




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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


SCREAMING_SNAKE_CASE:    Any											=   logging.get_logger(__name__)


if is_vision_available():
						import PIL



class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=["pixel_values"]



						def __init__( self  :      Any				,       snake_case__  :      bool = True				,       snake_case__  :      Dict[str, int] = None				,       snake_case__  :      PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__  :      bool = True				,       snake_case__  :      bool = False				,       snake_case__  :      bool = True				,       snake_case__  :      bool = True				,       snake_case__  :      Union[int, float] = 1 / 2_55				,       snake_case__  :      bool = True				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       **snake_case__  :      int				,       ):

								"""simple docstring"""

								super().__init__(**snake_case__     )

								SCREAMING_SNAKE_CASE_							=       size if size is not None else {'height': 25_60, 'width': 19_20}
								if isinstance(snake_case__				,       (tuple, list)     ):
										# The previous feature extractor size parameter was in (width, height) format
										SCREAMING_SNAKE_CASE_							=       size[::-1]
								SCREAMING_SNAKE_CASE_							=       get_size_dict(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       do_resize
								SCREAMING_SNAKE_CASE_							=       size
								SCREAMING_SNAKE_CASE_							=       resample
								SCREAMING_SNAKE_CASE_							=       do_thumbnail
								SCREAMING_SNAKE_CASE_							=       do_align_long_axis
								SCREAMING_SNAKE_CASE_							=       do_pad
								SCREAMING_SNAKE_CASE_							=       do_rescale
								SCREAMING_SNAKE_CASE_							=       rescale_factor
								SCREAMING_SNAKE_CASE_							=       do_normalize
								SCREAMING_SNAKE_CASE_							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								SCREAMING_SNAKE_CASE_							=       image_std if image_std is not None else IMAGENET_STANDARD_STD



						def   __a				( self  :      List[Any]				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_image_size(snake_case__     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size['height'], size['width']

								if (output_width < output_height and input_width > input_height) or (
								    output_width > output_height and input_width < input_height
								):
										SCREAMING_SNAKE_CASE_							=       np.rotaa(snake_case__				,       3     )

								if data_format is not None:
										SCREAMING_SNAKE_CASE_							=       to_channel_dimension_format(snake_case__				,       snake_case__     )

								return image



						def   __a				( self  :      Any				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      bool = False				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size['height'], size['width']
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_image_size(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       output_width - input_width
								SCREAMING_SNAKE_CASE_							=       output_height - input_height

								if random_padding:
										SCREAMING_SNAKE_CASE_							=       np.random.randint(low=0				,       high=delta_height + 1     )
										SCREAMING_SNAKE_CASE_							=       np.random.randint(low=0				,       high=delta_width + 1     )
								else:
										SCREAMING_SNAKE_CASE_							=       delta_height // 2
										SCREAMING_SNAKE_CASE_							=       delta_width // 2

								SCREAMING_SNAKE_CASE_							=       delta_height - pad_top
								SCREAMING_SNAKE_CASE_							=       delta_width - pad_left

								SCREAMING_SNAKE_CASE_							=       ((pad_top, pad_bottom), (pad_left, pad_right))
								return pad(snake_case__				,       snake_case__				,       data_format=snake_case__     )



						def   __a				( self  :      List[Any]				,       *snake_case__  :      Any				,       **snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'     )
								return self.pad_image(*snake_case__				,       **snake_case__     )



						def   __a				( self  :      Dict				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      PILImageResampling = PILImageResampling.BICUBIC				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       **snake_case__  :      Tuple				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_image_size(snake_case__     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size['height'], size['width']

								# We always resize to the smallest of either the input or output size.
								SCREAMING_SNAKE_CASE_							=       min(snake_case__				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       min(snake_case__				,       snake_case__     )

								if height == input_height and width == input_width:
										return image

								if input_height > input_width:
										SCREAMING_SNAKE_CASE_							=       int(input_width * height / input_height     )
								elif input_width > input_height:
										SCREAMING_SNAKE_CASE_							=       int(input_height * width / input_width     )

								return resize(
								    snake_case__				,       size=(height, width)				,       resample=snake_case__				,       reducing_gap=2.0				,       data_format=snake_case__				,       **snake_case__     )



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      PILImageResampling = PILImageResampling.BICUBIC				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       **snake_case__  :      List[Any]				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       get_size_dict(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       min(size['height']				,       size['width']     )
								SCREAMING_SNAKE_CASE_							=       get_resize_output_image_size(snake_case__				,       size=snake_case__				,       default_to_square=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       resize(snake_case__				,       size=snake_case__				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__     )
								return resized_image



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

								"""simple docstring"""

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



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

								"""simple docstring"""

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






						def   __a				( self  :      int				,       snake_case__  :      ImageInput				,       snake_case__  :      bool = None				,       snake_case__  :      Dict[str, int] = None				,       snake_case__  :      PILImageResampling = None				,       snake_case__  :      bool = None				,       snake_case__  :      bool = None				,       snake_case__  :      bool = None				,       snake_case__  :      bool = False				,       snake_case__  :      bool = None				,       snake_case__  :      float = None				,       snake_case__  :      bool = None				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       snake_case__  :      Optional[Union[str, TensorType]] = None				,       snake_case__  :      Optional[ChannelDimension] = ChannelDimension.FIRST				,       **snake_case__  :      int				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE_							=       size if size is not None else self.size
								if isinstance(snake_case__				,       (tuple, list)     ):
										# Previous feature extractor had size in (width, height) format
										SCREAMING_SNAKE_CASE_							=       size[::-1]
								SCREAMING_SNAKE_CASE_							=       get_size_dict(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE_							=       do_thumbnail if do_thumbnail is not None else self.do_thumbnail
								SCREAMING_SNAKE_CASE_							=       do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
								SCREAMING_SNAKE_CASE_							=       do_pad if do_pad is not None else self.do_pad
								SCREAMING_SNAKE_CASE_							=       do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE_							=       rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE_							=       do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE_							=       image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE_							=       image_std if image_std is not None else self.image_std

								SCREAMING_SNAKE_CASE_							=       make_list_of_images(snake_case__     )

								if not valid_images(snake_case__     ):
										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_pad and size is None:
										raise ValueError('Size must be specified if do_pad 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_							=       [to_numpy_array(snake_case__     ) for image in images]

								if do_align_long_axis:
										SCREAMING_SNAKE_CASE_							=       [self.align_long_axis(snake_case__				,       size=snake_case__     ) for image in images]

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

								if do_thumbnail:
										SCREAMING_SNAKE_CASE_							=       [self.thumbnail(image=snake_case__				,       size=snake_case__     ) for image in images]

								if do_pad:
										SCREAMING_SNAKE_CASE_							=       [self.pad_image(image=snake_case__				,       size=snake_case__				,       random_padding=snake_case__     ) for image in images]

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

								if do_normalize:
										SCREAMING_SNAKE_CASE_							=       [self.normalize(image=snake_case__				,       mean=snake_case__				,       std=snake_case__     ) for image in images]

								SCREAMING_SNAKE_CASE_							=       [to_channel_dimension_format(snake_case__				,       snake_case__     ) for image in images]

								SCREAMING_SNAKE_CASE_							=       {'pixel_values': images}
								return BatchFeature(data=snake_case__				,       tensor_type=snake_case__     )





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


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