





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    args.serialization_dir
			UpperCAmelCase       		=    args.pruning_method
			UpperCAmelCase       		=    args.threshold

			UpperCAmelCase       		=    torch.load(os.path.join(UpperCamelCase__      ,      '''pytorch_model.bin'''			)      ,      map_location='''cpu'''			)

			UpperCAmelCase       		=    0  # Number of remaining (not pruned) params in the encoder
			UpperCAmelCase       		=    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":
												UpperCAmelCase       		=    TopKBinarizer.apply(UpperCamelCase__      ,      UpperCamelCase__			).sum().item()
									elif pruning_method == "sigmoied_threshold":
												UpperCAmelCase       		=    ThresholdBinarizer.apply(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			).sum().item()
									elif pruning_method == "l0":
												UpperCAmelCase		,      UpperCAmelCase       		=    -0.1, 1.1
												UpperCAmelCase       		=    torch.sigmoid(UpperCamelCase__			)
												UpperCAmelCase       		=    s * (r - l) + l
												UpperCAmelCase       		=    s_bar.clamp(min=0.0      ,      max=1.0			)
												UpperCAmelCase       		=    (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(UpperCamelCase__			)			)
						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    :   int          =       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    :   Any          =       parser.parse_args()

				main(args)










import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_utf16_encoded.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''      ,      encoding='''utf-16'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts_field.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}






@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			),
            },
        ),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}



