import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		(LMSDiscreteScheduler,)
							_lowerCamelCase						=		10



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													_snake_case						:     Optional[int]				=    {
													    "num_train_timesteps": 1_100,
													    "beta_start": 0.0_001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													}

													config.update(**lowercase_				)
													return config



							def        UpperCamelCase       (						self				):
													for timesteps in [10, 50, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=lowercase_				)



							def        UpperCamelCase       (						self				):
													for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,				[0.0_002, 0.002, 0.02]				):
																			self.check_over_configs(beta_start=lowercase_      ,				beta_end=lowercase_				)



							def        UpperCamelCase       (						self				):
													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=lowercase_				)



							def        UpperCamelCase       (						self				):
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=lowercase_				)



							def        UpperCamelCase       (						self				):
													for t in [0, 500, 800]:
																			self.check_over_forward(time_step=lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.scheduler_classes[0]
													_snake_case						:     str				=    self.get_scheduler_config()
													_snake_case						:     str				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													_snake_case						:     Union[str, Any]				=    self.dummy_model()
													_snake_case						:     List[Any]				=    self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     List[Any]				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     str				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Tuple				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     Tuple				=    output.prev_sample

													_snake_case						:     str				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Tuple				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 1_006.388				) < 1e-2
													assert abs(result_mean.item() - 1.31				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    self.scheduler_classes[0]
													_snake_case						:     str				=    self.get_scheduler_config(prediction_type="v_prediction"				)
													_snake_case						:     Tuple				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													_snake_case						:     Optional[int]				=    self.dummy_model()
													_snake_case						:     List[str]				=    self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     Tuple				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     Tuple				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     int				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     Any				=    output.prev_sample

													_snake_case						:     List[Any]				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Tuple				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 0.0_017				) < 1e-2
													assert abs(result_mean.item() - 2.26_76e-06				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.scheduler_classes[0]
													_snake_case						:     Any				=    self.get_scheduler_config()
													_snake_case						:     int				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps      ,				device=lowercase_				)

													_snake_case						:     int				=    self.dummy_model()
													_snake_case						:     str				=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													_snake_case						:     int				=    sample.to(lowercase_				)

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     str				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     List[Any]				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Optional[Any]				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     List[Any]				=    output.prev_sample

													_snake_case						:     Optional[Any]				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Union[str, Any]				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 1_006.388				) < 1e-2
													assert abs(result_mean.item() - 1.31				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.scheduler_classes[0]
													_snake_case						:     Tuple				=    self.get_scheduler_config()
													_snake_case						:     List[str]				=    scheduler_class(**lowercase_      ,				use_karras_sigmas=lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps      ,				device=lowercase_				)

													_snake_case						:     str				=    self.dummy_model()
													_snake_case						:     Dict				=    self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma
													_snake_case						:     Any				=    sample.to(lowercase_				)

													for t in scheduler.timesteps:
																			_snake_case						:     int				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     Any				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Dict				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     Any				=    output.prev_sample

													_snake_case						:     Any				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Any				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 3_812.9_927				) < 2e-2
													assert abs(result_mean.item() - 4.9_648				) < 1e-3
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

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