




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    :				Optional[Any]    						=	logging.get_logger(__name__)

__A    :				Optional[Any]    						=	{
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}




class       UpperCAmelCase_   ( SCREAMING_SNAKE_CASE__							):




							'''simple docstring'''



							a__    	=  '''mobilevit'''





							def __init__(       self							:						List[str]							,  a							:						List[str]=3							,  a							:						str=256							,  a							:						Tuple=2							,  a							:						Optional[int]=[144, 192, 240]							,  a							:						Any=[16, 32, 64, 96, 128, 160, 640]							,  a							:						List[Any]=4							,  a							:						Any=2.0							,  a							:						Dict=4.0							,  a							:						Optional[int]="silu"							,  a							:						Optional[int]=3							,  a							:						List[str]=32							,  a							:						List[Any]=0.1							,  a							:						Any=0.0							,  a							:						str=0.1							,  a							:						Dict=0.02							,  a							:						Union[str, Any]=1E-5							,  a							:						int=True							,  a							:						Optional[Any]=256							,  a							:						Optional[int]=[6, 12, 18]							,  a							:						str=0.1							,  a							:						str=255							,  **a							:						Any							,  )      ->       Union[str, Any]:
										super().__init__(**a      )

										SCREAMING_SNAKE_CASE     =       num_channels
										SCREAMING_SNAKE_CASE     =       image_size
										SCREAMING_SNAKE_CASE     =       patch_size
										SCREAMING_SNAKE_CASE     =       hidden_sizes
										SCREAMING_SNAKE_CASE     =       neck_hidden_sizes
										SCREAMING_SNAKE_CASE     =       num_attention_heads
										SCREAMING_SNAKE_CASE     =       mlp_ratio
										SCREAMING_SNAKE_CASE     =       expand_ratio
										SCREAMING_SNAKE_CASE     =       hidden_act
										SCREAMING_SNAKE_CASE     =       conv_kernel_size
										SCREAMING_SNAKE_CASE     =       output_stride
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       classifier_dropout_prob
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       layer_norm_eps
										SCREAMING_SNAKE_CASE     =       qkv_bias

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





class       UpperCAmelCase_   ( SCREAMING_SNAKE_CASE__							):




							'''simple docstring'''



							a__    	=  version.parse('''1.11'''							)





							@property
							def  _UpperCAmelCase  (       self							:						int      )      ->       Mapping[str, Mapping[int, str]]:
										return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]      )





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







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







							@property
							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       float:
										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)