__main__":
					lowerCAmelCase							:    Optional[int]  							=							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))



