def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							int(_lowercase		)
						if n_element < 1:
												SCREAMING_SNAKE_CASE							:					Dict           =							ValueError('''a should be a positive number'''		)
												raise my_error

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							[1]
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							(0, 0, 0)
						SCREAMING_SNAKE_CASE							:					int           =							1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2	,						hamming_list[j] * 3	,						hamming_list[k] * 5		)		)
												index += 1
						return hamming_list


if __name__ == "__main__":
					__UpperCamelCase	: List[str]    		=  input('Enter the last number (nth term) of the Hamming Number Series: ')
					print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
					__UpperCamelCase	: Dict    		=  hamming(int(n))
					print('-----------------------------------------------------')
					print(f"""The list with nth numbers is: {hamming_numbers}""")
					print('-----------------------------------------------------')






import math
from typing import Dict, Iterable, List, Optional, Tuple, 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,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


__UpperCamelCase	: int    		=  logging.get_logger(__name__)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						def constraint_to_multiple_of(_lowercase	,						_lowercase	,						_lowercase=0	,						_lowercase=None		):
												SCREAMING_SNAKE_CASE							:					int           =							round(val / multiple		) * multiple

												if max_val is not None and x > max_val:
																		SCREAMING_SNAKE_CASE							:					Dict           =							math.floor(val / multiple		) * multiple

												if x < min_val:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							math.ceil(val / multiple		) * multiple

												return x

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(output_size, output_size) if isinstance(_lowercase	,						_lowercase		) else output_size

						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_image_size(_lowercase		)
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							output_size

						# determine new height and width
						SCREAMING_SNAKE_CASE							:					Dict           =							output_height / input_height
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width		) < abs(1 - scale_height		):
																		# fit width
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_width
												else:
																		# fit height
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_height

						SCREAMING_SNAKE_CASE							:					List[str]           =							constraint_to_multiple_of(scale_height * input_height	,						multiple=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							constraint_to_multiple_of(scale_width * input_width	,						multiple=_lowercase		)

						return (new_height, new_width)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""pixel_values"""]


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





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							size if size is not None else {'''height''': 384, '''width''': 384}
												SCREAMING_SNAKE_CASE							:					Any           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							do_resize
												SCREAMING_SNAKE_CASE							:					Any           =							size
												SCREAMING_SNAKE_CASE							:					str           =							keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					int           =							resample
												SCREAMING_SNAKE_CASE							:					Any           =							do_rescale
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rescale_factor
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							do_normalize
												SCREAMING_SNAKE_CASE							:					str           =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BICUBIC					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							get_size_dict(UpperCamelCase__		)
												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()}"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							get_resize_output_image_size(
												    UpperCamelCase__					,	output_size=(size['''height'''], size['''width'''])					,	keep_aspect_ratio=UpperCamelCase__					,	multiple=UpperCamelCase__					,	)
												return resize(UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[int, float]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





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


						def  __A ( self			:       Any					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





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


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       ImageInput					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       PILImageResampling = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       float = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       ChannelDimension = ChannelDimension.FIRST					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					Tuple           =							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:					str           =							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:					Dict           =							make_list_of_images(UpperCamelCase__		)

												if not valid_images(UpperCamelCase__		):
																		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_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.
												SCREAMING_SNAKE_CASE							:					Tuple           =							[to_numpy_array(UpperCamelCase__		) for image in images]

												if do_resize:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[self.resize(image=UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__		) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.rescale(image=UpperCamelCase__					,	scale=UpperCamelCase__		) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.normalize(image=UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[to_channel_dimension_format(UpperCamelCase__					,	UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Tuple           =							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Tuple] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							outputs.logits

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

																		if is_torch_tensor(UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							target_sizes.numpy()

																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]

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

												return semantic_segmentation





