

from __future__ import annotations

from typing import Generic, TypeVar

_UpperCAmelCase		:       str						    =	TypeVar("""T""")







class 			lowercase    (	Generic[T]      ):




				def __init__( self				,       snake_case	):
											snake_case_          =   data
											snake_case_          =   self
											snake_case_          =   0







class 			lowercase    (	Generic[T]      ):




				def __init__( self	):
											# map from node name to the node object
											snake_case_          =   {}




				def    a							( self				,       snake_case	):
											# create a new set with x as its member
											snake_case_          =   DisjointSetTreeNode(snake_case	)




				def    a							( self				,       snake_case	):
											# find the set x belongs to (with path-compression)
											snake_case_          =   self.map[data]
											if elem_ref != elem_ref.parent:
																		snake_case_          =   self.find_set(elem_ref.parent.data	)
											return elem_ref.parent




				def    a							( self				,       snake_case				,       snake_case	):
											# helper function for union operation
											if nodea.rank > nodea.rank:
																		snake_case_          =   nodea
											else:
																		snake_case_          =   nodea
																		if nodea.rank == nodea.rank:
																									nodea.rank += 1




				def    a							( self				,       snake_case				,       snake_case	):
											# merge 2 disjoint sets
											self.link(self.find_set(snake_case	)				,       self.find_set(snake_case	)	)







class 			lowercase    (	Generic[T]      ):




				def __init__( self	):
											# connections: map from the node to the neighbouring nodes (with weights)
											snake_case_          =   {}




				def    a							( self				,       snake_case	):
											# add a node ONLY if its not present in the graph
											if node not in self.connections:
																		snake_case_          =   {}




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											# add an edge with the given weight
											self.add_node(snake_case	)
											self.add_node(snake_case	)
											snake_case_          =   weight
											snake_case_          =   weight




				def    a							( self	):
											snake_case_          =   []
											snake_case_          =   set()
											for start in self.connections:
																		for end in self.connections[start]:
																									if (start, end) not in seen:
																																seen.add((end, start)	)
																																edges.append((start, end, self.connections[start][end])	)
											edges.sort(key=lambda snake_case	: x[2]	)

											# creating the disjoint set
											snake_case_          =   DisjointSetTree[T]()
											for node in self.connections:
																		disjoint_set.make_set(snake_case	)

											# MST generation
											snake_case_          =   0
											snake_case_          =   0
											snake_case_          =   GraphUndirectedWeighted[T]()
											while num_edges < len(self.connections	) - 1:
																		snake_case_    ,   snake_case_    ,   snake_case_          =   edges[index]
																		index += 1
																		snake_case_          =   disjoint_set.find_set(snake_case	)
																		snake_case_          =   disjoint_set.find_set(snake_case	)
																		if parent_u != parent_v:
																									num_edges += 1
																									graph.add_edge(snake_case				,       snake_case				,       snake_case	)
																									disjoint_set.union(snake_case				,       snake_case	)
											return graph







import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
					import torch

					from transformers import AutoModelForImageClassification

if is_vision_available():
					from transformers import AutoImageProcessor







@require_torch
@require_vision
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											snake_case_          =   AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											model.to(snake_case	)

											from datasets import load_dataset

											snake_case_          =   load_dataset('nielsr/rvlcdip-demo'	)

											snake_case_          =   dataset['train'][0]['image'].convert('RGB'	)

											snake_case_          =   image_processor(snake_case				,       return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																		snake_case_          =   model(**snake_case	)
																		snake_case_          =   outputs.logits

											snake_case_          =   torch.Size((1, 16)	)
											self.assertEqual(logits.shape				,       snake_case	)

											snake_case_          =   torch.tensor(
											    [-0.41_58, -0.40_92, -0.43_47]				,       device=snake_case				,       dtype=torch.float				,       )
											self.assertTrue(torch.allclose(logits[0, :3]				,       snake_case				,       atol=1e-4	)	)




