
"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class      lowerCAmelCase_						(							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						(ScoreSdeVeScheduler,)
							_snake_case		     =						()




							@property
							def  A__		(				self							)   ->       Optional[Any]:
													__lowerCAmelCase        =       4
													__lowerCAmelCase        =       3
													__lowerCAmelCase        =       8
													__lowerCAmelCase        =       8

													__lowerCAmelCase        =       torch.rand((batch_size, num_channels, height, width)							)

													return sample




							@property
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       4
													__lowerCAmelCase        =       3
													__lowerCAmelCase        =       8
													__lowerCAmelCase        =       8

													__lowerCAmelCase        =       batch_size * num_channels * height * width
													__lowerCAmelCase        =       torch.arange(__lowerCamelCase							)
													__lowerCAmelCase        =       sample.reshape(__lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase							)
													__lowerCAmelCase        =       sample / num_elems
													__lowerCAmelCase        =       sample.permute(3  ,							0  ,							1  ,							2							)

													return sample




							def  A__		(				self							)   ->       Any:


													def model(snake_case_  ,							snake_case_  ,							*snake_case_							):
																			return sample * t / (t + 1)

													return model




							def  A__		(				self  ,							**snake_case_							)   ->       int:
													__lowerCAmelCase        =       {
													    "num_train_timesteps": 2_000,
													    "snr": 0.15,
													    "sigma_min": 0.01,
													    "sigma_max": 1_348,
													    "sampling_eps": 1e-5,
													}

													config.update(**__lowerCamelCase							)
													return config




							def  A__		(				self  ,							snake_case_=0  ,							**snake_case_							)   ->       List[str]:
													__lowerCAmelCase        =       dict(self.forward_default_kwargs							)

													for scheduler_class in self.scheduler_classes:
																			__lowerCAmelCase        =       self.dummy_sample
																			__lowerCAmelCase        =       0.1 * sample

																			__lowerCAmelCase        =       self.get_scheduler_config(**__lowerCamelCase							)
																			__lowerCAmelCase        =       scheduler_class(**__lowerCamelCase							)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__lowerCamelCase							)
																									__lowerCAmelCase        =       scheduler_class.from_pretrained(__lowerCamelCase							)

																			__lowerCAmelCase        =       scheduler.step_pred(
																			    __lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample
																			__lowerCAmelCase        =       new_scheduler.step_pred(
																			    __lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample

																			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

																			__lowerCAmelCase        =       scheduler.step_correct(__lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample
																			__lowerCAmelCase        =       new_scheduler.step_correct(
																			    __lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample

																			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler correction are not identical"




							def  A__		(				self  ,							snake_case_=0  ,							**snake_case_							)   ->       str:
													__lowerCAmelCase        =       dict(self.forward_default_kwargs							)
													kwargs.update(__lowerCamelCase							)

													for scheduler_class in self.scheduler_classes:
																			__lowerCAmelCase        =       self.dummy_sample
																			__lowerCAmelCase        =       0.1 * sample

																			__lowerCAmelCase        =       self.get_scheduler_config()
																			__lowerCAmelCase        =       scheduler_class(**__lowerCamelCase							)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__lowerCamelCase							)
																									__lowerCAmelCase        =       scheduler_class.from_pretrained(__lowerCamelCase							)

																			__lowerCAmelCase        =       scheduler.step_pred(
																			    __lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample
																			__lowerCAmelCase        =       new_scheduler.step_pred(
																			    __lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample

																			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

																			__lowerCAmelCase        =       scheduler.step_correct(__lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample
																			__lowerCAmelCase        =       new_scheduler.step_correct(
																			    __lowerCamelCase  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample

																			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler correction are not identical"




							def  A__		(				self							)   ->       Union[str, Any]:


													for timesteps in [10, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=__lowerCamelCase							)




							def  A__		(				self							)   ->       List[Any]:


													for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,							[1, 100, 1_000]							):
																			self.check_over_configs(sigma_min=__lowerCamelCase  ,							sigma_max=__lowerCamelCase							)




							def  A__		(				self							)   ->       List[str]:


													for t in [0.1, 0.5, 0.75]:
																			self.check_over_forward(time_step=__lowerCamelCase							)




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase        =       dict(self.forward_default_kwargs							)

													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config()
													__lowerCAmelCase        =       scheduler_class(**__lowerCamelCase							)

													__lowerCAmelCase        =       3

													__lowerCAmelCase        =       self.dummy_model()
													__lowerCAmelCase        =       self.dummy_sample_deter

													scheduler.set_sigmas(__lowerCamelCase							)
													scheduler.set_timesteps(__lowerCamelCase							)
													__lowerCAmelCase        =       torch.manual_seed(0							)

													for i, t in enumerate(scheduler.timesteps							):
																			__lowerCAmelCase        =       scheduler.sigmas[i]

																			for _ in range(scheduler.config.correct_steps							):
																									with torch.no_grad():
																															__lowerCAmelCase        =       model(__lowerCamelCase  ,							__lowerCamelCase							)
																									__lowerCAmelCase        =       scheduler.step_correct(__lowerCamelCase  ,							__lowerCamelCase  ,							generator=__lowerCamelCase  ,							**__lowerCamelCase							).prev_sample

																			with torch.no_grad():
																									__lowerCAmelCase        =       model(__lowerCamelCase  ,							__lowerCamelCase							)

																			__lowerCAmelCase        =       scheduler.step_pred(__lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase  ,							generator=__lowerCamelCase  ,							**__lowerCamelCase							)
																			__lowerCAmelCase        =       output.prev_sample, output.prev_sample_mean

													__lowerCAmelCase        =       torch.sum(torch.abs(__lowerCamelCase							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(__lowerCamelCase							)							)

													assert np.isclose(result_sum.item()  ,							14_372_758_528.0							)
													assert np.isclose(result_mean.item()  ,							18_714_530.0							)




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       dict(self.forward_default_kwargs							)

													__lowerCAmelCase        =       kwargs.pop("""num_inference_steps"""  ,							__lowerCamelCase							)

													for scheduler_class in self.scheduler_classes:
																			__lowerCAmelCase        =       self.get_scheduler_config()
																			__lowerCAmelCase        =       scheduler_class(**__lowerCamelCase							)

																			__lowerCAmelCase        =       self.dummy_sample
																			__lowerCAmelCase        =       0.1 * sample

																			if num_inference_steps is not None and hasattr(__lowerCamelCase  ,							"""set_timesteps"""							):
																									scheduler.set_timesteps(__lowerCamelCase							)
																			elif num_inference_steps is not None and not hasattr(__lowerCamelCase  ,							"""set_timesteps"""							):
																									__lowerCAmelCase        =       num_inference_steps

																			__lowerCAmelCase        =       scheduler.step_pred(__lowerCamelCase  ,							0  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample
																			__lowerCAmelCase        =       scheduler.step_pred(__lowerCamelCase  ,							1  ,							__lowerCamelCase  ,							generator=torch.manual_seed(0							)  ,							**__lowerCamelCase							).prev_sample

																			self.assertEqual(output_a.shape  ,							sample.shape							)
																			self.assertEqual(output_a.shape  ,							output_a.shape							)


"""simple docstring"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       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":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (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(_lowerCAmelCase		)		)
												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__":
			SCREAMING_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''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
