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

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

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







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__:	Optional[Any]     				=      1
  while stack:
    a__:	Optional[Any]     				=      stack.pop()
    print(SCREAMING_SNAKE_CASE__						,	clothes[current_clothing]  )
    order += 1







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Any:
  a__:	Optional[Any]     				=      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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Optional[Any]:
  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)






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 EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






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



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






    @property
    def _a   (     self       )    ->			Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






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






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

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






    def _a   (     self       )    ->			List[str]:
         # 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       )    ->			List[str]:
         # 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       )    ->			Dict:
         # 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'],
             )   ,  )





