





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_        =							logging.get_logger(__name__)



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[Any]			     = ['''pixel_values''']






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


							'''simple docstring'''



							super().__init__(**__magic_name__       )
							snake_case_		:       Dict           =    size if size is not None else {'''shortest_edge''': 224}
							snake_case_		:       List[Any]           =    get_size_dict(__magic_name__   ,				default_to_square=__magic_name__       )
							snake_case_		:       Optional[Any]           =    crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
							snake_case_		:       Union[str, Any]           =    get_size_dict(__magic_name__   ,				param_name='''crop_size'''       )

							snake_case_		:       List[str]           =    do_resize
							snake_case_		:       List[Any]           =    size
							snake_case_		:       List[str]           =    resample
							snake_case_		:       int           =    do_rescale
							snake_case_		:       Tuple           =    rescale_factor
							snake_case_		:       Any           =    do_center_crop
							snake_case_		:       Union[str, Any]           =    crop_size
							snake_case_		:       int           =    do_flip_channel_order






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = PIL.Image.BILINEAR   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  np.ndarray:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  np.ndarray:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    get_size_dict(__magic_name__       )
							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(__magic_name__   ,				size=(size['''height'''], size['''width'''])   ,				data_format=__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  Optional[int]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  np.ndarray:


							'''simple docstring'''



							return flip_channel_order(__magic_name__   ,				data_format=__magic_name__       )






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


							'''simple docstring'''



							snake_case_		:       Dict           =    do_resize if do_resize is not None else self.do_resize
							snake_case_		:       Any           =    resample if resample is not None else self.resample
							snake_case_		:       Any           =    do_rescale if do_rescale is not None else self.do_rescale
							snake_case_		:       Optional[int]           =    rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_		:       str           =    do_center_crop if do_center_crop is not None else self.do_center_crop
							snake_case_		:       List[str]           =    (
							    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
							)

							snake_case_		:       Any           =    size if size is not None else self.size
							snake_case_		:       Optional[int]           =    get_size_dict(__magic_name__   ,				default_to_square=__magic_name__       )
							snake_case_		:       Tuple           =    crop_size if crop_size is not None else self.crop_size
							snake_case_		:       Tuple           =    get_size_dict(__magic_name__   ,				param_name='''crop_size'''       )

							snake_case_		:       Tuple           =    make_list_of_images(__magic_name__       )

							if not valid_images(__magic_name__       ):
											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.
							snake_case_		:       Tuple           =    [to_numpy_array(__magic_name__       ) for image in images]

							if do_resize:
											snake_case_		:       List[str]           =    [self.resize(image=__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__       ) for image in images]

							if do_center_crop:
											snake_case_		:       Dict           =    [self.center_crop(image=__magic_name__   ,				size=__magic_name__       ) for image in images]

							if do_rescale:
											snake_case_		:       str           =    [self.rescale(image=__magic_name__   ,				scale=__magic_name__       ) for image in images]

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

							snake_case_		:       Tuple           =    [to_channel_dimension_format(__magic_name__   ,				__magic_name__       ) for image in images]

							snake_case_		:       Optional[int]           =    {'''pixel_values''': images}
							return BatchFeature(data=__magic_name__   ,				tensor_type=__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Tuple           =    outputs.logits

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

											if is_torch_tensor(__magic_name__       ):
															snake_case_		:       List[str]           =    target_sizes.numpy()

											snake_case_		:       Any           =    []

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

							return semantic_segmentation




'''simple docstring'''
def 	_A  (       A							)  ->				list:
	lowercase						:		str   					=		int(A							)
	if n_element < 1:
		lowercase						:		List[Any]   					=		ValueError("a should be a positive number"							)
		raise my_error

	lowercase						:		List[Any]   					=		[1]
	lowercase					,		lowercase					,		lowercase						:		Tuple   					=		(0, 0, 0)
	lowercase						:		Dict   					=		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   : Union[str, 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   : Tuple             =				hamming(int(n))
					print("""-----------------------------------------------------""")
					print(F'''The list with nth numbers is: {hamming_numbers}''')
					print("""-----------------------------------------------------""")


