





'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 				_lowerCamelCase(	UpperCamelCase__     :			Any		)		->	Any:
    A							:							Dict				=    args.serialization_dir
    A							:							List[Any]				=    args.pruning_method
    A							:							Union[str, Any]				=    args.threshold

    A							:							Optional[Any]				=    torch.load(os.path.join(UpperCamelCase__     ,							'''pytorch_model.bin'''		)     ,							map_location='''cpu'''		)

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

     snake_case_     			=						parser.parse_args()

     main(args)


'''simple docstring'''







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)