





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

_lowerCamelCase 					=      TypeVar('KT')
_lowerCamelCase 					=      TypeVar('VT')




class 	UpperCamelCase_							(  Generic[KT, VT]     ):


							def __init__(				self     :List[str]				,					__A     :KT | str = "root"				,					__A     :VT | None = None		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   key
									SCREAMING_SNAKE_CASE__								=   value
									SCREAMING_SNAKE_CASE__								=   []


							def __repr__(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									return f'''Node({self.key}: {self.value})'''


							@property
							def   _snake_case	(				self     :str		) -> int:



									"""simple docstring"""



									return len(self.forward		)




class 	UpperCamelCase_							(  Generic[KT, VT]     ):


							def __init__(				self     :Union[str, Any]				,					__A     :float = 0.5				,					__A     :int = 16		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   Node[KT, VT]()
									SCREAMING_SNAKE_CASE__								=   0
									SCREAMING_SNAKE_CASE__								=   p
									SCREAMING_SNAKE_CASE__								=   max_level


							def __str__(				self     :Optional[int]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   list(self		)

									if len(__A		) == 0:
											return f'''SkipList(level={self.level})'''

									SCREAMING_SNAKE_CASE__								=   max((len(str(__A		)		) for item in items)				,					default=4		)
									SCREAMING_SNAKE_CASE__								=   max(__A				,					4		) + 4

									SCREAMING_SNAKE_CASE__								=   self.head
									SCREAMING_SNAKE_CASE__								=   []

									SCREAMING_SNAKE_CASE__								=   node.forward.copy()
									lines.append(f'''[{node.key}]'''.ljust(__A				,					"""-"""		) + """* """ * len(__A		)		)
									lines.append(""" """ * label_size + """| """ * len(__A		)		)

									while len(node.forward		) != 0:
											SCREAMING_SNAKE_CASE__								=   node.forward[0]

											lines.append(
											    f'''[{node.key}]'''.ljust(__A				,					"""-"""		)
											    + """ """.join(str(n.key		) if n.key == node.key else """|""" for n in forwards		)		)
											lines.append(""" """ * label_size + """| """ * len(__A		)		)
											SCREAMING_SNAKE_CASE__								=   node.forward

									lines.append("""None""".ljust(__A		) + """* """ * len(__A		)		)
									return f'''SkipList(level={self.level})\n''' + "\n".join(__A		)


							def __iter__(				self     :Dict		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.head

									while len(node.forward		) != 0:
											yield node.forward[0].key
											SCREAMING_SNAKE_CASE__								=   node.forward[0]


							def   _snake_case	(				self     :str		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   1
									while random() < self.p and level < self.max_level:
											level += 1

									return level


							def   _snake_case	(				self     :Dict				,					__A     :Optional[int]		) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   []

									SCREAMING_SNAKE_CASE__								=   self.head

									for i in reversed(range(self.level		)		):
											# i < node.level - When node level is lesser than `i` decrement `i`.
											# node.forward[i].key < key - Jumping to node with key value higher
											#                             or equal to searched key would result
											#                             in skipping searched key.
											while i < node.level and node.forward[i].key < key:
													SCREAMING_SNAKE_CASE__								=   node.forward[i]
											# Each leftmost node (relative to searched node) will potentially have to
											# be updated.
											update_vector.append(__A		)

									update_vector.reverse()  # Note that we were inserting values in reverse order.

									# len(node.forward) != 0 - If current node doesn't contain any further
									#                          references then searched key is not present.
									# node.forward[0].key == key - Next node key should be equal to search key
									#                              if key is present.
									if len(node.forward		) != 0 and node.forward[0].key == key:
											return node.forward[0], update_vector
									else:
											return None, update_vector


							def   _snake_case	(				self     :Optional[int]				,					__A     :KT		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._locate_node(__A		)

									if node is not None:
											for i, update_node in enumerate(__A		):
													# Remove or replace all references to removed node.
													if update_node.level > i and update_node.forward[i].key == key:
															if node.level > i:
																	SCREAMING_SNAKE_CASE__								=   node.forward[i]
															else:
																	SCREAMING_SNAKE_CASE__								=   update_node.forward[:i]


							def   _snake_case	(				self     :List[Any]				,					__A     :KT				,					__A     :VT		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._locate_node(__A		)
									if node is not None:
											SCREAMING_SNAKE_CASE__								=   value
									else:
											SCREAMING_SNAKE_CASE__								=   self.random_level()

											if level > self.level:
													# After level increase we have to add additional nodes to head.
													for _ in range(self.level - 1				,					__A		):
															update_vector.append(self.head		)
													SCREAMING_SNAKE_CASE__								=   level

											SCREAMING_SNAKE_CASE__								=   Node(__A				,					__A		)

											for i, update_node in enumerate(update_vector[:level]		):
													# Change references to pass through new node.
													if update_node.level > i:
															new_node.forward.append(update_node.forward[i]		)

													if update_node.level < i + 1:
															update_node.forward.append(__A		)
													else:
															SCREAMING_SNAKE_CASE__								=   new_node


							def   _snake_case	(				self     :Optional[Any]				,					__A     :VT		) -> VT | None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self._locate_node(__A		)

									if node is not None:
											return node.value

									return None






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()
		skip_list.insert("""Key1"""       , 3     )
		skip_list.insert("""Key2"""       , 12     )
		skip_list.insert("""Key3"""       , 41     )
		skip_list.insert("""Key4"""       , -19     )

		SCREAMING_SNAKE_CASE__								=   skip_list.head
		SCREAMING_SNAKE_CASE__								=   {}
		while node.level != 0:
				SCREAMING_SNAKE_CASE__								=   node.forward[0]
				SCREAMING_SNAKE_CASE__								=   node.value

		assert len(UpperCamelCase__     ) == 4
		assert all_values["Key1"] == 3
		assert all_values["Key2"] == 12
		assert all_values["Key3"] == 41
		assert all_values["Key4"] == -19






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()
		skip_list.insert("""Key1"""       , 10     )
		skip_list.insert("""Key1"""       , 12     )

		skip_list.insert("""Key5"""       , 7     )
		skip_list.insert("""Key7"""       , 10     )
		skip_list.insert("""Key10"""       , 5     )

		skip_list.insert("""Key7"""       , 7     )
		skip_list.insert("""Key5"""       , 5     )
		skip_list.insert("""Key10"""       , 10     )

		SCREAMING_SNAKE_CASE__								=   skip_list.head
		SCREAMING_SNAKE_CASE__								=   {}
		while node.level != 0:
				SCREAMING_SNAKE_CASE__								=   node.forward[0]
				SCREAMING_SNAKE_CASE__								=   node.value

		if len(UpperCamelCase__     ) != 4:
				print()
		assert len(UpperCamelCase__     ) == 4
		assert all_values["Key1"] == 12
		assert all_values["Key7"] == 7
		assert all_values["Key5"] == 5
		assert all_values["Key10"] == 10






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()
		assert skip_list.find("""Some key"""     ) is None






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()

		skip_list.insert("""Key2"""       , 20     )
		assert skip_list.find("""Key2"""     ) == 20

		skip_list.insert("""Some Key"""       , 10     )
		skip_list.insert("""Key2"""       , 8     )
		skip_list.insert("""V"""       , 13     )

		assert skip_list.find("""Y"""     ) is None
		assert skip_list.find("""Key2"""     ) == 8
		assert skip_list.find("""Some Key"""     ) == 10
		assert skip_list.find("""V"""     ) == 13






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()
		skip_list.delete("""Some key"""     )

		assert len(skip_list.head.forward     ) == 0






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()

		skip_list.insert("""Key1"""       , 12     )
		skip_list.insert("""V"""       , 13     )
		skip_list.insert("""X"""       , 14     )
		skip_list.insert("""Key2"""       , 15     )

		skip_list.delete("""V"""     )
		skip_list.delete("""Key2"""     )

		assert skip_list.find("""V"""     ) is None
		assert skip_list.find("""Key2"""     ) is None






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()

		skip_list.insert("""Key1"""       , 12     )
		skip_list.insert("""V"""       , 13     )
		skip_list.insert("""X"""       , 14     )
		skip_list.insert("""Key2"""       , 15     )

		skip_list.delete("""V"""     )
		assert skip_list.find("""V"""     ) is None
		assert skip_list.find("""X"""     ) == 14
		assert skip_list.find("""Key1"""     ) == 12
		assert skip_list.find("""Key2"""     ) == 15

		skip_list.delete("""X"""     )
		assert skip_list.find("""V"""     ) is None
		assert skip_list.find("""X"""     ) is None
		assert skip_list.find("""Key1"""     ) == 12
		assert skip_list.find("""Key2"""     ) == 15

		skip_list.delete("""Key1"""     )
		assert skip_list.find("""V"""     ) is None
		assert skip_list.find("""X"""     ) is None
		assert skip_list.find("""Key1"""     ) is None
		assert skip_list.find("""Key2"""     ) == 15

		skip_list.delete("""Key2"""     )
		assert skip_list.find("""V"""     ) is None
		assert skip_list.find("""X"""     ) is None
		assert skip_list.find("""Key1"""     ) is None
		assert skip_list.find("""Key2"""     ) is None






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()

		skip_list.insert("""Key1"""       , 12     )
		skip_list.insert("""V"""       , 13     )
		skip_list.insert("""X"""       , 142     )
		skip_list.insert("""Key2"""       , 15     )

		skip_list.delete("""X"""     )

		def traverse_keys(UpperCamelCase__:				List[Any]     ):
				yield node.key
				for forward_node in node.forward:
						yield from traverse_keys(UpperCamelCase__     )

		assert len(set(traverse_keys(skip_list.head     )     )     ) == 4






def 					SCREAMING_SNAKE_CASE__				(							):
		def is_sorted(UpperCamelCase__:				str     ):
				return all(next_item >= item for item, next_item in zip(UpperCamelCase__       , lst[1:]     )     )

		SCREAMING_SNAKE_CASE__								=   SkipList()
		for i in range(10     ):
				skip_list.insert(UpperCamelCase__       , UpperCamelCase__     )
		assert is_sorted(list(UpperCamelCase__     )     )
		skip_list.delete(5     )
		skip_list.delete(8     )
		skip_list.delete(2     )
		assert is_sorted(list(UpperCamelCase__     )     )
		skip_list.insert(-12       , -12     )
		skip_list.insert(77       , 77     )
		assert is_sorted(list(UpperCamelCase__     )     )






def 					SCREAMING_SNAKE_CASE__				(							):
		for _ in range(100     ):
				# Repeat test 100 times due to the probabilistic nature of skip list
				# random values == random bugs
				test_insert()
				test_insert_overrides_existing_value()

				test_searching_empty_list_returns_none()
				test_search()

				test_deleting_item_from_empty_list_do_nothing()
				test_deleted_items_are_not_founded_by_find_method()
				test_delete_removes_only_given_key()
				test_delete_doesnt_leave_dead_nodes()

				test_iter_always_yields_sorted_values()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   SkipList()
		skip_list.insert(2       , """2"""     )
		skip_list.insert(4       , """4"""     )
		skip_list.insert(6       , """4"""     )
		skip_list.insert(4       , """5"""     )
		skip_list.insert(8       , """4"""     )
		skip_list.insert(9       , """4"""     )

		skip_list.delete(4     )

		print(UpperCamelCase__     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
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


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					def constraint_to_multiple_of(__snake_case  ,     __snake_case  ,     __snake_case=0  ,     __snake_case=None						):
										_UpperCamelCase			     =     round(val / multiple						) * multiple

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

										if x < min_val:
															_UpperCamelCase			     =     math.ceil(val / multiple						) * multiple

										return x

					_UpperCamelCase			     =     (output_size, output_size) if isinstance(__snake_case  ,     __snake_case						) else output_size

					_UpperCamelCase     ,    _UpperCamelCase			     =     get_image_size(__snake_case						)
					_UpperCamelCase     ,    _UpperCamelCase			     =     output_size

					# determine new height and width
					_UpperCamelCase			     =     output_height / input_height
					_UpperCamelCase			     =     output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width						) < abs(1 - scale_height						):
															# fit width
															_UpperCamelCase			     =     scale_width
										else:
															# fit height
															_UpperCamelCase			     =     scale_height

					_UpperCamelCase			     =     constraint_to_multiple_of(scale_height * input_height  ,     multiple=__snake_case						)
					_UpperCamelCase			     =     constraint_to_multiple_of(scale_width * input_width  ,     multiple=__snake_case						)

					return (new_height, new_width)







class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     ["pixel_values"]







							def __init__(   self      :     List[Any] ,			_A      :     bool = True ,			_A      :     Dict[str, int] = None ,			_A      :     PILImageResampling = PILImageResampling.BILINEAR ,			_A      :     bool = False ,			_A      :     int = 1 ,			_A      :     bool = True ,			_A      :     Union[int, float] = 1 / 255 ,			_A      :     bool = True ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			**_A      :     List[str] ,			):
												super().__init__(**_A		)
												_UpperCamelCase			     =     size if size is not None else {'''height''': 384, '''width''': 384}
												_UpperCamelCase			     =     get_size_dict(_A		)
												_UpperCamelCase			     =     do_resize
												_UpperCamelCase			     =     size
												_UpperCamelCase			     =     keep_aspect_ratio
												_UpperCamelCase			     =     ensure_multiple_of
												_UpperCamelCase			     =     resample
												_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  UpperCamelCase_						(   self      :     List[str] ,			_A      :     np.ndarray ,			_A      :     Dict[str, int] ,			_A      :     bool = False ,			_A      :     int = 1 ,			_A      :     PILImageResampling = PILImageResampling.BICUBIC ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												_UpperCamelCase			     =     get_size_dict(_A		)
												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()}"""		)
												_UpperCamelCase			     =     get_resize_output_image_size(
												    _A ,			output_size=(size['''height'''], size['''width''']) ,			keep_aspect_ratio=_A ,			multiple=_A ,			)
												return resize(_A ,			size=_A ,			resample=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     np.ndarray ,			_A      :     Union[int, float] ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												return rescale(_A ,			scale=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     np.ndarray ,			_A      :     Union[float, List[float]] ,			_A      :     Union[float, List[float]] ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												return normalize(_A ,			mean=_A ,			std=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     ImageInput ,			_A      :     bool = None ,			_A      :     int = None ,			_A      :     bool = None ,			_A      :     int = None ,			_A      :     PILImageResampling = None ,			_A      :     bool = None ,			_A      :     float = None ,			_A      :     bool = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[str, TensorType]] = None ,			_A      :     ChannelDimension = ChannelDimension.FIRST ,			**_A      :     str ,			):
												_UpperCamelCase			     =     do_resize if do_resize is not None else self.do_resize
												_UpperCamelCase			     =     size if size is not None else self.size
												_UpperCamelCase			     =     get_size_dict(_A		)
												_UpperCamelCase			     =     keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_UpperCamelCase			     =     ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_UpperCamelCase			     =     resample if resample is not None else self.resample
												_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			     =     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_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_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		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     Any ,			_A      :     List[Tuple] = None		):
												_UpperCamelCase			     =     outputs.logits

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

																	if is_torch_tensor(_A		):
																						_UpperCamelCase			     =     target_sizes.numpy()

																	_UpperCamelCase			     =     []

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

												return semantic_segmentation
