




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 TvltImageProcessor



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase=10,				__UpperCamelCase=10,				__UpperCamelCase=False,				__UpperCamelCase=False	):
    SCREAMING_SNAKE_CASE__								=[]
    for i in range(image_processor_tester.num_frames	):
        video.append(np.random.randint(255,				size=(image_processor_tester.num_channels, width, height),				dtype=np.uinta	)	)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        SCREAMING_SNAKE_CASE__								=[Image.fromarray(np.moveaxis(snake_case__,				0,				-1	)	) for frame in video]

    if torchify:
        SCREAMING_SNAKE_CASE__								=[torch.from_numpy(snake_case__	) for frame in video]

    return video



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase=False,				__UpperCamelCase=False,				__UpperCamelCase=False	):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    SCREAMING_SNAKE_CASE__								=[]
    for i in range(image_processor_tester.batch_size	):
        if equal_resolution:
            SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=image_processor_tester.max_resolution
        else:
            SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=np.random.choice(
                np.arange(image_processor_tester.min_resolution,				image_processor_tester.max_resolution	),				2	)
            SCREAMING_SNAKE_CASE__								=prepare_video(
                image_processor_tester=snake_case__,				width=snake_case__,				height=snake_case__,				numpify=snake_case__,				torchify=snake_case__,				)
        video_inputs.append(snake_case__	)

    return video_inputs
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: int				,_UpperCamelCase			: Union[str, Any]				,_UpperCamelCase			: Tuple=7				,_UpperCamelCase			: str=3				,_UpperCamelCase			: List[str]=4				,_UpperCamelCase			: Any=1_8				,_UpperCamelCase			: List[str]=3_0				,_UpperCamelCase			: Dict=4_0_0				,_UpperCamelCase			: int=True				,_UpperCamelCase			: Optional[Any]=None				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: List[str]=[0.5, 0.5, 0.5]				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=True				,_UpperCamelCase			: Union[str, Any]=None				,)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 1_8}
         SCREAMING_SNAKE_CASE__								=crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}

         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=num_frames
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std
         SCREAMING_SNAKE_CASE__								=do_center_crop
         SCREAMING_SNAKE_CASE__								=crop_size




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


         '''simple docstring'''





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "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					( _UpperCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Tuple									=  TvltImageProcessor if is_vision_available() else None




     def   __A				(	self			: Any						)		->   Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=TvltImageProcessorTester(self						)




     @property
     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




     def   __A				(	self			: Tuple						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=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				,"""do_center_crop"""						)						)
         self.assertTrue(hasattr(__a				,"""size"""						)						)




     def   __A				(	self			: Any						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PIL videos
         SCREAMING_SNAKE_CASE__								=prepare_video_inputs(self.image_processor_tester				,equal_resolution=__a						)
         for video in video_inputs:
             self.assertIsInstance(__a				,__a						)
             self.assertIsInstance(video[0]				,Image.Image						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processor(video_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_videos.shape				,(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processor(__a				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_videos.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )				,)




     def   __A				(	self			: List[Any]						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__								=prepare_video_inputs(self.image_processor_tester				,equal_resolution=__a				,numpify=__a						)
         for video in video_inputs:
             self.assertIsInstance(__a				,__a						)
             self.assertIsInstance(video[0]				,np.ndarray						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processor(video_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_videos.shape				,(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processor(__a				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_videos.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )				,)




     def   __A				(	self			: int						)		->   List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE__								=prepare_video_inputs(self.image_processor_tester				,equal_resolution=__a				,torchify=__a						)
         for video in video_inputs:
             self.assertIsInstance(__a				,__a						)
             self.assertIsInstance(video[0]				,torch.Tensor						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processor(video_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_videos.shape				,(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processor(__a				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_videos.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )				,)









from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase			       =			{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 						UpperCamelCase_(			snake_case__:  dict[str, list[str]]   )       -> set[tuple[str, str]]:
 UpperCAmelCase__							=				{node: {node} for node in graph}

 UpperCAmelCase__							=				{node: graph[node][:] for node in graph}

 while len(snake_case__   ) > 2:
  # Choose a random edge.
  UpperCAmelCase__							=				random.choice(list(graph_copy.keys()   )   )
  UpperCAmelCase__							=				random.choice(graph_copy[u]   )

  # Contract edge (u, v) to new node uv
  UpperCAmelCase__							=				u + v
  UpperCAmelCase__							=				list(set(graph_copy[u] + graph_copy[v]   )   )
  uv_neighbors.remove(snake_case__   )
  uv_neighbors.remove(snake_case__   )
  UpperCAmelCase__							=				uv_neighbors
  for neighbor in uv_neighbors:
   graph_copy[neighbor].append(snake_case__   )

  UpperCAmelCase__							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

  # Remove nodes u and v.
  del graph_copy[u]
  del graph_copy[v]
  for neighbor in uv_neighbors:
   if u in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )
   if v in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )

    # Find cutset.
 UpperCAmelCase__							=				[contracted_nodes[node] for node in graph_copy]
 return {
     (node, neighbor)
     for node in groups[0]
     for neighbor in graph[node]
     if neighbor in groups[1]
 }


if __name__ == "__main__":
 print(partition_graph(TEST_GRAPH))



