
import argparse
import os
import shutil

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







def     A__   (  lowerCamelCase  )					->		Any:
				UpperCamelCase_:							Dict		=  args.pruning_method
				UpperCamelCase_:							List[str]		=  args.threshold

				UpperCamelCase_:							Union[str, Any]		=  args.model_name_or_path.rstrip("""/"""  )
				UpperCamelCase_:							List[str]		=  args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''  )
				UpperCamelCase_:							str		=  torch.load(os.path.join(lowerCamelCase       ,      """pytorch_model.bin"""  )  )
				UpperCamelCase_:							Dict		=  {}

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

				if target_model_path is None:
								UpperCamelCase_:							int		=  os.path.join(
								    os.path.dirname(lowerCamelCase  )       ,      F'''bertarized_{os.path.basename(lowerCamelCase  )}'''  )

				if not os.path.isdir(lowerCamelCase  ):
								shutil.copytree(lowerCamelCase       ,      lowerCamelCase  )
								print(F'''\nCreated folder {target_model_path}'''  )

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


if __name__ == "__main__":
					lowerCamelCase_  :	Optional[int] 				=					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""",
					)

					lowerCamelCase_  :	Any 				=					parser.parse_args()

					main(args)








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


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

lowerCamelCase_  :	Dict 				=					{
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	"""luke"""

			def __init__(    self	:    Tuple  ,		snake_case_	:    List[Any]=5_0267  ,		snake_case_	:    Any=50_0000  ,		snake_case_	:    str=768  ,		snake_case_	:    int=256  ,		snake_case_	:    str=12  ,		snake_case_	:    int=12  ,		snake_case_	:    Dict=3072  ,		snake_case_	:    Optional[Any]="gelu"  ,		snake_case_	:    Dict=0.1  ,		snake_case_	:    List[str]=0.1  ,		snake_case_	:    int=512  ,		snake_case_	:    Dict=2  ,		snake_case_	:    List[Any]=0.02  ,		snake_case_	:    int=1e-12  ,		snake_case_	:    Union[str, Any]=True  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Dict=1  ,		snake_case_	:    Optional[int]=0  ,		snake_case_	:    List[str]=2  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(pad_token_id=snake_case_  ,		bos_token_id=snake_case_  ,		eos_token_id=snake_case_  ,		**snake_case_ )

							UpperCamelCase_:							Dict		=  vocab_size
							UpperCamelCase_:							Tuple		=  entity_vocab_size
							UpperCamelCase_:							Optional[int]		=  hidden_size
							UpperCamelCase_:							Any		=  entity_emb_size
							UpperCamelCase_:							str		=  num_hidden_layers
							UpperCamelCase_:							Union[str, Any]		=  num_attention_heads
							UpperCamelCase_:							Dict		=  hidden_act
							UpperCamelCase_:							Dict		=  intermediate_size
							UpperCamelCase_:							str		=  hidden_dropout_prob
							UpperCamelCase_:							List[str]		=  attention_probs_dropout_prob
							UpperCamelCase_:							int		=  max_position_embeddings
							UpperCamelCase_:							int		=  type_vocab_size
							UpperCamelCase_:							List[Any]		=  initializer_range
							UpperCamelCase_:							Union[str, Any]		=  layer_norm_eps
							UpperCamelCase_:							Tuple		=  use_entity_aware_attention
							UpperCamelCase_:							int		=  classifier_dropout






