
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  args.serialization_dir
				A_       	=  args.pruning_method
				A_       	=  args.threshold

				A_       	=  torch.load(os.path.join(__UpperCamelCase ,						'''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(__UpperCamelCase ,						__UpperCamelCase     ).sum().item()
												elif pruning_method == "sigmoied_threshold":
																A_       	=  ThresholdBinarizer.apply(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ).sum().item()
												elif pruning_method == "l0":
																A_	,					A_       	=  -0.1, 1.1
																A_       	=  torch.sigmoid(__UpperCamelCase     )
																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(1_00 * 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) %: ''' ,						1_00 * remaining_count / encoder_count     )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		List[str]					 =       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",
						)

						SCREAMING_SNAKE_CASE   :		Any					 =       parser.parse_args()

						main(args)

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker