

'''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()				)








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
						        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
						        "Not needed for `l0`"
						    ),
						)
						parser.add_argument(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





