





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_    :				str					=			logging.get_logger(__name__)

a_    :				Union[str, Any]					=			{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							"""mobilevitv2"""





  def __init__(  self	,  A=3	,  A=2_56	,  A=2	,  A=2.0	,  A="swish"	,  A=3	,  A=32	,  A=0.1	,  A=0.02	,  A=1E-5	,  A=5_12	,  A=[6, 12, 18]	,  A=0.1	,  A=2_55	,  A=[2, 4, 3]	,  A=[1_28, 1_92, 2_56]	,  A=1.0	,  A=2	,  A=0.0	,  A=0.0	,  **A	,  )		->   Any:





       '''simple docstring'''







       super().__init__(**A						)

       __magic_name__				=       num_channels
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       expand_ratio
       __magic_name__				=       hidden_act
       __magic_name__				=       conv_kernel_size
       __magic_name__				=       output_stride
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       n_attn_blocks
       __magic_name__				=       base_attn_unit_dims
       __magic_name__				=       width_multiplier
       __magic_name__				=       ffn_multiplier
       __magic_name__				=       ffn_dropout
       __magic_name__				=       attn_dropout
       __magic_name__				=       classifier_dropout_prob

       # decode head attributes for semantic segmentation
       __magic_name__				=       aspp_out_channels
       __magic_name__				=       atrous_rates
       __magic_name__				=       aspp_dropout_prob
       __magic_name__				=       semantic_loss_ignore_index



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							version.parse("""1.11"""			)





  @property
  def 			__A      (  self						)		->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''







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





  @property
  def 			__A      (  self						)		->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''







       if self.task == "image-classification":
            return OrderedDict([('''logits''', {0: '''batch'''})]						)
       else:
            return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]						)







  @property
  def 			__A      (  self						)		->   float:





       '''simple docstring'''







       return 1E-4






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       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":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (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(snake_case_						)						)
          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__":
    a_    :				Optional[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',
    )

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

    main(args)