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 					snake_case						(__lowercase					,			__lowercase = False						)       ->       Any:

						'''simple docstring'''



						if device_specific:
												seed += AcceleratorState().process_index
						random.seed(__lowercase						)
						np.random.seed(__lowercase						)
						torch.manual_seed(__lowercase						)
						if is_xpu_available():
												torch.xpu.manual_seed_all(__lowercase						)
						elif is_npu_available():
												torch.npu.manual_seed_all(__lowercase						)
						else:
												torch.cuda.manual_seed_all(__lowercase						)
						# ^^ safe to call this function even if cuda is not available
						if is_tpu_available():
												xm.set_rng_state(__lowercase						)
def 					snake_case						(__lowercase = None					,			__lowercase = None						)       ->       Any:

						'''simple docstring'''



						if rng_type == RNGType.TORCH:
												_snake_case						:     int				=    torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												_snake_case						:     Dict				=    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						:     Optional[int]				=    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						:     Optional[int]				=    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						:     List[Any]				=    torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												_snake_case						:     List[Any]				=    generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						_snake_case						:     Optional[int]				=    AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												_snake_case						:     List[str]				=    rng_state.to(xm.xla_device()						)
												xm.collective_broadcast([rng_state]						)
												xm.mark_step()
												_snake_case						:     Tuple				=    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						:     Optional[Any]				=    rng_state.to(state.device						)
												torch.distributed.broadcast(__lowercase					,			0						)
												_snake_case						:     List[Any]				=    rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(__lowercase					,			0						)

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

def 					snake_case						(__lowercase					,			__lowercase = None						)       ->       str:

						'''simple docstring'''



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