
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def        lowerCamelCase_      (			UpperCAmelCase_							:				int       ):
						lowercase       :    Union[str, Any]											=  args.serialization_dir
						lowercase       :    str											=  args.pruning_method
						lowercase       :    List[str]											=  args.threshold

						lowercase       :    List[Any]											=  torch.load(os.path.join(UpperCAmelCase_							,			'''pytorch_model.bin'''       )							,			map_location='''cpu'''       )

						lowercase       :    Any											=  0  # Number of remaining (not pruned) params in the encoder
						lowercase       :    List[str]											=  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":
																								lowercase       :    List[Any]											=  TopKBinarizer.apply(UpperCAmelCase_							,			UpperCAmelCase_       ).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								lowercase       :    List[str]											=  ThresholdBinarizer.apply(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       ).sum().item()
																		elif pruning_method == "l0":
																								lowercase				,				lowercase       :    Union[str, Any]											=  -0.1, 1.1
																								lowercase       :    List[Any]											=  torch.sigmoid(UpperCAmelCase_       )
																								lowercase       :    List[str]											=  s * (r - l) + l
																								lowercase       :    List[str]											=  s_bar.clamp(min=0.0							,			max=1.0       )
																								lowercase       :    Union[str, Any]											=  (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__":
				snake_case__  							=							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""",
				)

				snake_case__  							=							parser.parse_args()

				main(args)




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

											self.register_modules(unet=lowerCAmelCase ,      scheduler=lowerCAmelCase					)




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


