





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    args.serialization_dir
			UpperCAmelCase       		=    args.pruning_method
			UpperCAmelCase       		=    args.threshold

			UpperCAmelCase       		=    torch.load(os.path.join(UpperCamelCase__      ,      '''pytorch_model.bin'''			)      ,      map_location='''cpu'''			)

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

				__A    :   Any          =       parser.parse_args()

				main(args)










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


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

class      A_	(a_      ):
	UpperCAmelCase__ =      '''speech-encoder-decoder'''
	UpperCAmelCase__ =      True
	def __init__(   self							, **_A		):






				'''simple docstring'''







				super().__init__(**_A		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
							    F""" `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase       		=    kwargs.pop('''encoder'''		)
				UpperCAmelCase       		=    encoder_config.pop('''model_type'''		)
				UpperCAmelCase       		=    kwargs.pop('''decoder'''		)
				UpperCAmelCase       		=    decoder_config.pop('''model_type'''		)

				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    True
	@classmethod
	def 					_lowercase    (   cls							, _A							, _A							, **_A		):






				'''simple docstring'''







				logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
				UpperCAmelCase       		=    True
				UpperCAmelCase       		=    True

				return cls(encoder=encoder_config.to_dict()							, decoder=decoder_config.to_dict()							, **_A		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    copy.deepcopy(self.__dict__		)
				UpperCAmelCase       		=    self.encoder.to_dict()
				UpperCAmelCase       		=    self.decoder.to_dict()
				UpperCAmelCase       		=    self.__class__.model_type
				return output



