


from __future__ import annotations

from random import random
from typing import Generic, TypeVar

snake_case_						:			Union[str, Any]													=							TypeVar('''KT''')
snake_case_						:			List[Any]													=							TypeVar('''VT''')

class 						A__							(		Generic[KT, VT]  ):




						def __init__(       self					:      Tuple				,    _a					:      KT | str = "root"				,    _a					:      VT | None = None  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =key
										_SCREAMING_SNAKE_CASE			  =value
										_SCREAMING_SNAKE_CASE			  =[]




						def __repr__(       self					:      int  )							->     List[Any]:


										"""simple docstring"""





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




						@property
						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[Any]:


										"""simple docstring"""





										return len(self.forward  )

class 						A__							(		Generic[KT, VT]  ):




						def __init__(       self					:      Tuple				,    _a					:      float = 0.5				,    _a					:      int = 16  )							->     Any:


										"""simple docstring"""





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




						def __str__(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =list(self  )

										if len(UpperCamelCase__  ) == 0:
														return f"SkipList(level={self.level})"

										_SCREAMING_SNAKE_CASE			  =max((len(str(UpperCamelCase__  )  ) for item in items)				,    default=4  )
										_SCREAMING_SNAKE_CASE			  =max(UpperCamelCase__				,    4  ) + 4

										_SCREAMING_SNAKE_CASE			  =self.head
										_SCREAMING_SNAKE_CASE			  =[]

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

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

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

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




						def __iter__(       self					:      Any  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.head

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




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





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

										return level




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      Any  )							->     Any:


										"""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(UpperCamelCase__  )

										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 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      KT  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self._locate_node(UpperCamelCase__  )

										if node is not None:
														for i, update_node in enumerate(UpperCamelCase__  ):
																		# 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 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      KT				,    _a					:      VT  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self._locate_node(UpperCamelCase__  )
										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				,    UpperCamelCase__  ):
																						update_vector.append(self.head  )
																		_SCREAMING_SNAKE_CASE			  =level

														_SCREAMING_SNAKE_CASE			  =Node(UpperCamelCase__				,    UpperCamelCase__  )

														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(UpperCamelCase__  )
																		else:
																						_SCREAMING_SNAKE_CASE			  =new_node




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      VT  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self._locate_node(UpperCamelCase__  )

										if node is not None:
														return node.value

										return None







def 						lowerCamelCase(   ):
				_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(a__) == 4
				assert all_values["Key1"] == 3
				assert all_values["Key2"] == 12
				assert all_values["Key3"] == 41
				assert all_values["Key4"] == -19







def 						lowerCamelCase(   ):
				_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(a__) != 4:
								print()
				assert len(a__) == 4
				assert all_values["Key1"] == 12
				assert all_values["Key7"] == 7
				assert all_values["Key5"] == 5
				assert all_values["Key10"] == 10







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =SkipList()
				assert skip_list.find('''Some key''') is None







def 						lowerCamelCase(   ):
				_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 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =SkipList()
				skip_list.delete('''Some key''')

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







def 						lowerCamelCase(   ):
				_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 						lowerCamelCase(   ):
				_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 						lowerCamelCase(   ):
				_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(a__):
								yield node.key
								for forward_node in node.forward:
												yield from traverse_keys(a__)

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







def 						lowerCamelCase(   ):
				def is_sorted(a__):
								return all(next_item >= item for item, next_item in zip(a__	,lst[1:]))

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







def 						lowerCamelCase(   ):
				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 						lowerCamelCase(   ):
				_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(a__)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

snake_case_						:			Union[str, Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				if isinstance(a__	,(list, tuple)) and isinstance(videos[0]	,(list, tuple)) and is_valid_image(videos[0][0]):
								return videos

				elif isinstance(a__	,(list, tuple)) and is_valid_image(videos[0]):
								return [videos]

				elif is_valid_image(a__):
								return [[videos]]

				raise ValueError(f"Could not make batched video from {videos}")


class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["pixel_values"]




						def __init__(       self					:      Tuple				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      bool = True				,    _a					:      Dict[str, int] = None				,    _a					:      bool = True				,    _a					:      Union[int, float] = 1 / 255				,    _a					:      bool = True				,    _a					:      bool = True				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    **_a					:      Any				,    )							->     None:


										"""simple docstring"""





										super().__init__(**_a  )
										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''shortest_edge''': 256}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										_SCREAMING_SNAKE_CASE			  =do_resize
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_center_crop
										_SCREAMING_SNAKE_CASE			  =crop_size
										_SCREAMING_SNAKE_CASE			  =resample
										_SCREAMING_SNAKE_CASE			  =do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset
										_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 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[Any]				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										if "shortest_edge" in size:
														_SCREAMING_SNAKE_CASE			  =get_resize_output_image_size(_a				,    size['''shortest_edge''']				,    default_to_square=_a  )
										elif "height" in size and "width" in size:
														_SCREAMING_SNAKE_CASE			  =(size['''height'''], size['''width'''])
										else:
														raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
										return resize(_a				,    size=_a				,    resample=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Dict				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a  )
										if "height" not in size or "width" not in size:
														raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
										return center_crop(_a				,    size=(size['''height'''], size['''width'''])				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      np.ndarray				,    _a					:      Union[int, float]				,    _a					:      bool = True				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[str]				,    )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =image.astype(np.floataa  )
										if offset:
														_SCREAMING_SNAKE_CASE			  =image - (scale / 2)
										return rescale(_a				,    scale=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      np.ndarray				,    _a					:      Union[float, List[float]]				,    _a					:      Union[float, List[float]]				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      Any				,    )							->     np.ndarray:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[ChannelDimension] = ChannelDimension.FIRST				,    )							->     np.ndarray:


										"""simple docstring"""





										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.'''  )

										if offset and not do_rescale:
														raise ValueError('''For offset, do_rescale must also be set to True.'''  )

										# All transformations expect numpy arrays.
										_SCREAMING_SNAKE_CASE			  =to_numpy_array(_a  )

										if do_resize:
														_SCREAMING_SNAKE_CASE			  =self.resize(image=_a				,    size=_a				,    resample=_a  )

										if do_center_crop:
														_SCREAMING_SNAKE_CASE			  =self.center_crop(_a				,    size=_a  )

										if do_rescale:
														_SCREAMING_SNAKE_CASE			  =self.rescale(image=_a				,    scale=_a				,    offset=_a  )

										if do_normalize:
														_SCREAMING_SNAKE_CASE			  =self.normalize(image=_a				,    mean=_a				,    std=_a  )

										_SCREAMING_SNAKE_CASE			  =to_channel_dimension_format(_a				,    _a  )
										return image




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = 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				,    )							->     PIL.Image.Image:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE			  =resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE			  =do_center_crop if do_center_crop is not None else self.do_center_crop
										_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			  =offset if offset is not None else self.offset
										_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			  =size if size is not None else self.size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else self.crop_size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										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.'''  )

										_SCREAMING_SNAKE_CASE			  =make_batched(_a  )

										_SCREAMING_SNAKE_CASE			  =[
										    [
										        self._preprocess_image(
										            image=_a				,    do_resize=_a				,    size=_a				,    resample=_a				,    do_center_crop=_a				,    crop_size=_a				,    do_rescale=_a				,    rescale_factor=_a				,    offset=_a				,    do_normalize=_a				,    image_mean=_a				,    image_std=_a				,    data_format=_a				,    )
										        for img in video
										    ]
										    for video in videos
										]

										_SCREAMING_SNAKE_CASE			  ={'''pixel_values''': videos}
										return BatchFeature(data=_a				,    tensor_type=_a  )