

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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import CLIPImageProcessor






class     __magic_name__     (							unittest.TestCase     ):


 def __init__(    self		:			Dict      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			List[Any]=7      ,    lowerCAmelCase__		:			Tuple=3      ,    lowerCAmelCase__		:			List[Any]=1_8      ,    lowerCAmelCase__		:			Dict=3_0      ,    lowerCAmelCase__		:			Tuple=4_0_0      ,    lowerCAmelCase__		:			Dict=True      ,    lowerCAmelCase__		:			Dict=None      ,    lowerCAmelCase__		:			List[Any]=True      ,    lowerCAmelCase__		:			List[str]=None      ,    lowerCAmelCase__		:			Optional[int]=True      ,    lowerCAmelCase__		:			Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073]      ,    lowerCAmelCase__		:			int=[0.26_862_954, 0.26_130_258, 0.27_577_711]      ,    lowerCAmelCase__		:			Dict=True      ,    )     ->   List[Any]:
   UpperCAmelCase          =	size if size is not None else {"shortest_edge": 2_0}
   UpperCAmelCase          =	crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
   UpperCAmelCase          =	parent
   UpperCAmelCase          =	batch_size
   UpperCAmelCase          =	num_channels
   UpperCAmelCase          =	image_size
   UpperCAmelCase          =	min_resolution
   UpperCAmelCase          =	max_resolution
   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	do_center_crop
   UpperCAmelCase          =	crop_size
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean
   UpperCAmelCase          =	image_std
   UpperCAmelCase          =	do_convert_rgb


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   Tuple:
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_convert_rgb": self.do_convert_rgb,
   }


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Optional[Any]=False      ,    lowerCAmelCase__		:			Optional[Any]=False      ,    lowerCAmelCase__		:			Union[str, Any]=False     )     ->   List[Any]:
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   if equal_resolution:
     UpperCAmelCase          =	[]
     for i in range(self.batch_size     ):
       image_inputs.append(
           np.random.randint(
               2_5_5      ,    size=(self.num_channels, self.max_resolution, self.max_resolution)      ,    dtype=np.uinta     )     )
   else:
     UpperCAmelCase          =	[]
     for i in range(self.batch_size     ):
       UpperCAmelCase   ,						UpperCAmelCase          =	np.random.choice(np.arange(self.min_resolution      ,    self.max_resolution     )      ,    2     )
       image_inputs.append(np.random.randint(2_5_5      ,    size=(self.num_channels, width, height)      ,    dtype=np.uinta     )     )

   if not numpify and not torchify:
     # PIL expects the channel dimension as last dimension
     UpperCAmelCase          =	[Image.fromarray(np.moveaxis(lowerCAmelCase__      ,    0      ,    -1     )     ) for x in image_inputs]

   if torchify:
     UpperCAmelCase          =	[torch.from_numpy(lowerCAmelCase__     ) for x in image_inputs]

   return image_inputs






@require_torch
@require_vision
class     __magic_name__     (							_snake_case						,    unittest.TestCase     ):
 UpperCAmelCase				     =    CLIPImageProcessor if is_vision_available() else None


 def _UpperCamelCase						(    self		:			int     )     ->   List[str]:
   UpperCAmelCase          =	CLIPImageProcessingTester(self     )


 @property
 def _UpperCamelCase						(    self		:			str     )     ->   Union[str, Any]:
   return self.image_processor_tester.prepare_image_processor_dict()


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   int:
   UpperCAmelCase          =	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"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_normalize"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "image_mean"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "image_std"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_convert_rgb"     )     )


 def _UpperCamelCase						(    self		:			str     )     ->   Union[str, Any]:
   UpperCAmelCase          =	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}     )

   UpperCAmelCase          =	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		:			int     )     ->   Optional[int]:
   pass


 def _UpperCamelCase						(    self		:			int     )     ->   str:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase          =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    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.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	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		:			List[Any]     )     ->   Tuple:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase          =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__      ,    numpify=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    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.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	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		:			List[str]     )     ->   List[Any]:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase          =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__      ,    torchify=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    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.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	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"],
       )      ,    )






@require_torch
@require_vision
class     __magic_name__     (							_snake_case						,    unittest.TestCase     ):
 UpperCAmelCase				     =    CLIPImageProcessor if is_vision_available() else None


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   Optional[int]:
   UpperCAmelCase          =	CLIPImageProcessingTester(self      ,    num_channels=4     )
   UpperCAmelCase          =	3


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


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   List[Any]:
   UpperCAmelCase          =	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"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_normalize"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "image_mean"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "image_std"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_convert_rgb"     )     )


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   List[Any]:
   pass


 def _UpperCamelCase						(    self		:			List[str]     )     ->   List[Any]:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase          =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    Image.Image     )

   # Test not batched input
   UpperCAmelCase          =	image_processing(image_inputs[0]      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           1,
           self.expected_encoded_image_num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	image_processing(lowerCAmelCase__      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           self.image_processor_tester.batch_size,
           self.expected_encoded_image_num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )






lowerCAmelCase__							=			float("inf")






class     __magic_name__     :


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Dict     )     ->   List[Any]:
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[[] for _ in range(lowerCAmelCase__     )]


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   Any:
   self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
   self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any     )     ->   List[str]:
   if vertex == sink or not flow:
     return flow

   for i in range(self.ptr[vertex]      ,    len(self.adj[vertex]     )     ):
     UpperCAmelCase          =	self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
       UpperCAmelCase          =	self.depth_first_search(e[0]      ,    lowerCAmelCase__      ,    min(lowerCAmelCase__      ,    e[2] - e[3]     )     )
       if p:
         self.adj[vertex][i][3] += p
         self.adj[e[0]][e[1]][3] -= p
         return p
     UpperCAmelCase          =	self.ptr[vertex] + 1
   return 0


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Tuple     )     ->   int:
   UpperCAmelCase   ,						UpperCAmelCase          =	0, source
   for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
     while True:
       UpperCAmelCase   ,						UpperCAmelCase          =	[0] * len(self.q     ), [0] * len(self.q     )
       UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	0, 1, 1
       while qi < qe and not self.lvl[sink]:
         UpperCAmelCase          =	self.q[qi]
         qi += 1
         for e in self.adj[v]:
           if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
             UpperCAmelCase          =	e[0]
             qe += 1
             UpperCAmelCase          =	self.lvl[v] + 1

       UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
       while p:
         flow += p
         UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )

       if not self.lvl[sink]:
         break

   return flow
lowerCAmelCase__							=			Dinic(10)
lowerCAmelCase__							=			0
lowerCAmelCase__							=			9
for vertex in range(1, 5):
      graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
      graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
      graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



