

'''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      __magic_name__							(			)      ->		int:
	snake_case							=					Dataset.from_dict({'i': range(1_7				)}				)
	snake_case							=					len(A				)
	snake_case							=					3
	snake_case							=					[
	    split_dataset_by_node(A       , rank=A       , world_size=A				) for rank in range(A				)
	]
	assert sum(len(A				) 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      __magic_name__							(			)      ->		List[str]:
	def gen():
		return ({"i": i} for i in range(1_7				))

	snake_case							=					3
	snake_case							=					IterableDataset.from_generator(A				)
	snake_case							=					len(list(A				)				)
	snake_case							=					[
	    split_dataset_by_node(A       , rank=A       , world_size=A				) for rank in range(A				)
	]
	assert sum(len(list(A				)				) 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      __magic_name__							(			A				)      ->		Any:
	def gen(A				):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(1_7				))

	snake_case							=					3
	snake_case							=					shards_per_node * world_size
	snake_case							=					{'shards': [F'''shard_{shard_idx}.txt''' for shard_idx in range(A				)]}
	snake_case							=					IterableDataset.from_generator(A       , gen_kwargs=A				)
	snake_case							=					len(list(A				)				)
	assert full_ds.n_shards == world_size * shards_per_node
	snake_case							=					[
	    split_dataset_by_node(A       , rank=A       , world_size=A				) for rank in range(A				)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(A				)				) 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      __magic_name__							(			)      ->		Any:
	def gen():
		return ({"i": i} for i in range(1_7				))

	snake_case							=					2
	snake_case							=					IterableDataset.from_generator(A				)
	snake_case							=					len(list(A				)				)

	snake_case							=					split_dataset_by_node(A       , rank=0       , world_size=A				).shuffle(seed=4_2				)
	assert len(list(A				)				) == 1 + full_size // world_size
	with pytest.raises(A				):
		split_dataset_by_node(A       , rank=0       , world_size=A				).shuffle()

	snake_case							=					split_dataset_by_node(full_ds.shuffle(seed=4_2				)       , rank=0       , world_size=A				)
	assert len(list(A				)				) == 1 + full_size // world_size
	with pytest.raises(A				):
		split_dataset_by_node(full_ds.shuffle()       , rank=0       , world_size=A				)

@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      __magic_name__							(			A				)      ->		Tuple:
	snake_case							=					2
	snake_case							=					get_torch_dist_unique_port()
	snake_case							=					Path(A				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	snake_case							=					F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	snake_case							=					F'''
        --streaming={streaming}
    '''.split()
	snake_case							=					[sys.executable] + distributed_args + args
	execute_subprocess_async(A       , 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      __magic_name__							(			A       , A				)      ->		Union[str, Any]:
	snake_case							=					True
	snake_case							=					get_torch_dist_unique_port()
	snake_case							=					Path(A				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	snake_case							=					F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	snake_case							=					F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
	snake_case							=					[sys.executable] + distributed_args + args
	execute_subprocess_async(A       , env=os.environ.copy()				)








from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_calculate_expected_aligned_error(
      alignment_confidence_breaks=__A ,							aligned_distance_error_probs=__A ,							)

  return {
      "aligned_confidence_probs": aligned_confidence_probs,
      "predicted_aligned_error": predicted_aligned_error,
      "max_predicted_aligned_error": max_predicted_aligned_error,
  }





def   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  torch.sum(__A      )
  UpperCAmelCase          =	logits.shape[-2]
  UpperCAmelCase          =	max(__A ,							19      )

  UpperCAmelCase          =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

  UpperCAmelCase          =	1.0 / (1 + (bin_centers**2) / (da**2))
  UpperCAmelCase          =	torch.sum(probs * tm_per_bin ,							dim=-1      )

  UpperCAmelCase          =	residue_weights / (eps + residue_weights.sum())
  UpperCAmelCase          =	torch.sum(predicted_tm_term * normed_residue_mask ,							dim=-1      )

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



