


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        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






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






    def _a   (     self       )    ->			Any:
         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 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=load_dataset('hf-internal-testing/fixtures_ade20k'     ,	split='test'							)

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

     return image, map

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=load_dataset('hf-internal-testing/fixtures_ade20k'     ,	split='test'							)

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

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






@require_torch
@require_vision
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[str]    =    SegformerImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=SegformerImageProcessingTester(self       )






    @property
    def _a   (     self       )    ->			int:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			Tuple:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_reduce_labels'       )       )






    def _a   (     self       )    ->			Optional[int]:
         __UpperCamelCase       				=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   ,  A_       )

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






    def _a   (     self       )    ->			int:
         pass






    def _a   (     self       )    ->			Optional[int]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  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 _a   (     self       )    ->			Union[str, Any]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  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 _a   (     self       )    ->			Any:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  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 _a   (     self       )    ->			List[Any]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         __UpperCamelCase       				=[]
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )
              maps.append(torch.zeros(image.shape[-2:]       ).long()       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  A_   ,  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)
         __UpperCamelCase				, __UpperCamelCase       				=prepare_semantic_single_inputs()

         __UpperCamelCase       				=image_processing(A_   ,  A_   ,  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)
         __UpperCamelCase				, __UpperCamelCase       				=prepare_semantic_batch_inputs()

         __UpperCamelCase       				=image_processing(A_   ,  A_   ,  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 _a   (     self       )    ->			Optional[int]:
         # Initialize image_processing
         __UpperCamelCase       				=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
         __UpperCamelCase				, __UpperCamelCase       				=prepare_semantic_single_inputs()
         __UpperCamelCase       				=image_processing(A_   ,  A_   ,  return_tensors='pt'       )
         self.assertTrue(encoding['labels'].min().item() >= 0       )
         self.assertTrue(encoding['labels'].max().item() <= 150       )

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









_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





