from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = logging.get_logger(__name__)

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



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'mega'



							def __init__(						self      ,				lowercase_=30_522      ,				lowercase_=128      ,				lowercase_=4      ,				lowercase_=256      ,				lowercase_=16      ,				lowercase_=True      ,				lowercase_=64      ,				lowercase_=False      ,				lowercase_=-1      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_="scalenorm"      ,				lowercase_=True      ,				lowercase_="silu"      ,				lowercase_="softmax"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=False      ,				lowercase_=True      ,				lowercase_=256      ,				lowercase_=True      ,				lowercase_=0.1      ,				lowercase_=2_048      ,				lowercase_=False      ,				lowercase_=2      ,				lowercase_=0.02      ,				lowercase_=0.2      ,				lowercase_=0.02      ,				lowercase_=1.0      ,				lowercase_=1      ,				lowercase_=0      ,				lowercase_=2      ,				lowercase_="rotary"      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_=True      ,				**lowercase_      ,				):
													super().__init__(pad_token_id=lowercase_      ,				bos_token_id=lowercase_      ,				eos_token_id=lowercase_      ,				**lowercase_				)

													_snake_case						:     int				=    vocab_size
													_snake_case						:     Tuple				=    hidden_size
													_snake_case						:     Optional[int]				=    num_hidden_layers
													_snake_case						:     List[Any]				=    activation
													_snake_case						:     Tuple				=    attention_activation
													_snake_case						:     List[str]				=    intermediate_size
													_snake_case						:     Union[str, Any]				=    ema_projection_size
													_snake_case						:     List[Any]				=    bidirectional
													_snake_case						:     Optional[int]				=    shared_representation_size
													_snake_case						:     Tuple				=    use_chunking
													_snake_case						:     int				=    chunk_size
													_snake_case						:     Tuple				=    truncation
													_snake_case						:     Dict				=    normalize_before_mega
													_snake_case						:     str				=    normalization_type
													_snake_case						:     Any				=    norm_affine
													_snake_case						:     str				=    dropout_prob
													_snake_case						:     Tuple				=    hidden_dropout_prob
													_snake_case						:     int				=    attention_probs_dropout_prob
													_snake_case						:     Optional[Any]				=    use_feature_dropout
													_snake_case						:     Optional[int]				=    use_normalized_ffn
													_snake_case						:     Optional[Any]				=    nffn_hidden_size
													_snake_case						:     str				=    normalize_before_ffn
													_snake_case						:     Union[str, Any]				=    nffn_activation_dropout_prob
													_snake_case						:     Dict				=    max_positions
													_snake_case						:     int				=    add_token_type_embeddings
													_snake_case						:     str				=    type_vocab_size
													_snake_case						:     int				=    initializer_range
													_snake_case						:     str				=    ema_delta_alpha_range
													_snake_case						:     Optional[int]				=    ema_beta_range
													_snake_case						:     List[str]				=    ema_gamma_omega_range
													_snake_case						:     Optional[int]				=    relative_positional_bias
													_snake_case						:     int				=    use_cache
													_snake_case						:     Tuple				=    classifier_dropout
													_snake_case						:     int				=    add_lm_hidden_dense_layer
													_snake_case						:     Any				=    1  # not used but required by Hugging Face




class    lowercase_    (     __snake_case		):



							@property
							def        UpperCamelCase       (						self				):
													if self.task == "multiple-choice":
																			_snake_case						:     Optional[int]				=    {0: "batch", 1: "choice", 2: "sequence"}
													else:
																			_snake_case						:     int				=    {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)