






'''simple docstring'''





def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  []
 lowerCamelCase__	=  len(lowercase__)
 for i in range(lowercase__):
  lowerCamelCase__	=  []
  for j in range(lowercase__):
   lowerCamelCase__	=  0
   for k in range(lowercase__):
    lowerCamelCase__	=  val + matrix_a[i][k] * matrix_b[k][j]
   list_a.append(lowercase__)
  matrix_c.append(lowercase__)
 return matrix_c






def  lowerCamelCase_  (     lowercase__):
 return [[int(row == column) for column in range(lowercase__)] for row in range(lowercase__)]






def  lowerCamelCase_  (     lowercase__):
 if n <= 1:
  return n
 lowerCamelCase__	=  identity(2)
 lowerCamelCase__	=  [[1, 1], [1, 0]]
 lowerCamelCase__	=  n - 1
 while n > 0:
  if n % 2 == 1:
   lowerCamelCase__	=  multiply(lowercase__					,  lowercase__)
  lowerCamelCase__	=  multiply(lowercase__					,  lowercase__)
  lowerCamelCase__	=  int(n / 2)
 return res_matrix[0][0]






def  lowerCamelCase_  (     lowercase__):
 if n <= 1:
  return n
 lowerCamelCase__	=  0
 lowerCamelCase__	=  1
 for _ in range(2					,  n + 1):
  lowerCamelCase__	,						lowerCamelCase__	=  fiba, fiba + fiba
 return fiba






def  lowerCamelCase_  (     ):
 for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
  lowerCamelCase__	=  int("".join(c for c in ordinal if c in "0123456789"))  # 1000th --> 1000
  print(
      F'''{ordinal} fibonacci number using matrix exponentiation is '''
      F'''{nth_fibonacci_matrix(lowercase__)} and using bruteforce is '''
      F'''{nth_fibonacci_bruteforce(lowercase__)}\n''')
 # from timeit import timeit
 # print(timeit("nth_fibonacci_matrix(1000000)",
 #              "from main import nth_fibonacci_matrix", number=5))
 # print(timeit("nth_fibonacci_bruteforce(1000000)",
 #              "from main import nth_fibonacci_bruteforce", number=5))
 # 2.3342058970001744
 # 57.256506615000035


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()









'''simple docstring'''





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def  lowerCamelCase_  (     ):
 lowerCamelCase__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






@pytest.mark.parametrize("shards_per_node"					,  [1, 2, 3])
def  lowerCamelCase_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

 lowerCamelCase__	=  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle(seed=42)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle()

 lowerCamelCase__	=  split_dataset_by_node(full_ds.shuffle(seed=42)					,  rank=0					,  world_size=lowercase__)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(full_ds.shuffle()					,  rank=0					,  world_size=lowercase__)






@pytest.mark.parametrize("streaming"					,  [False, True])
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())






@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,  )
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

