
"""simple docstring"""




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:       int   		=       False

class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self	,    _a=32				):
          set_seed(0				)
          lowercase    :				List[Any]       		=							UNetaDModel(sample_size=_a	,    in_channels=3	,    out_channels=3				)
          lowercase    :				int       		=							torch.optim.SGD(model.parameters()	,    lr=0.0_0_0_1				)
          return model, optimizer

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
          lowercase    :				Optional[Any]       		=							DDPMScheduler(
              num_train_timesteps=1_000	,    beta_start=0.0_0_0_1	,    beta_end=0.0_2	,    beta_schedule="linear"	,    clip_sample=_a	,    )
          lowercase    :				List[Any]       		=							DDIMScheduler(
              num_train_timesteps=1_000	,    beta_start=0.0_0_0_1	,    beta_end=0.0_2	,    beta_schedule="linear"	,    clip_sample=_a	,    )

          assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

          # shared batches for DDPM and DDIM
          set_seed(0				)
          lowercase    :				List[Any]       		=							[torch.randn((4, 3, 32, 32)				).clip(-1	,    1				).to(_a				) for _ in range(4				)]
          lowercase    :				Dict       		=							[torch.randn((4, 3, 32, 32)				).to(_a				) for _ in range(4				)]
          lowercase    :				Tuple       		=							[torch.randint(0	,    1_000	,    (4,)				).long().to(_a				) for _ in range(4				)]

          # train with a DDPM scheduler
          lowercase		,    lowercase    :				List[Any]       		=							self.get_model_optimizer(resolution=32				)
          model.train().to(_a				)
          for i in range(4				):
                 optimizer.zero_grad()
                 lowercase    :				Any       		=							ddpm_scheduler.add_noise(clean_images[i]	,    noise[i]	,    timesteps[i]				)
                 lowercase    :				List[Any]       		=							model(_a	,    timesteps[i]				).sample
                 lowercase    :				str       		=							torch.nn.functional.mse_loss(_a	,    noise[i]				)
                 loss.backward()
                 optimizer.step()
          del model, optimizer

          # recreate the model and optimizer, and retry with DDIM
          lowercase		,    lowercase    :				Union[str, Any]       		=							self.get_model_optimizer(resolution=32				)
          model.train().to(_a				)
          for i in range(4				):
                 optimizer.zero_grad()
                 lowercase    :				List[str]       		=							ddim_scheduler.add_noise(clean_images[i]	,    noise[i]	,    timesteps[i]				)
                 lowercase    :				Optional[int]       		=							model(_a	,    timesteps[i]				).sample
                 lowercase    :				List[Any]       		=							torch.nn.functional.mse_loss(_a	,    noise[i]				)
                 loss.backward()
                 optimizer.step()
          del model, optimizer

          self.assertTrue(torch.allclose(_a	,    _a	,    atol=1E-5				)				)
          self.assertTrue(torch.allclose(_a	,    _a	,    atol=1E-5				)				)




import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

			_snake_case		=			args.model_name_or_path.rstrip('/' )
			_snake_case		=			args.target_model_path

			print(f'''Load fine-pruned model from {model_name_or_path}''' )
			_snake_case		=			torch.load(os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			_snake_case		=			{}

			for name, tensor in model.items():
						if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "classifier" in name or "qa_output" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "bias" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						else:
									if pruning_method == "magnitude":
												_snake_case		=			MagnitudeBinarizer.apply(inputs=__lowercase       ,					threshold=__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "topK":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			TopKBinarizer.apply(__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "sigmoied_threshold":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			ThresholdBinarizer.apply(__lowercase       ,					__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "l0":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case	,					_snake_case		=			-0.1, 1.1
												_snake_case		=			torch.sigmoid(__lowercase )
												_snake_case		=			s * (r - l) + l
												_snake_case		=			s_bar.clamp(min=0.0       ,					max=1.0 )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									else:
												raise ValueError('Unknown pruning method' )

			if target_model_path is None:
						_snake_case		=			os.path.join(
						    os.path.dirname(__lowercase )       ,					f'''bertarized_{os.path.basename(__lowercase )}''' )

			if not os.path.isdir(__lowercase ):
						shutil.copytree(__lowercase       ,					__lowercase )
						print(f'''\nCreated folder {target_model_path}''' )

			torch.save(__lowercase       ,					os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			print('\nPruned model saved! See you later!' )


if __name__ == "__main__":
			_lowerCamelCase       :				Dict       =			argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
			        ''' sigmoied_threshold = Soft movement pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `magnitude` and `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(
			    '''--model_name_or_path''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)
			parser.add_argument(
			    '''--target_model_path''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			_lowerCamelCase       :				int       =			parser.parse_args()

			main(args)