






'''simple docstring'''



from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__UpperCamelCase      :       List[Any]      			=      TypeVar("""KT""")
__UpperCamelCase      :       List[str]      			=      TypeVar("""VT""")
class 							__SCREAMING_SNAKE_CASE       (							Generic[KT, VT]     ):




      def __init__(  self ,   lowerCamelCase = "root" ,   lowerCamelCase = None      )							->Tuple:



         '''simple docstring'''
         __a				     =    key
         __a				     =    value
         __a				     =    []




      def __repr__(  self      )							->str:



         '''simple docstring'''
         return F"""Node({self.key}: {self.value})"""






      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return len(self.forward      )







class 							__SCREAMING_SNAKE_CASE       (							Generic[KT, VT]     ):




      def __init__(  self ,   lowerCamelCase = 0.5 ,   lowerCamelCase = 16      )							->Dict:



         '''simple docstring'''
         __a				     =    Node[KT, VT]()
         __a				     =    0
         __a				     =    p
         __a				     =    max_level




      def __str__(  self      )							->str:



         '''simple docstring'''
         __a				     =    list(self      )

         if len(lowerCamelCase      ) == 0:
            return F"""SkipList(level={self.level})"""

         __a				     =    max((len(str(lowerCamelCase      )      ) for item in items) ,   default=4      )
         __a				     =    max(lowerCamelCase ,   4      ) + 4

         __a				     =    self.head
         __a				     =    []

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

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

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

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




      def __iter__(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.head

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




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    1
         while random() < self.p and level < self.max_level:
            level += 1

         return level




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:



         '''simple docstring'''
         __a				     =    []

         __a				     =    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:
               __a				     =    node.forward[i]
            # Each leftmost node (relative to searched node) will potentially have to
            # be updated.
            update_vector.append(lowerCamelCase      )

         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 ,   lowerCamelCase      )							->Dict:



         '''simple docstring'''
         __a     ,			__a				     =    self._locate_node(lowerCamelCase      )

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         __a     ,			__a				     =    self._locate_node(lowerCamelCase      )
         if node is not None:
            __a				     =    value
         else:
            __a				     =    self.random_level()

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

            __a				     =    Node(lowerCamelCase ,   lowerCamelCase      )

            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(lowerCamelCase      )
               else:
                  __a				     =    new_node






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->VT | None:



         '''simple docstring'''
         __a     ,			__a				     =    self._locate_node(lowerCamelCase      )

         if node is not None:
            return node.value

         return None







def    __UpperCAmelCase	(				)  ->			Tuple:

   """simple docstring"""
   __a				     =    SkipList()
   skip_list.insert('Key1',					3    )
   skip_list.insert('Key2',					12    )
   skip_list.insert('Key3',					41    )
   skip_list.insert('Key4',					-19    )

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

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



def    __UpperCAmelCase	(				)  ->			Any:

   """simple docstring"""
   __a				     =    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    )

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

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



def    __UpperCAmelCase	(				)  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    SkipList()
   assert skip_list.find('Some key'    ) is None



def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    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    __UpperCAmelCase	(				)  ->			str:

   """simple docstring"""
   __a				     =    SkipList()
   skip_list.delete('Some key'    )

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



def    __UpperCAmelCase	(				)  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    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    __UpperCAmelCase	(				)  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    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    __UpperCAmelCase	(				)  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    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(SCREAMING_SNAKE_CASE__:	Optional[Any]    ):
      yield node.key
      for forward_node in node.forward:
         yield from traverse_keys(SCREAMING_SNAKE_CASE__    )

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



def    __UpperCAmelCase	(				)  ->			str:

   """simple docstring"""
   def is_sorted(SCREAMING_SNAKE_CASE__:	Tuple    ):
      return all(next_item >= item for item, next_item in zip(SCREAMING_SNAKE_CASE__,					lst[1:]    )    )

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



def    __UpperCAmelCase	(				)  ->			Tuple:

   """simple docstring"""
   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    __UpperCAmelCase	(				)  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    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(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()







'''simple docstring'''



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

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

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

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

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



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




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



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

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __a				     =    do_normalize
         __a				     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __a				     =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



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




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



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         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(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




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



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = 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.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






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



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    offset if offset is not None else self.offset
         __a				     =    do_normalize if do_normalize is not None else self.do_normalize
         __a				     =    image_mean if image_mean is not None else self.image_mean
         __a				     =    image_std if image_std is not None else self.image_std

         __a				     =    size if size is not None else self.size
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         if not valid_images(lowerCamelCase      ):
            raise ValueError(
                'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                'torch.Tensor, tf.Tensor or jax.ndarray.'      )

         __a				     =    make_batched(lowerCamelCase      )

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

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )