


"""simple docstring"""






import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer





def 				A_       ( snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	args.pruning_method
			_UpperCamelCase					:Any					     =	args.threshold

			_UpperCamelCase					:Optional[int]					     =	args.model_name_or_path.rstrip('''/'''		)
			_UpperCamelCase					:Tuple					     =	args.target_model_path

			print(f"Load fine-pruned model from {model_name_or_path}"		)
			_UpperCamelCase					:Optional[int]					     =	torch.load(os.path.join(snake_case__       ,					'''pytorch_model.bin'''		)		)
			_UpperCamelCase					:Union[str, Any]					     =	{}

			for name, tensor in model.items():
						if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
									_UpperCamelCase					:Any					     =	tensor
									print(f"Copied layer {name}"		)
						elif "classifier" in name or "qa_output" in name:
									_UpperCamelCase					:Optional[int]					     =	tensor
									print(f"Copied layer {name}"		)
						elif "bias" in name:
									_UpperCamelCase					:Tuple					     =	tensor
									print(f"Copied layer {name}"		)
						else:
									if pruning_method == "magnitude":
												_UpperCamelCase					:Optional[Any]					     =	MagnitudeBinarizer.apply(inputs=snake_case__       ,					threshold=snake_case__		)
												_UpperCamelCase					:Tuple					     =	tensor * mask
												print(f"Pruned layer {name}"		)
									elif pruning_method == "topK":
												if "mask_scores" in name:
															continue
												_UpperCamelCase					:int					     =	name[:-6]
												_UpperCamelCase					:Tuple					     =	model[f"{prefix_}mask_scores"]
												_UpperCamelCase					:Dict					     =	TopKBinarizer.apply(snake_case__       ,					snake_case__		)
												_UpperCamelCase					:Any					     =	tensor * mask
												print(f"Pruned layer {name}"		)
									elif pruning_method == "sigmoied_threshold":
												if "mask_scores" in name:
															continue
												_UpperCamelCase					:Optional[Any]					     =	name[:-6]
												_UpperCamelCase					:Tuple					     =	model[f"{prefix_}mask_scores"]
												_UpperCamelCase					:Any					     =	ThresholdBinarizer.apply(snake_case__       ,					snake_case__       ,					snake_case__		)
												_UpperCamelCase					:Dict					     =	tensor * mask
												print(f"Pruned layer {name}"		)
									elif pruning_method == "l0":
												if "mask_scores" in name:
															continue
												_UpperCamelCase					:Any					     =	name[:-6]
												_UpperCamelCase					:List[str]					     =	model[f"{prefix_}mask_scores"]
												_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	-0.1, 1.1
												_UpperCamelCase					:Dict					     =	torch.sigmoid(snake_case__		)
												_UpperCamelCase					:Dict					     =	s * (r - l) + l
												_UpperCamelCase					:Dict					     =	s_bar.clamp(min=0.0       ,					max=1.0		)
												_UpperCamelCase					:Any					     =	tensor * mask
												print(f"Pruned layer {name}"		)
									else:
												raise ValueError('''Unknown pruning method'''		)

			if target_model_path is None:
						_UpperCamelCase					:Tuple					     =	os.path.join(
						    os.path.dirname(snake_case__		)       ,					f"bertarized_{os.path.basename(snake_case__		)}"		)

			if not os.path.isdir(snake_case__		):
						shutil.copytree(snake_case__       ,					snake_case__		)
						print(f"\nCreated folder {target_model_path}"		)

			torch.save(snake_case__       ,					os.path.join(snake_case__       ,					'''pytorch_model.bin'''		)		)
			print('''\nPruned model saved! See you later!'''		)


if __name__ == "__main__":
					UpperCamelCase__				:Optional[Any]			   =	argparse.ArgumentParser()

					parser.add_argument(
					    """--pruning_method""",
					    choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
					    type=str,
					    required=True,
					    help=(
					        """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
					        """ sigmoied_threshold = Soft movement pruning)"""
					    ),
					)
					parser.add_argument(
					    """--threshold""",
					    type=float,
					    required=False,
					    help=(
					        """For `magnitude` and `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(
					    """--model_name_or_path""",
					    type=str,
					    required=True,
					    help="""Folder containing the model that was previously fine-pruned""",
					)
					parser.add_argument(
					    """--target_model_path""",
					    default=None,
					    type=str,
					    required=False,
					    help="""Folder containing the model that was previously fine-pruned""",
					)

					UpperCamelCase__				:Any			   =	parser.parse_args()

					main(args)





"""simple docstring"""






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


UpperCamelCase__				:str			   =	logging.get_logger(__name__)

UpperCamelCase__				:Optional[int]			   =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"markuplm"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3_05_22					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=30_72					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=1E-12					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2_56					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=2_16					,				SCREAMING_SNAKE_CASE__=10_01					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=50					,				SCREAMING_SNAKE_CASE__="absolute"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__(
					    pad_token_id=SCREAMING_SNAKE_CASE__					,				bos_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:Any					     =	vocab_size
					_UpperCamelCase					:Union[str, Any]					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	num_hidden_layers
					_UpperCamelCase					:int					     =	num_attention_heads
					_UpperCamelCase					:Tuple					     =	hidden_act
					_UpperCamelCase					:str					     =	intermediate_size
					_UpperCamelCase					:Optional[int]					     =	hidden_dropout_prob
					_UpperCamelCase					:Any					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Union[str, Any]					     =	max_position_embeddings
					_UpperCamelCase					:Dict					     =	type_vocab_size
					_UpperCamelCase					:int					     =	initializer_range
					_UpperCamelCase					:Optional[int]					     =	layer_norm_eps
					_UpperCamelCase					:Any					     =	position_embedding_type
					_UpperCamelCase					:Any					     =	use_cache
					_UpperCamelCase					:List[str]					     =	classifier_dropout
					# additional properties
					_UpperCamelCase					:Union[str, Any]					     =	max_depth
					_UpperCamelCase					:Union[str, Any]					     =	max_xpath_tag_unit_embeddings
					_UpperCamelCase					:Optional[Any]					     =	max_xpath_subs_unit_embeddings
					_UpperCamelCase					:int					     =	tag_pad_id
					_UpperCamelCase					:str					     =	subs_pad_id
					_UpperCamelCase					:List[str]					     =	xpath_unit_hidden_size

