



"""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


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BILINEAR   ,		lowercase__ = True   ,		lowercase__ = 1 / 2_55   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = True   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		Dict 					=					size if size is not None else {"""shortest_edge""": 2_24}
											snake_case_					:		Optional[Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		Union[str, Any] 					=					crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

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







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = PIL.Image.BILINEAR   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											if "shortest_edge" not in size:
																raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'       )
											snake_case_					:		int 					=					get_resize_output_image_size(lowercase__   ,		size=size["""shortest_edge"""]   ,		default_to_square=lowercase__       )
											return resize(lowercase__   ,		size=lowercase__   ,		resample=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		List[Any] 					=					get_size_dict(lowercase__       )
											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(lowercase__   ,		size=(size["""height"""], size["""width"""])   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return rescale(lowercase__   ,		scale=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											return flip_channel_order(lowercase__   ,		data_format=lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = ChannelDimension.FIRST   ,		**lowercase__   ,		):
											snake_case_					:		List[str] 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		Tuple 					=					resample if resample is not None else self.resample
											snake_case_					:		Optional[Any] 					=					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_					:		int 					=					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_					:		int 					=					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_					:		Tuple 					=					(
											    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_					:		int 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		Optional[int] 					=					crop_size if crop_size is not None else self.crop_size
											snake_case_					:		str 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		List[str] 					=					make_list_of_images(lowercase__       )

											if not valid_images(lowercase__       ):
																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_					:		Union[str, Any] 					=					[to_numpy_array(lowercase__       ) for image in images]

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

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

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

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

											snake_case_					:		Any 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		Tuple 					=					{"""pixel_values""": images}
											return BatchFeature(data=lowercase__   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											snake_case_					:		List[str] 					=					outputs.logits

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

																if is_torch_tensor(lowercase__       ):
																					snake_case_					:		str 					=					target_sizes.numpy()

																snake_case_					:		int 					=					[]

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

											return semantic_segmentation









"""simple docstring"""

def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  list			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					len(SCREAMING_SNAKE_CASE__			)
					for i in range(1				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		Tuple 					=					collection[i]
										snake_case_					:		Tuple 					=					0
										snake_case_					:		str 					=					i - 1

										while low <= high:
															snake_case_					:		Optional[int] 					=					(low + high) // 2
															if val < collection[mid]:
																				snake_case_					:		List[str] 					=					mid - 1
															else:
																				snake_case_					:		str 					=					mid + 1
										for j in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    -1			):
															snake_case_					:		List[str] 					=					collection[j - 1]
										snake_case_					:		Any 					=					val
					return collection


if __name__ == "__main__":
			a_			   =					input('''Enter numbers separated by a comma:\n''').strip()
			a_			   =					[int(item) for item in user_input.split(''',''')]
			print(binary_insertion_sort(unsorted))




