


"""simple docstring"""


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def   a__			(					__lowercase )						->						Optional[int]:
				_A       	=      args.serialization_dir
				_A       	=      args.pruning_method
				_A       	=      args.threshold

				_A       	=      torch.load(os.path.join(__lowercase  ,      "pytorch_model.bin" )  ,      map_location="cpu" )

				_A       	=      0  # Number of remaining (not pruned) params in the encoder
				_A       	=      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":
																_A       	=      TopKBinarizer.apply(__lowercase  ,      __lowercase ).sum().item()
												elif pruning_method == "sigmoied_threshold":
																_A       	=      ThresholdBinarizer.apply(__lowercase  ,      __lowercase  ,      __lowercase ).sum().item()
												elif pruning_method == "l0":
																_A	,		_A       	=      -0.1, 1.1
																_A       	=      torch.sigmoid(__lowercase )
																_A       	=      s * (r - l) + l
																_A       	=      s_bar.clamp(min=0.0  ,      max=1.0 )
																_A       	=      (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(__lowercase ) )
								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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class snake_case   (	unittest.TestCase):







		@patch("diffusers.utils.hub_utils.get_full_repo_name"		)
		def 			a_			(       self   :							Any     , a__   :							Mock		)							->    None:





						'''simple docstring'''







						_A       	=      "full_repo_name"
						with TemporaryDirectory() as tmpdir:
										# Dummy args values
										_A       	=      Mock()
										_A       	=      tmpdir
										_A       	=      0
										_A       	=      "hub_token"
										_A       	=      "dataset_name"
										_A       	=      0.0_1
										_A       	=      10_00_00
										_A       	=      1_00_00
										_A       	=      0.0_1
										_A       	=      0.0_2
										_A       	=      0.0_3
										_A       	=      0.0_0_0_5
										_A       	=      0.0_0_0_0_0_1
										_A       	=      1
										_A       	=      10
										_A       	=      0.0_0_1
										_A       	=      0.1
										_A       	=      0.2
										_A       	=      True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(a__     , model_name="model_name"		)
										self.assertTrue((Path(a__		) / "README.md").is_file()		)