




'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def   UpperCamelCase_(			snake_case							: Union[str, Any]  ):

							'''simple docstring'''






							snake_case_			      =   args.serialization_dir
							snake_case_			      =   args.pruning_method
							snake_case_			      =   args.threshold

							snake_case_			      =   torch.load(os.path.join(snake_case							,       "pytorch_model.bin"  )							,       map_location="cpu"  )

							snake_case_			      =   0  # Number of remaining (not pruned) params in the encoder
							snake_case_			      =   0  # Number of params in the encoder

							print("name".ljust(6_0							,       " "  )							,       "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_			      =   TopKBinarizer.apply(snake_case							,       snake_case  ).sum().item()
																					elif pruning_method == "sigmoied_threshold":
																												snake_case_			      =   ThresholdBinarizer.apply(snake_case							,       snake_case							,       snake_case  ).sum().item()
																					elif pruning_method == "l0":
																												snake_case_      ,   snake_case_			      =   -0.1, 1.1
																												snake_case_			      =   torch.sigmoid(snake_case  )
																												snake_case_			      =   s * (r - l) + l
																												snake_case_			      =   s_bar.clamp(min=0.0							,       max=1.0  )
																												snake_case_			      =   (mask > 0.0).sum().item()
																					else:
																												raise ValueError("Unknown pruning method"  )
																					remaining_count += mask_ones
																					print(name.ljust(6_0							,       " "  )							,       str(round(1_0_0 * mask_ones / param.numel()							,       3  )  ).ljust(2_0							,       " "  )							,       str(snake_case  )  )
														else:
																					encoder_count += param.numel()
																					if "bias" in name or "LayerNorm" in name:
																												remaining_count += param.numel()

							print(""  )
							print("Remaining Weights (global) %: "							,       1_0_0 * remaining_count / encoder_count  )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				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							:     Union[str, Any]   	=				parser.parse_args()

		main(args)










'''simple docstring'''





import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: bool = False  ):

							'''simple docstring'''






							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(snake_case  )
							np.random.seed(snake_case  )
							torch.manual_seed(snake_case  )
							if is_xpu_available():
														torch.xpu.manual_seed_all(snake_case  )
							elif is_npu_available():
														torch.npu.manual_seed_all(snake_case  )
							else:
														torch.cuda.manual_seed_all(snake_case  )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(snake_case  )


def   UpperCamelCase_(			snake_case							: Optional[RNGType] = None							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							if rng_type == RNGType.TORCH:
														snake_case_			      =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														snake_case_			      =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														snake_case_			      =   torch.tensor(xm.get_rng_state()  )
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														snake_case_			      =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														snake_case_			      =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														snake_case_			      =   generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							snake_case_			      =   AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														snake_case_			      =   rng_state.to(xm.xla_device()  )
														xm.collective_broadcast([rng_state]  )
														xm.mark_step()
														snake_case_			      =   rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														snake_case_			      =   rng_state.to(state.device  )
														torch.distributed.broadcast(snake_case							,       0  )
														snake_case_			      =   rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(snake_case							,       0  )

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(snake_case  )
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(snake_case  )
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()  )
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(snake_case  )




def   UpperCamelCase_(			snake_case							: List[Union[str, RNGType]]							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							for rng_type in rng_types:
														synchronize_rng_state(RNGType(snake_case  )							,       generator=snake_case  )




