

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 						a_			(				unittest.TestCase	):



      """simple docstring"""







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


      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           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      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=load_dataset('hf-internal-testing/fixtures_ade20k',       split='test'     )

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

     return image, map







def      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=load_dataset('hf-internal-testing/fixtures_ade20k',       split='test'     )

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

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



@require_torch
@require_vision
class 						a_			(				lowerCamelCase_					,     unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= SegformerImageProcessor if is_vision_available() else None
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=SegformerImageProcessingTester(self							)
      @property
      def 			_lowerCAmelCase	( self	:				List[str]							):
           return self.image_processor_tester.prepare_image_processor_dict()
      def 			_lowerCAmelCase	( self	:				str							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           self.assertTrue(hasattr(UpperCamelCase_     ,'do_resize'							)							)
           self.assertTrue(hasattr(UpperCamelCase_     ,'size'							)							)
           self.assertTrue(hasattr(UpperCamelCase_     ,'do_normalize'							)							)
           self.assertTrue(hasattr(UpperCamelCase_     ,'image_mean'							)							)
           self.assertTrue(hasattr(UpperCamelCase_     ,'image_std'							)							)
           self.assertTrue(hasattr(UpperCamelCase_     ,'do_reduce_labels'							)							)
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_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     ,UpperCamelCase_							)

           SCREAMING_SNAKE_CASE 		=self.image_processing_class.from_dict(self.image_processor_dict     ,size=42     ,reduce_labels=UpperCamelCase_							)
           self.assertEqual(image_processor.size     ,{'height': 42, 'width': 42}							)
           self.assertEqual(image_processor.do_reduce_labels     ,UpperCamelCase_							)
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           pass
      def 			_lowerCAmelCase	( self	:				str							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           # create random PIL images
           SCREAMING_SNAKE_CASE 		=prepare_image_inputs(self.image_processor_tester     ,equal_resolution=UpperCamelCase_							)
           for image in image_inputs:
                self.assertIsInstance(UpperCamelCase_     ,Image.Image							)

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

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

           # Test not batched input
           SCREAMING_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
           SCREAMING_SNAKE_CASE 		=image_processing(UpperCamelCase_     ,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	:				int							):
           SCREAMING_SNAKE_CASE 		=self.image_processing_class(**self.image_processor_dict							)
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE 		=prepare_image_inputs(self.image_processor_tester     ,equal_resolution=UpperCamelCase_     ,torchify=UpperCamelCase_							)
           SCREAMING_SNAKE_CASE 		=[]
           for image in image_inputs:
                self.assertIsInstance(UpperCamelCase_     ,torch.Tensor							)
                maps.append(torch.zeros(image.shape[-2:]							).long()							)

           # Test not batched input
           SCREAMING_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
           SCREAMING_SNAKE_CASE 		=image_processing(UpperCamelCase_     ,UpperCamelCase_     ,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)
           SCREAMING_SNAKE_CASE 		=prepare_semantic_single_inputs()

           SCREAMING_SNAKE_CASE 		=image_processing(UpperCamelCase_     ,UpperCamelCase_     ,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)
           SCREAMING_SNAKE_CASE 		=prepare_semantic_batch_inputs()

           SCREAMING_SNAKE_CASE 		=image_processing(UpperCamelCase_     ,UpperCamelCase_     ,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	:				Optional[Any]							):
           SCREAMING_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
           SCREAMING_SNAKE_CASE 		=prepare_semantic_single_inputs()
           SCREAMING_SNAKE_CASE 		=image_processing(UpperCamelCase_     ,UpperCamelCase_     ,return_tensors='pt'							)
           self.assertTrue(encoding['labels'].min().item() >= 0							)
           self.assertTrue(encoding['labels'].max().item() <= 150							)

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






'''simple docstring'''

from __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						if node is None:
							return

						if path_sum == self.target:
							self.paths += 1

						if node.left:
							self.depth_first_search(node.left  ,	path_sum + node.left.value       )
						if node.right:
							self.depth_first_search(node.right  ,	path_sum + node.right.value       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

						self.depth_first_search(UpperCamelCase_  ,	node.value       )
						self.path_sum(node.left       )
						self.path_sum(node.right       )

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
