



'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 			snake_case_					( _lowerCAmelCase  :						Tuple  )				->       Optional[int]:
							UpperCAmelCase			:	Optional[Any]										=		args.serialization_dir
							UpperCAmelCase			:	List[str]										=		args.pruning_method
							UpperCAmelCase			:	int										=		args.threshold

							UpperCAmelCase			:	Optional[int]										=		torch.load(os.path.join(_lowerCAmelCase       ,				'''pytorch_model.bin'''  )       ,				map_location='''cpu'''  )

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

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

						main(args)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__:       Tuple							     =      {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:       Optional[Any]							     =      ["SplinterTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:       Any							     =      [
						    "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SplinterForQuestionAnswering",
						    "SplinterForPreTraining",
						    "SplinterLayer",
						    "SplinterModel",
						    "SplinterPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
						from .tokenization_splinter import SplinterTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_splinter_fast import SplinterTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_splinter import (
												    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SplinterForPreTraining,
												    SplinterForQuestionAnswering,
												    SplinterLayer,
												    SplinterModel,
												    SplinterPreTrainedModel,
												)


else:
						import sys

						UpperCamelCase__:       Dict							     =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





