

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			a_	:	Dict          =   args.serialization_dir
			a_	:	Dict          =   args.pruning_method
			a_	:	List[Any]          =   args.threshold

			a_	:	List[str]          =   torch.load(os.path.join(a__     ,  """pytorch_model.bin""")     ,  map_location="""cpu""")

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

	main(args)





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case							:			Any      		= {
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			Dict      		= [
	    """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ConvNextV2ForImageClassification""",
	    """ConvNextV2Model""",
	    """ConvNextV2PreTrainedModel""",
	    """ConvNextV2Backbone""",
	]


if TYPE_CHECKING:
	from .configuration_convnextva import (
	    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ConvNextVaConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnextva import (
		    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextVaBackbone,
		    ConvNextVaForImageClassification,
		    ConvNextVaModel,
		    ConvNextVaPreTrainedModel,
		)

else:
	import sys

	__snake_case							:			Dict      		= _LazyModule(__name__, globals()["""__file__"""], _import_structure)


