
'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 	snake_case_							(				_a  :				Tuple      ):
      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(60			,       " "      )			,       "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(60			,       " "      )			,       str(round(100 * mask_ones / param.numel()			,       3      )      ).ljust(20			,       " "      )			,       str(_a      )      )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(""      )
      print("Remaining Weights (global) %: "			,       100 * remaining_count / encoder_count      )


if __name__ == "__main__":
     A							     = 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",
     )

     A							     = parser.parse_args()

     main(args)



'''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 	snake_case_							(				):
      snake_case_    							=				Dataset.from_dict({"i": range(17      )}      )
      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 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

      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 	snake_case_							(				_a  :				List[Any]      ):
      def gen(_a  :				Dict      ):
            for shard in shards:
                  yield from ({"i": i, "shard": shard} for i in range(17      ))

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

      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=42      )
      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=42      )			,       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 	snake_case_							(				_a  :				Any      ):
      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 	snake_case_							(				_a  :				int			,       _a  :				List[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()      )

