






import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 				snake_case__							(							UpperCAmelCase							:  Optional[int]       ):
    lowerCAmelCase__   :List[Any]      							=				args.pruning_method
    lowerCAmelCase__   :Any      							=				args.threshold

    lowerCAmelCase__   :List[Any]      							=				args.model_name_or_path.rstrip("/"       )
    lowerCAmelCase__   :int      							=				args.target_model_path

    print(F'''Load fine-pruned model from {model_name_or_path}'''       )
    lowerCAmelCase__   :str      							=				torch.load(os.path.join(UpperCAmelCase    ,			"pytorch_model.bin"       )       )
    lowerCAmelCase__   :str      							=				{}

    for name, tensor in model.items():
        if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
            lowerCAmelCase__   :Dict      							=				tensor
            print(F'''Copied layer {name}'''       )
        elif "classifier" in name or "qa_output" in name:
            lowerCAmelCase__   :Union[str, Any]      							=				tensor
            print(F'''Copied layer {name}'''       )
        elif "bias" in name:
            lowerCAmelCase__   :Tuple      							=				tensor
            print(F'''Copied layer {name}'''       )
        else:
            if pruning_method == "magnitude":
                lowerCAmelCase__   :List[str]      							=				MagnitudeBinarizer.apply(inputs=UpperCAmelCase    ,			threshold=UpperCAmelCase       )
                lowerCAmelCase__   :int      							=				tensor * mask
                print(F'''Pruned layer {name}'''       )
            elif pruning_method == "topK":
                if "mask_scores" in name:
                    continue
                lowerCAmelCase__   :List[str]      							=				name[:-6]
                lowerCAmelCase__   :Dict      							=				model[F'''{prefix_}mask_scores''']
                lowerCAmelCase__   :Optional[int]      							=				TopKBinarizer.apply(UpperCAmelCase    ,			UpperCAmelCase       )
                lowerCAmelCase__   :int      							=				tensor * mask
                print(F'''Pruned layer {name}'''       )
            elif pruning_method == "sigmoied_threshold":
                if "mask_scores" in name:
                    continue
                lowerCAmelCase__   :Optional[Any]      							=				name[:-6]
                lowerCAmelCase__   :List[Any]      							=				model[F'''{prefix_}mask_scores''']
                lowerCAmelCase__   :Any      							=				ThresholdBinarizer.apply(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )
                lowerCAmelCase__   :List[Any]      							=				tensor * mask
                print(F'''Pruned layer {name}'''       )
            elif pruning_method == "l0":
                if "mask_scores" in name:
                    continue
                lowerCAmelCase__   :Tuple      							=				name[:-6]
                lowerCAmelCase__   :Optional[int]      							=				model[F'''{prefix_}mask_scores''']
                lowerCAmelCase__   :Union[str, Any]      							=				-0.1, 1.1
                lowerCAmelCase__   :Union[str, Any]      							=				torch.sigmoid(UpperCAmelCase       )
                lowerCAmelCase__   :str      							=				s * (r - l) + l
                lowerCAmelCase__   :Optional[int]      							=				s_bar.clamp(min=0.0    ,			max=1.0       )
                lowerCAmelCase__   :Optional[Any]      							=				tensor * mask
                print(F'''Pruned layer {name}'''       )
            else:
                raise ValueError("Unknown pruning method"       )

    if target_model_path is None:
        lowerCAmelCase__   :Tuple      							=				os.path.join(
            os.path.dirname(UpperCAmelCase       )    ,			F'''bertarized_{os.path.basename(UpperCAmelCase       )}'''       )

    if not os.path.isdir(UpperCAmelCase       ):
        shutil.copytree(UpperCAmelCase    ,			UpperCAmelCase       )
        print(F'''\nCreated folder {target_model_path}'''       )

    torch.save(UpperCAmelCase    ,			os.path.join(UpperCAmelCase    ,			"pytorch_model.bin"       )       )
    print("\nPruned model saved! See you later!"       )


if __name__ == "__main__":
  _a    :   List[Any]        =			argparse.ArgumentParser()

  parser.add_argument(
      """--pruning_method""",
      choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
      type=str,
      required=True,
      help=(
          """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
          """ sigmoied_threshold = Soft movement pruning)"""
      ),
  )
  parser.add_argument(
      """--threshold""",
      type=float,
      required=False,
      help=(
          """For `magnitude` and `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(
      """--model_name_or_path""",
      type=str,
      required=True,
      help="""Folder containing the model that was previously fine-pruned""",
  )
  parser.add_argument(
      """--target_model_path""",
      default=None,
      type=str,
      required=False,
      help="""Folder containing the model that was previously fine-pruned""",
  )

  _a    :   List[Any]        =			parser.parse_args()

  main(args)











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a    :   Tuple        =			logging.get_logger(__name__)

_a    :   Optional[int]        =			{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''levit'''
      def __init__(   self    ,	_lowerCAmelCase=224    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=16    ,	_lowerCAmelCase=[128, 256, 384]    ,	_lowerCAmelCase=[4, 8, 12]    ,	_lowerCAmelCase=[4, 4, 4]    ,	_lowerCAmelCase=[16, 16, 16]    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=0.02    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				image_size
          lowerCAmelCase__   :Dict      							=				num_channels
          lowerCAmelCase__   :List[Any]      							=				kernel_size
          lowerCAmelCase__   :List[Any]      							=				stride
          lowerCAmelCase__   :Tuple      							=				padding
          lowerCAmelCase__   :str      							=				hidden_sizes
          lowerCAmelCase__   :Union[str, Any]      							=				num_attention_heads
          lowerCAmelCase__   :Optional[int]      							=				depths
          lowerCAmelCase__   :Union[str, Any]      							=				key_dim
          lowerCAmelCase__   :str      							=				drop_path_rate
          lowerCAmelCase__   :int      							=				patch_size
          lowerCAmelCase__   :Dict      							=				attention_ratio
          lowerCAmelCase__   :Dict      							=				mlp_ratio
          lowerCAmelCase__   :Optional[int]      							=				initializer_range
          lowerCAmelCase__   :Tuple      							=				[
              ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    version.parse('''1.11'''		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)



      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 1e-4



