





def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Dict           =   int(UpperCamelCase__					)
						if n_element < 1:
												__SCREAMING_SNAKE_CASE:				Any           =   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:				Any           =   (0, 0, 0)
						__SCREAMING_SNAKE_CASE:				List[str]           =   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__":
							lowerCAmelCase							:       List[Any]					 =	input("""Enter the last number (nth term) of the Hamming Number Series: """)
							print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
							lowerCAmelCase							:       Optional[Any]					 =	hamming(int(n))
							print("""-----------------------------------------------------""")
							print(f'''The list with nth numbers is: {hamming_numbers}''')
							print("""-----------------------------------------------------""")





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, 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


lowerCamelCase__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			str						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = PIL.Image.BICUBIC							,				a = True							,				a = None							,				a = 1 / 2_55							,				a = True							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""height""": 2_56, """width""": 2_56}
								_UpperCamelCase         =						get_size_dict(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 			A_					(      self							,				a							,				a							,				a = PIL.Image.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return resize(
								    a							,				size=(size["""height"""], size["""width"""])							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return center_crop(a							,				size=(size["""height"""], size["""width"""])							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   List[str]:


								'''simple docstring'''

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

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

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







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a=None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

								_UpperCamelCase         =						do_resize if do_resize is not None else self.do_resize
								_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         =						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         =						size if size is not None else self.size
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else self.crop_size
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

								_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 or resample is None:
										raise ValueError("""Size and resample 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					)



