




'''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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				[
		    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MobileViTV2ForImageClassification",
		    "MobileViTV2ForSemanticSegmentation",
		    "MobileViTV2Model",
		    "MobileViTV2PreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileViTVaConfig,
		    MobileViTVaOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTVaForImageClassification,
				    MobileViTVaForSemanticSegmentation,
				    MobileViTVaModel,
				    MobileViTVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE							:     Any   	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




