"""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 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__=0.0_1       ,  a__=1000						):
       _lowerCAmelCase		: Any						    =  p_stop
       _lowerCAmelCase		: Tuple						    =  max_length






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







class 		__A							(				unittest.TestCase	):






     def 					__A						(	self       ,  a__       ,  a__       ,  a__=False       ,  a__=True						):
       _lowerCAmelCase		: Tuple						    =  [
           BatchSamplerShard(__A       ,  2       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)
           for i in range(2						)
       ]
       _lowerCAmelCase		: Dict						    =  [list(__A						) for batch_sampler_shard in batch_sampler_shards]
       if not split_batches:
         self.assertListEqual([len(__A						) for shard in batch_sampler_shards]       ,  [len(__A						) for e in expected]						)
       self.assertListEqual(__A       ,  __A						)






     def 					__A						(	self						):
       # Check the shards when the dataset is a round multiple of total batch size.
       _lowerCAmelCase		: int						    =  BatchSampler(range(24						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Tuple						    =  [
           [[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(__A       ,  __A						)

       _lowerCAmelCase		: Any						    =  BatchSampler(range(24						)       ,  batch_size=3       ,  drop_last=__A						)
       # Expected shouldn't change
       self.check_batch_sampler_shards(__A       ,  __A						)

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       _lowerCAmelCase		: int						    =  BatchSampler(range(21						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: int						    =  [
           [[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(__A       ,  __A						)

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

       # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
       # num_processes batch.
       _lowerCAmelCase		: Optional[int]						    =  BatchSampler(range(22						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: List[Any]						    =  [
           [[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(__A       ,  __A						)

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

       # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
       # num_processes batch.
       _lowerCAmelCase		: Any						    =  BatchSampler(range(20						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: int						    =  [
           [[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(__A       ,  __A						)

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

       # Check the shards when the dataset is very small.
       _lowerCAmelCase		: Dict						    =  BatchSampler(range(2						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: List[Any]						    =  [[[0, 1, 0]], [[1, 0, 1]]]
       self.check_batch_sampler_shards(__A       ,  __A						)

       _lowerCAmelCase		: Tuple						    =  BatchSampler(range(2						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Dict						    =  [[], []]
       self.check_batch_sampler_shards(__A       ,  __A						)






     def 					__A						(	self						):
       # Check the shards when the dataset is a round multiple of batch size.
       _lowerCAmelCase		: Any						    =  BatchSampler(range(24						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Dict						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A						)

       _lowerCAmelCase		: Optional[Any]						    =  BatchSampler(range(24						)       ,  batch_size=4       ,  drop_last=__A						)
       # Expected shouldn't change
       self.check_batch_sampler_shards(__A       ,  __A       ,  split_batches=__A						)

       # Check the shards when the dataset is not a round multiple of batch size.
       _lowerCAmelCase		: int						    =  BatchSampler(range(22						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: List[str]						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A						)

       _lowerCAmelCase		: Union[str, Any]						    =  BatchSampler(range(22						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Any						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A						)

       # Check the shards when the dataset is not a round multiple of batch size or num_processes.
       _lowerCAmelCase		: Any						    =  BatchSampler(range(21						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Dict						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A						)

       _lowerCAmelCase		: List[Any]						    =  BatchSampler(range(21						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Tuple						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A						)

       # Check the shards when the dataset is very small.
       _lowerCAmelCase		: Tuple						    =  BatchSampler(range(2						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: List[Any]						    =  [[[0, 1]], [[0, 1]]]
       self.check_batch_sampler_shards(__A       ,  __A       ,  split_batches=__A						)

       _lowerCAmelCase		: Dict						    =  BatchSampler(range(2						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: int						    =  [[], []]
       self.check_batch_sampler_shards(__A       ,  __A       ,  split_batches=__A						)






     def 					__A						(	self						):
       # Check the shards when the dataset is a round multiple of total batch size.
       _lowerCAmelCase		: List[Any]						    =  BatchSampler(range(24						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Tuple						    =  [
           [[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(__A       ,  __A       ,  even_batches=__A						)

       _lowerCAmelCase		: str						    =  BatchSampler(range(24						)       ,  batch_size=3       ,  drop_last=__A						)
       # Expected shouldn't change
       self.check_batch_sampler_shards(__A       ,  __A       ,  even_batches=__A						)

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       _lowerCAmelCase		: List[str]						    =  BatchSampler(range(21						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Tuple						    =  [
           [[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(__A       ,  __A       ,  even_batches=__A						)

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

       # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
       # num_processes batch.
       _lowerCAmelCase		: Optional[Any]						    =  BatchSampler(range(22						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Optional[Any]						    =  [
           [[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(__A       ,  __A       ,  even_batches=__A						)

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

       # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
       # num_processes batch.
       _lowerCAmelCase		: Optional[Any]						    =  BatchSampler(range(20						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: List[Any]						    =  [
           [[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(__A       ,  __A       ,  even_batches=__A						)

       _lowerCAmelCase		: List[str]						    =  BatchSampler(range(20						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Union[str, Any]						    =  [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__A       ,  __A       ,  even_batches=__A						)

       # Check the shards when the dataset is very small.
       _lowerCAmelCase		: Dict						    =  BatchSampler(range(2						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: Optional[int]						    =  [[[0, 1]], []]
       self.check_batch_sampler_shards(__A       ,  __A       ,  even_batches=__A						)

       _lowerCAmelCase		: Optional[Any]						    =  BatchSampler(range(2						)       ,  batch_size=3       ,  drop_last=__A						)
       _lowerCAmelCase		: List[str]						    =  [[], []]
       self.check_batch_sampler_shards(__A       ,  __A       ,  even_batches=__A						)






     def 					__A						(	self						):
       # Check the shards when the dataset is a round multiple of batch size.
       _lowerCAmelCase		: Any						    =  BatchSampler(range(24						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Any						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       _lowerCAmelCase		: Union[str, Any]						    =  BatchSampler(range(24						)       ,  batch_size=4       ,  drop_last=__A						)
       # Expected shouldn't change
       self.check_batch_sampler_shards(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       # Check the shards when the dataset is not a round multiple of batch size.
       _lowerCAmelCase		: Optional[Any]						    =  BatchSampler(range(22						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Any						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       _lowerCAmelCase		: List[Any]						    =  BatchSampler(range(22						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Any						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       # Check the shards when the dataset is not a round multiple of batch size or num_processes.
       _lowerCAmelCase		: str						    =  BatchSampler(range(21						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Optional[Any]						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       _lowerCAmelCase		: Optional[int]						    =  BatchSampler(range(21						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: str						    =  [
           [[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(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       # Check the shards when the dataset is very small.
       _lowerCAmelCase		: int						    =  BatchSampler(range(2						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Optional[Any]						    =  [[[0, 1]], []]
       self.check_batch_sampler_shards(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)

       _lowerCAmelCase		: Optional[Any]						    =  BatchSampler(range(2						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Union[str, Any]						    =  [[], []]
       self.check_batch_sampler_shards(__A       ,  __A       ,  split_batches=__A       ,  even_batches=__A						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
       _lowerCAmelCase		: List[str]						    =  [BatchSamplerShard(__A       ,  2       ,  __A       ,  even_batches=__A						) 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 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__=False       ,  a__=2       ,  a__=False						):
       random.seed(__A						)
       _lowerCAmelCase		: str						    =  list(__A						)

       _lowerCAmelCase		: List[str]						    =  [
           IterableDatasetShard(
               __A       ,  batch_size=__A       ,  drop_last=__A       ,  num_processes=__A       ,  process_index=__A       ,  split_batches=__A       ,  )
           for i in range(__A						)
       ]
       _lowerCAmelCase		: Dict						    =  []
       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(__A						)
         iterable_dataset_lists.append(list(__A						)						)

       _lowerCAmelCase		: Union[str, Any]						    =  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
       _lowerCAmelCase		: Any						    =  iterable_dataset_lists[0]
       for l in iterable_dataset_lists[1:]:
         self.assertEqual(len(__A						)       ,  len(__A						)						)
         self.assertTrue(len(__A						) % shard_batch_size == 0						)

       _lowerCAmelCase		: Optional[int]						    =  []
       for idx in range(0       ,  len(__A						)       ,  __A						):
         for l in iterable_dataset_lists:
           observed += l[idx : idx + shard_batch_size]

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






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  42
       _lowerCAmelCase		: Optional[int]						    =  RandomIterableDataset()

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

       # Edge case with a very small dataset
       _lowerCAmelCase		: Tuple						    =  RandomIterableDataset(max_length=2						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  BatchSampler(range(16						)       ,  batch_size=4       ,  drop_last=__A						)
       _lowerCAmelCase		: Tuple						    =  SkipBatchSampler(__A       ,  2						)
       self.assertListEqual(list(__A						)       ,  [[8, 9, 10, 11], [12, 13, 14, 15]]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  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 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  DataLoader(list(range(16						)						)       ,  batch_size=4						)
       _lowerCAmelCase		: List[str]						    =  skip_first_batches(__A       ,  num_batches=2						)
       self.assertListEqual([t.tolist() for t in new_dataloader]       ,  [[8, 9, 10, 11], [12, 13, 14, 15]]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  DataLoaderShard(list(range(16						)						)       ,  batch_size=4						)
       for idx, _ in enumerate(__A						):
         self.assertEqual(dataloader.end_of_dataloader       ,  idx == 3						)

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






     def 					__A						(	self						):
       Accelerator()
       _lowerCAmelCase		: List[Any]						    =  DataLoaderDispatcher(range(16						)       ,  batch_size=4						)
       for idx, _ in enumerate(__A						):
         self.assertEqual(dataloader.end_of_dataloader       ,  idx == 3						)

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





"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple			) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  int(_lowerCamelCase			)
  assert noofclusters < len(_lowerCamelCase			)

  # Find out the dimensionality
  _lowerCAmelCase		: Any						    =  len(vectors[0]			)

  # Will help select random centroids from among the available vectors
  _lowerCAmelCase		: Any						    =  list(range(len(_lowerCamelCase			)			)			)
  shuffle(_lowerCamelCase			)

  # GRAPH OF COMPUTATION
  # We initialize a new graph and set it as the default during each run
  # of this algorithm. This ensures that as this function is called
  # multiple times, the default graph doesn't keep getting crowded with
  # unused ops and Variables from previous function calls.

  _lowerCAmelCase		: List[Any]						    =  tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    _lowerCAmelCase		: str						    =  tf.Session()

    ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

    ##First lets ensure we have a Variable vector for each centroid,
    ##initialized to one of the vectors from the available data points
    _lowerCAmelCase		: List[str]						    =  [
        tf.Variable(vectors[vector_indices[i]]			) for i in range(_lowerCamelCase			)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float64"""    ,[dim]			)
    _lowerCAmelCase		: Optional[int]						    =  []
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    _lowerCAmelCase		: Dict						    =  [tf.Variable(0			) for i in range(len(_lowerCamelCase			)			)]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    _lowerCAmelCase		: List[Any]						    =  tf.placeholder("""int32"""			)
    _lowerCAmelCase		: Any						    =  []
    for assignment in assignments:
      cluster_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float"""    ,[None, dim]			)
    # The Node/op takes the input and computes a mean along the 0th
    # dimension, i.e. the list of input vectors
    _lowerCAmelCase		: Optional[int]						    =  tf.reduce_mean(_lowerCamelCase    ,0			)

    ##Node for computing Euclidean distances
    # Placeholders for input
    _lowerCAmelCase		: Dict						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: List[Any]						    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase    ,_lowerCamelCase			)    ,2			)			)			)

    ##This node will figure out which cluster to assign a vector to,
    ##based on Euclidean distances of the vector from the centroids.
    # Placeholder for input
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[noofclusters]			)
    _lowerCAmelCase		: str						    =  tf.argmin(_lowerCamelCase    ,0			)

    ##INITIALIZING STATE VARIABLES

    ##This will help initialization of all Variables defined with respect
    ##to the graph. The Variable-initializer should be defined after
    ##all the Variables have been constructed, so that each of them
    ##will be included in the initialization.
    _lowerCAmelCase		: Optional[Any]						    =  tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_lowerCamelCase			)

    ##CLUSTERING ITERATIONS

    # Now perform the Expectation-Maximization steps of K-Means clustering
    # iterations. To keep things simple, we will only do a set number of
    # iterations, instead of using a Stopping Criterion.
    _lowerCAmelCase		: List[str]						    =  100
    for _ in range(_lowerCamelCase			):
      ##EXPECTATION STEP
      ##Based on the centroid locations till last iteration, compute
      ##the _expected_ centroid assignments.
      # Iterate over each vector
      for vector_n in range(len(_lowerCamelCase			)			):
        _lowerCAmelCase		: int						    =  vectors[vector_n]
        # Compute Euclidean distance between this vector and each
        # centroid. Remember that this list cannot be named
        #'centroid_distances', since that is the input to the
        # cluster assignment node.
        _lowerCAmelCase		: Any						    =  [
            sess.run(_lowerCamelCase    ,feed_dict={va: vect, va: sess.run(_lowerCamelCase			)}			)
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        _lowerCAmelCase		: Any						    =  sess.run(
            _lowerCamelCase    ,feed_dict={centroid_distances: distances}			)
        # Now assign the value to the appropriate state variable
        sess.run(
            cluster_assigns[vector_n]    ,feed_dict={assignment_value: assignment}			)

      ##MAXIMIZATION STEP
      # Based on the expected state computed from the Expectation Step,
      # compute the locations of the centroids so as to maximize the
      # overall objective of minimizing within-cluster Sum-of-Squares
      for cluster_n in range(_lowerCamelCase			):
        # Collect all the vectors assigned to this cluster
        _lowerCAmelCase		: List[Any]						    =  [
            vectors[i]
            for i in range(len(_lowerCamelCase			)			)
            if sess.run(assignments[i]			) == cluster_n
        ]
        # Compute new centroid location
        _lowerCAmelCase		: Optional[int]						    =  sess.run(
            _lowerCamelCase    ,feed_dict={mean_input: array(_lowerCamelCase			)}			)
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]    ,feed_dict={centroid_value: new_location}			)

        # Return centroids and assignments
    _lowerCAmelCase		: Optional[int]						    =  sess.run(_lowerCamelCase			)
    _lowerCAmelCase		: List[Any]						    =  sess.run(_lowerCamelCase			)
    return centroids, assignments




