from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_pt_objects import *  # noqa F403
else:
				from .scheduling_consistency_models import CMStochasticIterativeScheduler
				from .scheduling_ddim import DDIMScheduler
				from .scheduling_ddim_inverse import DDIMInverseScheduler
				from .scheduling_ddim_parallel import DDIMParallelScheduler
				from .scheduling_ddpm import DDPMScheduler
				from .scheduling_ddpm_parallel import DDPMParallelScheduler
				from .scheduling_deis_multistep import DEISMultistepScheduler
				from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
				from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
				from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
				from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
				from .scheduling_euler_discrete import EulerDiscreteScheduler
				from .scheduling_heun_discrete import HeunDiscreteScheduler
				from .scheduling_ipndm import IPNDMScheduler
				from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
				from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
				from .scheduling_karras_ve import KarrasVeScheduler
				from .scheduling_pndm import PNDMScheduler
				from .scheduling_repaint import RePaintScheduler
				from .scheduling_sde_ve import ScoreSdeVeScheduler
				from .scheduling_sde_vp import ScoreSdeVpScheduler
				from .scheduling_unclip import UnCLIPScheduler
				from .scheduling_unipc_multistep import UniPCMultistepScheduler
				from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
				from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_objects import *  # noqa F403
else:
				from .scheduling_ddim_flax import FlaxDDIMScheduler
				from .scheduling_ddpm_flax import FlaxDDPMScheduler
				from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
				from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
				from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
				from .scheduling_pndm_flax import FlaxPNDMScheduler
				from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
				from .scheduling_utils_flax import (
				    FlaxKarrasDiffusionSchedulers,
				    FlaxSchedulerMixin,
				    FlaxSchedulerOutput,
				    broadcast_to_shape_from_left,
				)


try:
				if not (is_torch_available() and is_scipy_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
				from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
				if not (is_torch_available() and is_torchsde_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
				from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

						print(F"""Load fine-pruned model from {model_name_or_path}"""						)
						_snake_case						:					List[Any]    =   torch.load(os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						_snake_case						:					List[str]    =   {}

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

						if target_model_path is None:
												_snake_case						:					Any    =   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__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			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',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)