
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_video_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import VivitImageProcessor



class 		UpperCamelCase__  (       unittest.TestCase    ):
   def __init__(self :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					List[str]=7					,				snake_case_ :					Any=3					,				snake_case_ :					int=1_0					,				snake_case_ :					Union[str, Any]=1_8					,				snake_case_ :					Any=3_0					,				snake_case_ :					List[Any]=4_0_0					,				snake_case_ :					Tuple=True					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Dict=True					,				snake_case_ :					List[Any]=[0.5, 0.5, 0.5]					,				snake_case_ :					Any=[0.5, 0.5, 0.5]					,				snake_case_ :					Optional[int]=None					,				):
    __a :  Dict						=  size if size is not None else {'''shortest_edge''': 1_8}
    __a :  Optional[Any]						=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

    __a :  List[Any]						=  parent
    __a :  Tuple						=  batch_size
    __a :  Tuple						=  num_channels
    __a :  Tuple						=  num_frames
    __a :  int						=  image_size
    __a :  Any						=  min_resolution
    __a :  List[Any]						=  max_resolution
    __a :  Optional[Any]						=  do_resize
    __a :  Tuple						=  size
    __a :  int						=  do_normalize
    __a :  int						=  image_mean
    __a :  str						=  image_std
    __a :  Union[str, Any]						=  crop_size







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "crop_size": self.crop_size,
    }





@require_torch
@require_vision
class 		UpperCamelCase__  (       __lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VivitImageProcessor if is_vision_available() else None
   def       lowerCAmelCase			(self :					int   ):
    __a :  Dict						=  VivitImageProcessingTester(self   )
   @property
   def       lowerCAmelCase			(self :					Tuple   ):
    return self.image_processor_tester.prepare_image_processor_dict()
   def       lowerCAmelCase			(self :					List[str]   ):
    __a :  Tuple						=  self.image_processing_class(**self.image_processor_dict   )
    self.assertTrue(hasattr(snake_case_					,				'''image_mean'''   )   )
    self.assertTrue(hasattr(snake_case_					,				'''image_std'''   )   )
    self.assertTrue(hasattr(snake_case_					,				'''do_normalize'''   )   )
    self.assertTrue(hasattr(snake_case_					,				'''do_resize'''   )   )
    self.assertTrue(hasattr(snake_case_					,				'''do_center_crop'''   )   )
    self.assertTrue(hasattr(snake_case_					,				'''size'''   )   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  int						=  self.image_processing_class.from_dict(self.image_processor_dict   )
    self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8}   )
    self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8}   )

    __a :  Optional[Any]						=  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       lowerCAmelCase			(self :					Optional[int]   ):
    # Initialize image_processing
    __a :  Dict						=  self.image_processing_class(**self.image_processor_dict   )
    # create random PIL videos
    __a :  List[Any]						=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=snake_case_   )
    for video in video_inputs:
     self.assertIsInstance(snake_case_					,				snake_case_   )
     self.assertIsInstance(video[0]					,				Image.Image   )

    # Test not batched input
    __a :  List[str]						=  image_processing(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
    __a :  str						=  image_processing(snake_case_					,				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       lowerCAmelCase			(self :					str   ):
    # Initialize image_processing
    __a :  Dict						=  self.image_processing_class(**self.image_processor_dict   )
    # create random numpy tensors
    __a :  List[str]						=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=snake_case_					,				numpify=snake_case_   )
    for video in video_inputs:
     self.assertIsInstance(snake_case_					,				snake_case_   )
     self.assertIsInstance(video[0]					,				np.ndarray   )

    # Test not batched input
    __a :  List[Any]						=  image_processing(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
    __a :  Tuple						=  image_processing(snake_case_					,				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       lowerCAmelCase			(self :					str   ):
    # Initialize image_processing
    __a :  Any						=  self.image_processing_class(**self.image_processor_dict   )
    # create random PyTorch tensors
    __a :  Optional[int]						=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=snake_case_					,				torchify=snake_case_   )
    for video in video_inputs:
     self.assertIsInstance(snake_case_					,				snake_case_   )
     self.assertIsInstance(video[0]					,				torch.Tensor   )

    # Test not batched input
    __a :  Optional[Any]						=  image_processing(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
    __a :  List[str]						=  image_processing(snake_case_					,				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

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes   ):
     self.m_component.update({node: node}   )
     component_size.append(1   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  self.m_component[v]

      if u_component != v_component:

       for component in (u_component, v_component):
        if (
            minimum_weight_edge[component] == -1
            or minimum_weight_edge[component][2] > w
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

       if u_component != v_component:
        mst_weight += w
        self.union(snake_case_					,				snake_case_					,				snake_case_   )
        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"   )
        num_of_components -= 1

     __a :  Optional[int]						=  [-1] * self.m_num_of_nodes
    print(f"The total weight of the minimal spanning tree is: {mst_weight}"   )





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
