
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 OwlViTImageProcessor







class  _lowerCamelCase			(unittest.TestCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=7							,       SCREAMING_SNAKE_CASE_=3							,       SCREAMING_SNAKE_CASE_=18							,       SCREAMING_SNAKE_CASE_=30							,       SCREAMING_SNAKE_CASE_=400							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,       SCREAMING_SNAKE_CASE_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,       SCREAMING_SNAKE_CASE_=True							,       ):
            __snake_case						       =   parent
            __snake_case						       =   batch_size
            __snake_case						       =   num_channels
            __snake_case						       =   image_size
            __snake_case						       =   min_resolution
            __snake_case						       =   max_resolution
            __snake_case						       =   do_resize
            __snake_case						       =   size if size is not None else {'height': 18, 'width': 18}
            __snake_case						       =   do_center_crop
            __snake_case						       =   crop_size if crop_size is not None else {'height': 18, 'width': 18}
            __snake_case						       =   do_normalize
            __snake_case						       =   image_mean
            __snake_case						       =   image_std
            __snake_case						       =   do_convert_rgb

     def 						__lowerCamelCase				(  self    ):
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_convert_rgb": self.do_convert_rgb,
            }







@require_torch
@require_vision
class  _lowerCamelCase			(lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			OwlViTImageProcessor if is_vision_available() else None

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   OwlViTImageProcessingTester(self    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return self.image_processor_tester.prepare_image_processor_dict()

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_resize'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'size'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_center_crop'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'center_crop'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_normalize'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'image_mean'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'image_std'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_convert_rgb'    )    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size							,       {'height': 18, 'width': 18}    )
            self.assertEqual(image_processor.crop_size							,       {'height': 18, 'width': 18}    )

            __snake_case						       =   self.image_processing_class.from_dict(self.image_processor_dict							,       size=42							,       crop_size=84    )
            self.assertEqual(image_processor.size							,       {'height': 42, 'width': 42}    )
            self.assertEqual(image_processor.crop_size							,       {'height': 84, 'width': 84}    )

     def 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case						       =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE_    )

            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       Image.Image    )

            # Test not batched input
            __snake_case						       =   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
            __snake_case						       =   image_processing(SCREAMING_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 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case						       =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE_							,       numpify=SCREAMING_SNAKE_CASE_    )
            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       np.ndarray    )

            # Test not batched input
            __snake_case						       =   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
            __snake_case						       =   image_processing(SCREAMING_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 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case						       =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE_							,       torchify=SCREAMING_SNAKE_CASE_    )
            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    )

            # Test not batched input
            __snake_case						       =   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
            __snake_case						       =   image_processing(SCREAMING_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







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 0    ):
            __snake_case						       =   value
            __snake_case						       =   None
            __snake_case						       =   None





def        __lowercase(							__snake_case				:		Node | None    ,__snake_case				:		Node | None					)       ->   Node | None:
       if treea is None:
              return treea
       if treea is None:
              return treea

       __snake_case						       =   treea.value + treea.value
       __snake_case						       =   merge_two_binary_trees(treea.left    ,treea.left					)
       __snake_case						       =   merge_two_binary_trees(treea.right    ,treea.right					)
       return treea
def        __lowercase(							__snake_case				:		Node | None					)       ->   None:
       if root:
              print(root.value					)
              print_preorder(root.left					)
              print_preorder(root.right					)


if __name__ == "__main__":
 lowerCamelCase_				:							int       =    Node(1)
 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							Tuple       =    Node(3)
 lowerCamelCase_				:							int       =    Node(4)

 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							List[Any]       =    Node(4)
 lowerCamelCase_				:							List[Any]       =    Node(6)
 lowerCamelCase_				:							Optional[Any]       =    Node(9)
 lowerCamelCase_				:							List[str]       =    Node(5)

 print("Tree1 is: ")
 print_preorder(treea)
 print("Tree2 is: ")
 print_preorder(treea)
 lowerCamelCase_				:							int       =    merge_two_binary_trees(treea, treea)
 print("Merged Tree is: ")
 print_preorder(merged_tree)
