





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 SwinaSRImageProcessor
    from transformers.image_transforms import get_image_size







class 		_lowerCAmelCase(		unittest.TestCase):

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=7    ,							UpperCAmelCase=3    ,							UpperCAmelCase=18    ,							UpperCAmelCase=30    ,							UpperCAmelCase=4_00    ,							UpperCAmelCase=True    ,							UpperCAmelCase=1 / 2_55    ,							UpperCAmelCase=True    ,							UpperCAmelCase=8    ,							)->   Dict:
     __A    =  parent
     __A    =  batch_size
     __A    =  num_channels
     __A    =  image_size
     __A    =  min_resolution
     __A    =  max_resolution
     __A    =  do_rescale
     __A    =  rescale_factor
     __A    =  do_pad
     __A    =  pad_size

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     return {
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
         "pad_size": self.pad_size,
     }





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase=False    ,							UpperCAmelCase=False    ,							UpperCAmelCase=False      )->   int:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
        __A    =  []
        for i in range(self.batch_size      ):
           image_inputs.append(
               np.random.randint(
                   2_55    ,							size=(self.num_channels, self.max_resolution, self.max_resolution)    ,							dtype=np.uinta      )      )
     else:
        __A    =  []
        for i in range(self.batch_size      ):
           __A   ,		__A    =  np.random.choice(np.arange(self.min_resolution    ,							self.max_resolution      )    ,							2      )
           image_inputs.append(np.random.randint(2_55    ,							size=(self.num_channels, width, height)    ,							dtype=np.uinta      )      )

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

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

     return image_inputs







@require_torch
@require_vision
class 		_lowerCAmelCase(		_a						,       unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__     	=       SwinaSRImageProcessor if is_vision_available() else None

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[Any]:
     __A    =  SwinaSRImageProcessingTester(self      )

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

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[Any]:
     __A    =  self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(UpperCAmelCase    ,							'''do_rescale'''      )      )
     self.assertTrue(hasattr(UpperCAmelCase    ,							'''rescale_factor'''      )      )
     self.assertTrue(hasattr(UpperCAmelCase    ,							'''do_pad'''      )      )
     self.assertTrue(hasattr(UpperCAmelCase    ,							'''pad_size'''      )      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     pass

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[str]:
     __A   ,		__A    =  get_image_size(UpperCAmelCase      )
     __A    =  self.image_processor_tester.pad_size

     __A    =  (old_height // size + 1) * size - old_height
     __A    =  (old_width // size + 1) * size - old_width
     return old_height + pad_height, old_width + pad_width

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     # Initialize image_processor
     __A    =  self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     __A    =  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase      )
     for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase    ,							Image.Image      )

     # Test not batched input
     __A    =  image_processor(image_inputs[0]    ,							return_tensors='''pt'''      ).pixel_values
     __A   ,		__A    =  self.calculate_expected_size(np.array(image_inputs[0]      )      )
     self.assertEqual(
         encoded_images.shape    ,							(
             1,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )    ,							)

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     # Initialize image_processor
     __A    =  self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     __A    =  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase    ,							numpify=UpperCAmelCase      )
     for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase    ,							np.ndarray      )

     # Test not batched input
     __A    =  image_processor(image_inputs[0]    ,							return_tensors='''pt'''      ).pixel_values
     __A   ,		__A    =  self.calculate_expected_size(image_inputs[0]      )
     self.assertEqual(
         encoded_images.shape    ,							(
             1,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )    ,							)





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     # Initialize image_processor
     __A    =  self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     __A    =  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase    ,							torchify=UpperCAmelCase      )
     for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase    ,							torch.Tensor      )

     # Test not batched input
     __A    =  image_processor(image_inputs[0]    ,							return_tensors='''pt'''      ).pixel_values
     __A   ,		__A    =  self.calculate_expected_size(image_inputs[0]      )
     self.assertEqual(
         encoded_images.shape    ,							(
             1,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )    ,							)








from collections import deque
def  __UpperCamelCase    (						snake_case      )    ->		List[str]:


   '''simple docstring'''







   return input(snake_case      ).strip().split(''' '''      )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		dict[int, list[int]]:


   '''simple docstring'''







   __A    =  {}
   for i in range(snake_case      ):
      __A    =  []

   for e in range(snake_case      ):
      __A   ,		__A    =  (int(snake_case      ) for i in _input(F"Edge {e + 1}: <node1> <node2> "      ))
      graph[x].append(snake_case      )
   return graph
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		dict[int, list[int]]:


   '''simple docstring'''







   __A    =  {}
   for i in range(snake_case      ):
      __A    =  []

   for e in range(snake_case      ):
      __A   ,		__A    =  (int(snake_case      ) for i in _input(F"Edge {e + 1}: <node1> <node2> "      ))
      graph[x].append(snake_case      )
      graph[y].append(snake_case      )
   return graph
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		dict[int, list[tuple[int, int]]]:


   '''simple docstring'''







   __A    =  {}
   for i in range(snake_case      ):
      __A    =  []

   for e in range(snake_case      ):
      __A   ,		__A   ,		__A    =  (int(snake_case      ) for i in _input(F"Edge {e + 1}: <node1> <node2> <weight> "      ))
      graph[x].append((y, w)      )
      graph[y].append((x, w)      )
   return graph


if __name__ == "__main__":
    _UpperCamelCase       ,    _UpperCamelCase       :				List[Any]           =			(int(i) for i in _input("""Number of nodes and edges: """))

    _UpperCamelCase       :				int           =			int(
        _input(
            """Press 1 or 2 or 3 \n"""
            """1. Unweighted directed \n"""
            """2. Unweighted undirected \n"""
            """3. Weighted undirected \n"""
        )[0]
    )

    _UpperCamelCase       :				Optional[Any]           =			{
        1: initialize_unweighted_directed_graph,
        2: initialize_unweighted_undirected_graph,
        3: initialize_weighted_undirected_graph,
    }[graph_choice](n, m)
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''simple docstring'''







   __A   ,		__A    =  {s}, [s]
   print(snake_case      )
   while _s:
      __A    =  0
      for i in g[_s[-1]]:
         if i not in vis:
            _s.append(snake_case      )
            vis.add(snake_case      )
            __A    =  1
            print(snake_case      )
            break
      if not flag:
         _s.pop()
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		Dict:


   '''simple docstring'''







   __A   ,		__A    =  {s}, deque([s]      )
   print(snake_case      )
   while q:
      __A    =  q.popleft()
      for v in g[u]:
         if v not in vis:
            vis.add(snake_case      )
            q.append(snake_case      )
            print(snake_case      )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		List[Any]:


   '''simple docstring'''







   __A   ,		__A   ,		__A    =  {s: 0}, set(), {s: 0}
   while True:
      if len(snake_case      ) == len(snake_case      ) - 1:
         break
      __A    =  1_0_0_0_0_0
      for i in dist:
         if i not in known and dist[i] < mini:
            __A    =  dist[i]
            __A    =  i
      known.add(snake_case      )
      for v in g[u]:
         if v[0] not in known and dist[u] + v[1] < dist.get(v[0]							,			1_0_0_0_0_0      ):
            __A    =  dist[u] + v[1]
            __A    =  u
   for i in dist:
      if i != s:
         print(dist[i]      )
def  __UpperCamelCase    (						snake_case							,			snake_case=None							,			snake_case=None      )    ->		Any:


   '''simple docstring'''







   if q is None:
      __A    =  [1]
   if ind is None:
      __A    =  [0] * (len(snake_case      ) + 1)  # SInce oth Index is ignored
      for u in g:
         for v in g[u]:
            ind[v] += 1
      __A    =  deque()
      for i in g:
         if ind[i] == 0:
            q.append(snake_case      )
   if len(snake_case      ) == 0:
      return
   __A    =  q.popleft()
   print(snake_case      )
   for w in g[v]:
      ind[w] -= 1
      if ind[w] == 0:
         q.append(snake_case      )
   topo(snake_case							,			snake_case							,			snake_case      )
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







   __A    =  input().strip()
   __A    =  []
   for _ in range(snake_case      ):
      a.append(map(snake_case							,			input().strip().split()      )      )
   return a, n
def  __UpperCamelCase    (						snake_case      )    ->		List[Any]:


   '''simple docstring'''







   ((__A)   ,		(__A))    =  a_and_n
   __A    =  list(snake_case      )
   __A    =  [[0] * n for i in range(snake_case      )]
   for k in range(snake_case      ):
      for i in range(snake_case      ):
         for j in range(snake_case      ):
            if dist[i][j] > dist[i][k] + dist[k][j]:
               __A    =  dist[i][k] + dist[k][j]
               __A    =  k
   print(snake_case      )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		Dict:


   '''simple docstring'''







   __A   ,		__A   ,		__A    =  {s: 0}, set(), {s: 0}
   while True:
      if len(snake_case      ) == len(snake_case      ) - 1:
         break
      __A    =  1_0_0_0_0_0
      for i in dist:
         if i not in known and dist[i] < mini:
            __A    =  dist[i]
            __A    =  i
      known.add(snake_case      )
      for v in g[u]:
         if v[0] not in known and v[1] < dist.get(v[0]							,			1_0_0_0_0_0      ):
            __A    =  v[1]
            __A    =  u
   return dist
def  __UpperCamelCase    (						)    ->		List[str]:


   '''simple docstring'''







   __A   ,		__A    =  map(snake_case							,			input().split(''' '''      )      )
   __A    =  []
   for _ in range(snake_case      ):
      edges.append(map(snake_case							,			input().split(''' '''      )      )      )
   return edges, n
def  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   ((__A)   ,		(__A))    =  e_and_n
   e.sort(reverse=snake_case							,			key=lambda snake_case      : x[2]      )
   __A    =  [{i} for i in range(1							,			n + 1      )]
   while True:
      if len(snake_case      ) == 1:
         break
      print(snake_case      )
      __A    =  e.pop()
      for i in range(len(snake_case      )      ):
         if x[0] in s[i]:
            break
      for j in range(len(snake_case      )      ):
         if x[1] in s[j]:
            if i == j:
               break
            s[j].update(s[i]      )
            s.pop(snake_case      )
            break
def  __UpperCamelCase    (						snake_case      )    ->		Tuple:


   '''simple docstring'''







   __A    =  []
   for node in graph:
      if not graph[node]:
         isolated.append(snake_case      )
   return isolated

