'''simple docstring'''

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 MobileNetVaImageProcessor



class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=7     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=18     ,       __lowerCAmelCase=30     ,       __lowerCAmelCase=400     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=None     ,       )  ->  str:
        lowercase__    :   int						= size if size is not None else {'''shortest_edge''': 20}
        lowercase__    :   List[Any]						= crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        lowercase__    :   Optional[int]						= parent
        lowercase__    :   List[Any]						= batch_size
        lowercase__    :   Optional[int]						= num_channels
        lowercase__    :   List[Any]						= image_size
        lowercase__    :   List[Any]						= min_resolution
        lowercase__    :   Optional[int]						= max_resolution
        lowercase__    :   str						= do_resize
        lowercase__    :   Dict						= size
        lowercase__    :   Optional[int]						= do_center_crop
        lowercase__    :   Optional[Any]						= crop_size


       def _lowerCAmelCase(						self			)  ->  int:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   MobileNetVaImageProcessor if is_vision_available() else None

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   Any						= MobileNetVaImageProcessingTester(self			)

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

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Dict						= self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''do_resize'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''size'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''do_center_crop'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''crop_size'''			)			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   str						= self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 20}			)
        self.assertEqual(image_processor.crop_size     ,       {'''height''': 18, '''width''': 18}			)

        lowercase__    :   Union[str, Any]						= self.image_processing_class.from_dict(self.image_processor_dict     ,       size=42     ,       crop_size=84			)
        self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 42}			)
        self.assertEqual(image_processor.crop_size     ,       {'''height''': 84, '''width''': 84}			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        pass

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

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

        # Test not batched input
        lowercase__    :   Dict						= 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
        lowercase__    :   Tuple						= image_processing(__lowerCAmelCase     ,       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			)  ->  List[str]:
        # Initialize image_processing
        lowercase__    :   Any						= self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__    :   List[str]						= prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCAmelCase     ,       torchify=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       torch.Tensor			)

        # Test not batched input
        lowercase__    :   Union[str, Any]						= 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
        lowercase__    :   Optional[Any]						= image_processing(__lowerCAmelCase     ,       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'''],
            )     ,       )


'''simple docstring'''

class       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self			)  ->  List[str]:
        lowercase__    :   Dict						= {}

       def _lowerCAmelCase(						self			)  ->  None:
        print(self.vertex			)
        for i in self.vertex:
         print(__lowerCAmelCase     ,       ''' -> '''     ,       ''' -> '''.join([str(__lowerCAmelCase			) for j in self.vertex[i]]			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  None:
        # check if vertex is already present,
        if from_vertex in self.vertex:
         self.vertex[from_vertex].append(__lowerCAmelCase			)
        else:
         # else make a new vertex
         lowercase__    :   Union[str, Any]						= [to_vertex]

       def _lowerCAmelCase(						self			)  ->  None:
        # visited array for storing already visited nodes
        lowercase__    :   str						= [False] * len(self.vertex			)

        # call the recursive helper function
        for i in range(len(self.vertex			)			):
         if not visited[i]:
          self.dfs_recursive(__lowerCAmelCase     ,       __lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  None:
        # mark start vertex as visited
        lowercase__    :   List[str]						= True

        print(__lowerCAmelCase     ,       end=''' '''			)

        # Recur for all the vertices that are adjacent to this node
        for i in self.vertex:
         if not visited[i]:
          self.dfs_recursive(__lowerCAmelCase     ,       __lowerCAmelCase			)


if __name__ == "__main__":
  __a:			Optional[Any]	       =       Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3

