






'''simple docstring'''





from typing import Dict, Optional, Tuple

import torch






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  boundaries[1] - boundaries[0]
 lowerCamelCase__	=  boundaries + step / 2
 lowerCamelCase__	=  torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1)]					,  dim=0)
 return bin_centers






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  ):
 lowerCamelCase__	=  _calculate_bin_centers(lowercase__)
 return (
     torch.sum(aligned_distance_error_probs * bin_centers					,  dim=-1),
     bin_centers[-1],
 )






def  lowerCamelCase_  (     lowercase__					,  lowercase__ = 31					,  lowercase__ = 64					,  **lowercase__					,  ):
 lowerCamelCase__	=  torch.linspace(0					,  lowercase__					,  steps=(no_bins - 1)					,  device=logits.device)

 lowerCamelCase__	=  torch.nn.functional.softmax(lowercase__					,  dim=-1)
 lowerCamelCase__	,						lowerCamelCase__	=  _calculate_expected_aligned_error(
     alignment_confidence_breaks=lowercase__					,  aligned_distance_error_probs=lowercase__					,  )

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






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

 lowerCamelCase__	=  torch.linspace(0					,  lowercase__					,  steps=(no_bins - 1)					,  device=logits.device)

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

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

 lowerCamelCase__	=  torch.nn.functional.softmax(lowercase__					,  dim=-1)

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

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

 lowerCamelCase__	=  per_alignment * residue_weights

 lowerCamelCase__	=  (weighted == torch.max(lowercase__)).nonzero()[0]
 return per_alignment[tuple(lowercase__)]









'''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())

