
import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor







class  _lowerCamelCase			(unittest.TestCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=7							,       SCREAMING_SNAKE_CASE_=3							,       SCREAMING_SNAKE_CASE_=30							,       SCREAMING_SNAKE_CASE_=400							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]							,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]							,       SCREAMING_SNAKE_CASE_=False							,       ):
            __snake_case						       =   size if size is not None else {'height': 30, 'width': 30}
            __snake_case						       =   parent
            __snake_case						       =   batch_size
            __snake_case						       =   num_channels
            __snake_case						       =   min_resolution
            __snake_case						       =   max_resolution
            __snake_case						       =   do_resize
            __snake_case						       =   size
            __snake_case						       =   do_normalize
            __snake_case						       =   image_mean
            __snake_case						       =   image_std
            __snake_case						       =   do_reduce_labels

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





def        __lowercase(							)       ->   Optional[Any]:
       __snake_case						       =   load_dataset('hf-internal-testing/fixtures_ade20k'    ,split='test'					)

       __snake_case						       =   Image.open(dataset[0]['file']					)
       __snake_case						       =   Image.open(dataset[1]['file']					)

       return image, map
def        __lowercase(							)       ->   Optional[int]:
       __snake_case						       =   load_dataset('hf-internal-testing/fixtures_ade20k'    ,split='test'					)

       __snake_case						       =   Image.open(dataset[0]['file']					)
       __snake_case						       =   Image.open(dataset[1]['file']					)
       __snake_case						       =   Image.open(dataset[2]['file']					)
       __snake_case						       =   Image.open(dataset[3]['file']					)

       return [imagea, imagea], [mapa, mapa]







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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   SegformerImageProcessingTester(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_normalize'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'image_mean'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'image_std'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_reduce_labels'    )    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size							,       {'height': 30, 'width': 30}    )
            self.assertEqual(image_processor.do_reduce_labels							,       SCREAMING_SNAKE_CASE_    )

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

     def 						__lowerCamelCase				(  self    ):
            pass

     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.size['height'],
                    self.image_processor_tester.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.size['height'],
                    self.image_processor_tester.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.size['height'],
                    self.image_processor_tester.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.size['height'],
                    self.image_processor_tester.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.size['height'],
                    self.image_processor_tester.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.size['height'],
                    self.image_processor_tester.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_    )
            __snake_case						       =   []
            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    )
                   maps.append(torch.zeros(image.shape[-2:]    ).long()    )

            # Test not batched input
            __snake_case						       =   image_processing(image_inputs[0]							,       maps[0]							,       return_tensors='pt'    )
            self.assertEqual(
                encoding['pixel_values'].shape							,       (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(
                encoding['labels'].shape							,       (
                    1,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(encoding['labels'].dtype							,       torch.long    )
            self.assertTrue(encoding['labels'].min().item() >= 0    )
            self.assertTrue(encoding['labels'].max().item() <= 255    )

            # Test batched
            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
            self.assertEqual(
                encoding['pixel_values'].shape							,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(
                encoding['labels'].shape							,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(encoding['labels'].dtype							,       torch.long    )
            self.assertTrue(encoding['labels'].min().item() >= 0    )
            self.assertTrue(encoding['labels'].max().item() <= 255    )

            # Test not batched input (PIL images)
            __snake_case       ,       __snake_case						       =   prepare_semantic_single_inputs()

            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
            self.assertEqual(
                encoding['pixel_values'].shape							,       (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(
                encoding['labels'].shape							,       (
                    1,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(encoding['labels'].dtype							,       torch.long    )
            self.assertTrue(encoding['labels'].min().item() >= 0    )
            self.assertTrue(encoding['labels'].max().item() <= 255    )

            # Test batched input (PIL images)
            __snake_case       ,       __snake_case						       =   prepare_semantic_batch_inputs()

            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
            self.assertEqual(
                encoding['pixel_values'].shape							,       (
                    2,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(
                encoding['labels'].shape							,       (
                    2,
                    self.image_processor_tester.size['height'],
                    self.image_processor_tester.size['width'],
                )							,       )
            self.assertEqual(encoding['labels'].dtype							,       torch.long    )
            self.assertTrue(encoding['labels'].min().item() >= 0    )
            self.assertTrue(encoding['labels'].max().item() <= 255    )

     def 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )

            # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
            __snake_case       ,       __snake_case						       =   prepare_semantic_single_inputs()
            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
            self.assertTrue(encoding['labels'].min().item() >= 0    )
            self.assertTrue(encoding['labels'].max().item() <= 150    )

            __snake_case						       =   True
            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
            self.assertTrue(encoding['labels'].min().item() >= 0    )
            self.assertTrue(encoding['labels'].max().item() <= 255    )


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)
