
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		str:
				__lowercase								=   args.serialization_dir
				__lowercase								=   args.pruning_method
				__lowercase								=   args.threshold

				__lowercase								=   torch.load(os.path.join(SCREAMING_SNAKE_CASE       ,				'pytorch_model.bin'     )       ,				map_location='cpu'     )

				__lowercase								=   0  # Number of remaining (not pruned) params in the encoder
				__lowercase								=   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":
																__lowercase								=   TopKBinarizer.apply(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ).sum().item()
												elif pruning_method == "sigmoied_threshold":
																__lowercase								=   ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ).sum().item()
												elif pruning_method == "l0":
																__lowercase						,   __lowercase								=   -0.1, 1.1
																__lowercase								=   torch.sigmoid(SCREAMING_SNAKE_CASE     )
																__lowercase								=   s * (r - l) + l
																__lowercase								=   s_bar.clamp(min=0.0       ,				max=1.0     )
																__lowercase								=   (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(SCREAMING_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__":
							SCREAMING_SNAKE_CASE__				=							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__				=							parser.parse_args()

							main(args)








'''simple docstring'''

from typing import List, Union

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


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''led'''
				snake_case_							     = {
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				    '''initializer_range''': '''init_std''',
				}

				def __init__(       self,						lowercase_=50265,						lowercase_=16384,						lowercase_=1024,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=True,						lowercase_=True,						lowercase_="gelu",						lowercase_=1024,						lowercase_=0.1,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=2,						lowercase_=0.0,						lowercase_=1,						lowercase_=0,						lowercase_=2,						lowercase_ = 512,						**lowercase_,						)    ->							Union[str, Any]:
					snake_case							=					vocab_size
					snake_case							=					max_encoder_position_embeddings
					snake_case							=					max_decoder_position_embeddings
					snake_case							=					d_model
					snake_case							=					encoder_ffn_dim
					snake_case							=					encoder_layers
					snake_case							=					encoder_attention_heads
					snake_case							=					decoder_ffn_dim
					snake_case							=					decoder_layers
					snake_case							=					decoder_attention_heads
					snake_case							=					dropout
					snake_case							=					attention_dropout
					snake_case							=					activation_dropout
					snake_case							=					activation_function
					snake_case							=					init_std
					snake_case							=					encoder_layerdrop
					snake_case							=					decoder_layerdrop
					snake_case							=					classifier_dropout
					snake_case							=					use_cache
					snake_case							=					encoder_layers
					snake_case							=					attention_window

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





