'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def SCREAMING_SNAKE_CASE__  (				snake_case       :				str  )   ->				Any:







     """simple docstring"""
     a					:							str							= args.serialization_dir
     a					:							List[str]							= args.pruning_method
     a					:							List[Any]							= args.threshold

     a					:							List[str]							= torch.load(os.path.join(snake_case				,	'pytorch_model.bin'  )				,	map_location='cpu'  )

     a					:							Dict							= 0  # Number of remaining (not pruned) params in the encoder
     a					:							str							= 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(snake_case				,	snake_case  ).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a					:							Union[str, Any]							= ThresholdBinarizer.apply(snake_case				,	snake_case				,	snake_case  ).sum().item()
               elif pruning_method == "l0":
                    a							,			a					:							List[Any]							= -0.1, 1.1
                    a					:							Optional[int]							= torch.sigmoid(snake_case  )
                    a					:							Optional[Any]							= 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(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__":
      UpperCamelCase       :     int			=    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""",
      )

      UpperCamelCase       :     Any			=    parser.parse_args()

      main(args)


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Optional[int]			=    {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"cpmant"





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						int = 3_0_7_2_0							,							UpperCAmelCase_     :						int = 4_0_9_6							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 1_2_8							,							UpperCAmelCase_     :						int = 1_0_2_4_0							,							UpperCAmelCase_     :						int = 4_8							,							UpperCAmelCase_     :						int = 0.0							,							UpperCAmelCase_     :						int = 5_1_2							,							UpperCAmelCase_     :						int = 2_0_4_8							,							UpperCAmelCase_     :						int = 1e-6							,							UpperCAmelCase_     :						float = 1.0							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						bool = True							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							Union[str, Any]							= prompt_types
         a					:							List[str]							= prompt_length
         a					:							List[Any]							= segment_types
         a					:							Any							= hidden_size
         a					:							Optional[int]							= num_attention_heads
         a					:							Union[str, Any]							= dim_head
         a					:							int							= dim_ff
         a					:							Union[str, Any]							= num_hidden_layers
         a					:							List[Any]							= position_bias_num_buckets
         a					:							Optional[int]							= position_bias_max_distance
         a					:							str							= dropout_p
         a					:							List[str]							= eps
         a					:							Dict							= use_cache
         a					:							Tuple							= vocab_size
         a					:							Optional[Any]							= init_std

