






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


a :				Optional[int]      					=		logging.get_logger(__name__)

a :				int      					=		{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					"electra"




						def __init__(   self       ,  snake_case__=30522       ,  snake_case__=128       ,  snake_case__=256       ,  snake_case__=12       ,  snake_case__=4       ,  snake_case__=1024       ,  snake_case__="gelu"       ,  snake_case__=0.1       ,  snake_case__=0.1       ,  snake_case__=512       ,  snake_case__=2       ,  snake_case__=0.02       ,  snake_case__=1e-12       ,  snake_case__="first"       ,  snake_case__=True       ,  snake_case__="gelu"       ,  snake_case__=0.1       ,  snake_case__=0       ,  snake_case__="absolute"       ,  snake_case__=True       ,  snake_case__=None       ,  **snake_case__       ,  ):
												'''simple docstring'''
												super().__init__(pad_token_id=snake_case__       ,  **snake_case__			)

												lowercase__				:      Tuple=       vocab_size
												lowercase__				:      Optional[Any]=       embedding_size
												lowercase__				:      Optional[int]=       hidden_size
												lowercase__				:      Union[str, Any]=       num_hidden_layers
												lowercase__				:      List[Any]=       num_attention_heads
												lowercase__				:      List[str]=       intermediate_size
												lowercase__				:      str=       hidden_act
												lowercase__				:      str=       hidden_dropout_prob
												lowercase__				:      Tuple=       attention_probs_dropout_prob
												lowercase__				:      Dict=       max_position_embeddings
												lowercase__				:      Optional[Any]=       type_vocab_size
												lowercase__				:      Union[str, Any]=       initializer_range
												lowercase__				:      Union[str, Any]=       layer_norm_eps

												lowercase__				:      Optional[int]=       summary_type
												lowercase__				:      Union[str, Any]=       summary_use_proj
												lowercase__				:      str=       summary_activation
												lowercase__				:      List[str]=       summary_last_dropout
												lowercase__				:      List[str]=       position_embedding_type
												lowercase__				:      Tuple=       use_cache
												lowercase__				:      Tuple=       classifier_dropout


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												if self.task == "multiple-choice":
																		lowercase__				:      int=       {0: "batch", 1: "choice", 2: "sequence"}
												else:
																		lowercase__				:      int=       {0: "batch", 1: "sequence"}
												return OrderedDict(
												    [
												        ("input_ids", dynamic_axis),
												        ("attention_mask", dynamic_axis),
												        ("token_type_ids", dynamic_axis),
												    ]			)







import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

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

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)