"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


__lowercase      					=							logging.get_logger(__name__)

__lowercase      					=							{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}






class 						_lowercase      (		__snake_case	):



						"""simple docstring"""




						lowercase__  					=   '''mega'''





						def __init__(							self				:  Union[str, Any]			,      UpperCamelCase__				:  Dict=30522			,      UpperCamelCase__				:  Optional[Any]=128			,      UpperCamelCase__				:  Union[str, Any]=4			,      UpperCamelCase__				:  List[Any]=256			,      UpperCamelCase__				:  Optional[Any]=16			,      UpperCamelCase__				:  str=True			,      UpperCamelCase__				:  Union[str, Any]=64			,      UpperCamelCase__				:  Union[str, Any]=False			,      UpperCamelCase__				:  Optional[int]=-1			,      UpperCamelCase__				:  Optional[Any]=None			,      UpperCamelCase__				:  Optional[int]=True			,      UpperCamelCase__				:  List[Any]="scalenorm"			,      UpperCamelCase__				:  Tuple=True			,      UpperCamelCase__				:  Any="silu"			,      UpperCamelCase__				:  str="softmax"			,      UpperCamelCase__				:  Tuple=0.1			,      UpperCamelCase__				:  Tuple=0.1			,      UpperCamelCase__				:  Dict=0.1			,      UpperCamelCase__				:  Any=False			,      UpperCamelCase__				:  Any=True			,      UpperCamelCase__				:  str=256			,      UpperCamelCase__				:  str=True			,      UpperCamelCase__				:  Optional[Any]=0.1			,      UpperCamelCase__				:  Optional[int]=2048			,      UpperCamelCase__				:  List[Any]=False			,      UpperCamelCase__				:  Union[str, Any]=2			,      UpperCamelCase__				:  Union[str, Any]=0.02			,      UpperCamelCase__				:  Optional[Any]=0.2			,      UpperCamelCase__				:  List[str]=0.02			,      UpperCamelCase__				:  Tuple=1.0			,      UpperCamelCase__				:  Optional[int]=1			,      UpperCamelCase__				:  Dict=0			,      UpperCamelCase__				:  List[Any]=2			,      UpperCamelCase__				:  Optional[int]="rotary"			,      UpperCamelCase__				:  Dict=None			,      UpperCamelCase__				:  Optional[Any]=True			,      UpperCamelCase__				:  Optional[int]=True			,      **UpperCamelCase__				:  str			,      )							->							int:
													'''simple docstring'''



													super().__init__(pad_token_id=lowercase_			,      bos_token_id=lowercase_			,      eos_token_id=lowercase_			,      **lowercase_							)

													__UpperCamelCase		     =vocab_size
													__UpperCamelCase		     =hidden_size
													__UpperCamelCase		     =num_hidden_layers
													__UpperCamelCase		     =activation
													__UpperCamelCase		     =attention_activation
													__UpperCamelCase		     =intermediate_size
													__UpperCamelCase		     =ema_projection_size
													__UpperCamelCase		     =bidirectional
													__UpperCamelCase		     =shared_representation_size
													__UpperCamelCase		     =use_chunking
													__UpperCamelCase		     =chunk_size
													__UpperCamelCase		     =truncation
													__UpperCamelCase		     =normalize_before_mega
													__UpperCamelCase		     =normalization_type
													__UpperCamelCase		     =norm_affine
													__UpperCamelCase		     =dropout_prob
													__UpperCamelCase		     =hidden_dropout_prob
													__UpperCamelCase		     =attention_probs_dropout_prob
													__UpperCamelCase		     =use_feature_dropout
													__UpperCamelCase		     =use_normalized_ffn
													__UpperCamelCase		     =nffn_hidden_size
													__UpperCamelCase		     =normalize_before_ffn
													__UpperCamelCase		     =nffn_activation_dropout_prob
													__UpperCamelCase		     =max_positions
													__UpperCamelCase		     =add_token_type_embeddings
													__UpperCamelCase		     =type_vocab_size
													__UpperCamelCase		     =initializer_range
													__UpperCamelCase		     =ema_delta_alpha_range
													__UpperCamelCase		     =ema_beta_range
													__UpperCamelCase		     =ema_gamma_omega_range
													__UpperCamelCase		     =relative_positional_bias
													__UpperCamelCase		     =use_cache
													__UpperCamelCase		     =classifier_dropout
													__UpperCamelCase		     =add_lm_hidden_dense_layer
													__UpperCamelCase		     =1  # not used but required by Hugging Face






class 						_lowercase      (		__snake_case	):



						"""simple docstring"""




						@property
						def    UpperCAmelCase_      (							self				:  Any							)							->							Tuple:
													'''simple docstring'''



													if self.task == "multiple-choice":
																				__UpperCamelCase		     ={0: "batch", 1: "choice", 2: "sequence"}
													else:
																				__UpperCamelCase		     ={0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ('''input_ids''', dynamic_axis),
													        ('''attention_mask''', 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)