





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor







class   __lowerCAmelCase			(					unittest.TestCase  ):





   def __init__( self					,			snake_case					,			snake_case=7					,			snake_case=3					,			snake_case=18					,			snake_case=30					,			snake_case=400					,			snake_case=True					,			snake_case=None					,			snake_case=True					,			snake_case=None					,			snake_case=True					,			snake_case=[0.5, 0.5, 0.5]					,			snake_case=[0.5, 0.5, 0.5]					,			)				->  List[str]:
      """simple docstring"""





      a__	:						List[Any]      		=  size if size is not None else {"shortest_edge": 18}
      a__	:						Any      		=  crop_size if crop_size is not None else {"height": 18, "width": 18}
      a__	:						str      		=  parent
      a__	:						Optional[int]      		=  batch_size
      a__	:						List[str]      		=  num_channels
      a__	:						Optional[Any]      		=  image_size
      a__	:						Dict      		=  min_resolution
      a__	:						Any      		=  max_resolution
      a__	:						Optional[Any]      		=  do_resize
      a__	:						Dict      		=  size
      a__	:						int      		=  do_center_crop
      a__	:						Tuple      		=  crop_size
      a__	:						str      		=  do_normalize
      a__	:						Any      		=  image_mean
      a__	:						Tuple      		=  image_std





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "do_center_crop": self.do_center_crop,
          "size": self.size,
          "crop_size": self.crop_size,
      }







@require_torch
@require_vision
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Any     		=				LevitImageProcessor if is_vision_available() else None





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[Any]      		=  LevitImageProcessingTester(self      )





   @property
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Dict      		=  self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(snake_case					,			"image_mean"      )      )
      self.assertTrue(hasattr(snake_case					,			"image_std"      )      )
      self.assertTrue(hasattr(snake_case					,			"do_normalize"      )      )
      self.assertTrue(hasattr(snake_case					,			"do_resize"      )      )
      self.assertTrue(hasattr(snake_case					,			"do_center_crop"      )      )
      self.assertTrue(hasattr(snake_case					,			"size"      )      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size					,			{"shortest_edge": 18}      )
      self.assertEqual(image_processor.crop_size					,			{"height": 18, "width": 18}      )

      a__	:						Tuple      		=  self.image_processing_class.from_dict(self.image_processor_dict					,			size=42					,			crop_size=84      )
      self.assertEqual(image_processor.size					,			{"shortest_edge": 42}      )
      self.assertEqual(image_processor.crop_size					,			{"height": 84, "width": 84}      )





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Any      		=  self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a__	:						Union[str, Any]      		=  prepare_image_inputs(self.image_processor_tester					,			equal_resolution=snake_case      )
      for image in image_inputs:
         self.assertIsInstance(snake_case					,			Image.Image      )

      # Test not batched input
      a__	:						List[str]      		=  image_processing(image_inputs[0]					,			return_tensors="pt"      ).pixel_values
      self.assertEqual(
          encoded_images.shape					,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )					,			)

      # Test batched
      a__	:						List[Any]      		=  image_processing(snake_case					,			return_tensors="pt"      ).pixel_values
      self.assertEqual(
          encoded_images.shape					,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )					,			)





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Tuple      		=  self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      a__	:						str      		=  prepare_image_inputs(self.image_processor_tester					,			equal_resolution=snake_case					,			numpify=snake_case      )
      for image in image_inputs:
         self.assertIsInstance(snake_case					,			np.ndarray      )

      # Test not batched input
      a__	:						List[str]      		=  image_processing(image_inputs[0]					,			return_tensors="pt"      ).pixel_values
      self.assertEqual(
          encoded_images.shape					,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )					,			)

      # Test batched
      a__	:						str      		=  image_processing(snake_case					,			return_tensors="pt"      ).pixel_values
      self.assertEqual(
          encoded_images.shape					,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )					,			)





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Dict      		=  self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      a__	:						Dict      		=  prepare_image_inputs(self.image_processor_tester					,			equal_resolution=snake_case					,			torchify=snake_case      )
      for image in image_inputs:
         self.assertIsInstance(snake_case					,			torch.Tensor      )

      # Test not batched input
      a__	:						List[str]      		=  image_processing(image_inputs[0]					,			return_tensors="pt"      ).pixel_values
      self.assertEqual(
          encoded_images.shape					,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )					,			)

      # Test batched
      a__	:						Dict      		=  image_processing(snake_case					,			return_tensors="pt"      ).pixel_values
      self.assertEqual(
          encoded_images.shape					,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )					,			)








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

