"""simple docstring"""




import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=10_00   )    -> Union[str, Any]:
													A__		   =							p_stop
													A__		   =							max_length





						def __iter__(				self   )    -> List[str]:
													A__		   =							0
													A__		   =							False
													while not stop and count < self.max_length:
																				yield count
																				count += 1
																				A__		   =							random.random() < self.p_stop






class 			UpperCamelCase__(	unittest.TestCase       ):





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=False						,__UpperCAmelCase=True   )    -> Any:
													A__		   =							[
													    BatchSamplerShard(__UpperCAmelCase						,2						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )
													    for i in range(2   )
													]
													A__		   =							[list(__UpperCAmelCase   ) for batch_sampler_shard in batch_sampler_shards]
													if not split_batches:
																				self.assertListEqual([len(__UpperCAmelCase   ) for shard in batch_sampler_shards]						,[len(__UpperCAmelCase   ) for e in expected]   )
													self.assertListEqual(__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> str:
													# Check the shards when the dataset is a round multiple of total batch size.
													A__		   =							BatchSampler(range(24   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							BatchSampler(range(24   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													# Expected shouldn't change
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													# Check the shards when the dataset is a round multiple of batch size but not total batch size.
													A__		   =							BatchSampler(range(21   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							BatchSampler(range(21   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
													# num_processes batch.
													A__		   =							BatchSampler(range(22   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							BatchSampler(range(22   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
													# num_processes batch.
													A__		   =							BatchSampler(range(20   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							BatchSampler(range(20   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													# Check the shards when the dataset is very small.
													A__		   =							BatchSampler(range(2   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[[[0, 1, 0]], [[1, 0, 1]]]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							BatchSampler(range(2   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[[], []]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[Any]:
													# Check the shards when the dataset is a round multiple of batch size.
													A__		   =							BatchSampler(range(24   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(24   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													# Expected shouldn't change
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size.
													A__		   =							BatchSampler(range(22   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(22   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size or num_processes.
													A__		   =							BatchSampler(range(21   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(21   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is very small.
													A__		   =							BatchSampler(range(2   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[[[0, 1]], [[0, 1]]]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(2   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[[], []]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													# Check the shards when the dataset is a round multiple of total batch size.
													A__		   =							BatchSampler(range(24   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(24   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													# Expected shouldn't change
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is a round multiple of batch size but not total batch size.
													A__		   =							BatchSampler(range(21   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(21   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
													# num_processes batch.
													A__		   =							BatchSampler(range(22   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(22   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
													# num_processes batch.
													A__		   =							BatchSampler(range(20   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(20   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
													    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is very small.
													A__		   =							BatchSampler(range(2   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[[[0, 1]], []]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(2   )						,batch_size=3						,drop_last=__UpperCAmelCase   )
													A__		   =							[[], []]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[Any]:
													# Check the shards when the dataset is a round multiple of batch size.
													A__		   =							BatchSampler(range(24   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(24   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													# Expected shouldn't change
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size.
													A__		   =							BatchSampler(range(22   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(22   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is not a round multiple of batch size or num_processes.
													A__		   =							BatchSampler(range(21   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(21   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[
													    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
													    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
													]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													# Check the shards when the dataset is very small.
													A__		   =							BatchSampler(range(2   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[[[0, 1]], []]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )

													A__		   =							BatchSampler(range(2   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							[[], []]
													self.check_batch_sampler_shards(__UpperCAmelCase						,__UpperCAmelCase						,split_batches=__UpperCAmelCase						,even_batches=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
													A__		   =							[BatchSamplerShard(__UpperCAmelCase						,2						,__UpperCAmelCase						,even_batches=__UpperCAmelCase   ) for i in range(2   )]

													self.assertEqual(len(batch_sampler_shards[0]   )						,3   )
													self.assertEqual(len(batch_sampler_shards[1]   )						,2   )

													self.assertListEqual(list(batch_sampler_shards[0]   )						,[[0, 1, 2], [5, 6, 7, 8], [12, 13]]   )
													self.assertListEqual(list(batch_sampler_shards[1]   )						,[[3, 4], [9, 10, 11]]   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=False						,__UpperCAmelCase=2						,__UpperCAmelCase=False   )    -> Optional[Any]:
													random.seed(__UpperCAmelCase   )
													A__		   =							list(__UpperCAmelCase   )

													A__		   =							[
													    IterableDatasetShard(
													        __UpperCAmelCase						,batch_size=__UpperCAmelCase						,drop_last=__UpperCAmelCase						,num_processes=__UpperCAmelCase						,process_index=__UpperCAmelCase						,split_batches=__UpperCAmelCase						,)
													    for i in range(__UpperCAmelCase   )
													]
													A__		   =							[]
													for iterable_dataset_shard in iterable_dataset_shards:
																				# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
																				random.seed(__UpperCAmelCase   )
																				iterable_dataset_lists.append(list(__UpperCAmelCase   )   )

													A__		   =							batch_size // num_processes if split_batches else batch_size
													# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
													A__		   =							iterable_dataset_lists[0]
													for l in iterable_dataset_lists[1:]:
																				self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
																				self.assertTrue(len(__UpperCAmelCase   ) % shard_batch_size == 0   )

													A__		   =							[]
													for idx in range(0						,len(__UpperCAmelCase   )						,__UpperCAmelCase   ):
																				for l in iterable_dataset_lists:
																											observed += l[idx : idx + shard_batch_size]

													if not drop_last:
																				while len(__UpperCAmelCase   ) < len(__UpperCAmelCase   ):
																											reference += reference
													self.assertListEqual(__UpperCAmelCase						,reference[: len(__UpperCAmelCase   )]   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							42
													A__		   =							RandomIterableDataset()

													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )
													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )
													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )
													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )

													# Edge case with a very small dataset
													A__		   =							RandomIterableDataset(max_length=2   )

													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )
													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )
													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )
													self.check_iterable_dataset_shards(__UpperCAmelCase						,__UpperCAmelCase						,batch_size=4						,drop_last=__UpperCAmelCase						,split_batches=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							BatchSampler(range(16   )						,batch_size=4						,drop_last=__UpperCAmelCase   )
													A__		   =							SkipBatchSampler(__UpperCAmelCase						,2   )
													self.assertListEqual(list(__UpperCAmelCase   )						,[[8, 9, 10, 11], [12, 13, 14, 15]]   )





						def  snake_case__		(				self   )    -> int:
													A__		   =							SkipDataLoader(list(range(16   )   )						,batch_size=4						,skip_batches=2   )
													self.assertListEqual([t.tolist() for t in dataloader]						,[[8, 9, 10, 11], [12, 13, 14, 15]]   )





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							DataLoader(list(range(16   )   )						,batch_size=4   )
													A__		   =							skip_first_batches(__UpperCAmelCase						,num_batches=2   )
													self.assertListEqual([t.tolist() for t in new_dataloader]						,[[8, 9, 10, 11], [12, 13, 14, 15]]   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							DataLoaderShard(list(range(16   )   )						,batch_size=4   )
													for idx, _ in enumerate(__UpperCAmelCase   ):
																				self.assertEqual(dataloader.end_of_dataloader						,idx == 3   )

													# Test it also works on the second iteration
													for idx, _ in enumerate(__UpperCAmelCase   ):
																				self.assertEqual(dataloader.end_of_dataloader						,idx == 3   )





						def  snake_case__		(				self   )    -> int:
													Accelerator()
													A__		   =							DataLoaderDispatcher(range(16   )						,batch_size=4   )
													for idx, _ in enumerate(__UpperCAmelCase   ):
																				self.assertEqual(dataloader.end_of_dataloader						,idx == 3   )

													# Test it also works on the second iteration
													for idx, _ in enumerate(__UpperCAmelCase   ):
																				self.assertEqual(dataloader.end_of_dataloader						,idx == 3   )






"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase							 =     TypeVar("T")
class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							data
													A__		   =							self
													A__		   =							0






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# map from node name to the node object
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# create a new set with x as its member
													A__		   =							DisjointSetTreeNode(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> DisjointSetTreeNode[T]:
													# find the set x belongs to (with path-compression)
													A__		   =							self.map[data]
													if elem_ref != elem_ref.parent:
																				A__		   =							self.find_set(elem_ref.parent.data   )
													return elem_ref.parent





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# helper function for union operation
													if nodea.rank > nodea.rank:
																				A__		   =							nodea
													else:
																				A__		   =							nodea
																				if nodea.rank == nodea.rank:
																											nodea.rank += 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# merge 2 disjoint sets
													self.link(self.find_set(__UpperCAmelCase   )						,self.find_set(__UpperCAmelCase   )   )






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# connections: map from the node to the neighbouring nodes (with weights)
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# add a node ONLY if its not present in the graph
													if node not in self.connections:
																				A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# add an edge with the given weight
													self.add_node(__UpperCAmelCase   )
													self.add_node(__UpperCAmelCase   )
													A__		   =							weight
													A__		   =							weight





						def  snake_case__		(				self   )    -> GraphUndirectedWeighted[T]:
													A__		   =							[]
													A__		   =							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 __UpperCAmelCase   : x[2]   )

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

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





