


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 	a__						(    unittest.TestCase     ):

       def __init__( self   :  Union[str, Any]   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  Dict=7   ,				lowerCamelCase_   :  Optional[Any]=3   ,				lowerCamelCase_   :  Union[str, Any]=1_8   ,				lowerCamelCase_   :  Any=3_0   ,				lowerCamelCase_   :  int=4_0_0   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  Tuple=None   ,				):
            a_     :  Dict				      =     size if size is not None else {"""shortest_edge""": 2_0}
            a_     :  Optional[int]				      =     crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
            a_     :  Tuple				      =     parent
            a_     :  Optional[int]				      =     batch_size
            a_     :  Union[str, Any]				      =     num_channels
            a_     :  List[str]				      =     image_size
            a_     :  int				      =     min_resolution
            a_     :  Union[str, Any]				      =     max_resolution
            a_     :  Union[str, Any]				      =     do_resize
            a_     :  int				      =     size
            a_     :  int				      =     do_center_crop
            a_     :  Any				      =     crop_size

       def     UpperCAmelCase( self   :  List[Any]     ):
            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 	a__						(    lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  Union[str, Any]				     =  MobileNetVaImageProcessor if is_vision_available() else None

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Any				      =     MobileNetVaImageProcessingTester(self     )

       @property
       def     UpperCAmelCase( self   :  Optional[int]     ):
            return self.image_processor_tester.prepare_image_processor_dict()

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  int				      =     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_   ,				"""center_crop"""     )     )

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  List[Any]				      =     self.image_processing_class.from_dict(self.image_processor_dict     )
            self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 2_0}     )
            self.assertEqual(image_processor.crop_size   ,				{"""height""": 1_8, """width""": 1_8}     )

            a_     :  str				      =     self.image_processing_class.from_dict(self.image_processor_dict   ,				size=4_2   ,				crop_size=8_4     )
            self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 4_2}     )
            self.assertEqual(image_processor.crop_size   ,				{"""height""": 8_4, """width""": 8_4}     )

       def     UpperCAmelCase( self   :  List[str]     ):
            pass

       def     UpperCAmelCase( self   :  Any     ):
            # Initialize image_processing
            a_     :  str				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random PIL images
            a_     :  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
            a_     :  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
            a_     :  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"""],
                )   ,				)

       def     UpperCAmelCase( self   :  Optional[int]     ):
            # Initialize image_processing
            a_     :  int				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random numpy tensors
            a_     :  Union[str, Any]				      =     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
            a_     :  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
            a_     :  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     UpperCAmelCase( self   :  Optional[Any]     ):
            # Initialize image_processing
            a_     :  Optional[Any]				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random PyTorch tensors
            a_     :  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
            a_     :  List[str]				      =     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
            a_     :  int				      =     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"""],
                )   ,				)







__lowerCamelCase   	=  {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase   	=  {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Tuple				      =     True
     a_     :  Optional[Any]				      =     []

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               connected_verts += dfs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)

     return [vert, *connected_verts]



def 			_a      (    __UpperCamelCase			):
     a_     :  Dict				      =     len(__UpperCamelCase			)
     a_     :  List[str]				      =     graph_size * [False]
     a_     :  Dict				      =     []

     for i in range(__UpperCamelCase			):
          if not visited[i]:
               a_     :  str				      =     dfs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
               components_list.append(__UpperCamelCase			)

     return components_list


if __name__ == "__main__":
       import doctest

       doctest.testmod()



