




"""simple docstring"""





import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class  __lowercase    (     nn.Linear		):


				'''simple docstring'''


				def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       bool = True ,SCREAMING_SNAKE_CASE_				:       str = "constant" ,SCREAMING_SNAKE_CASE_				:       float = 0.0 ,SCREAMING_SNAKE_CASE_				:       str = "topK" ,):
										super(SCREAMING_SNAKE_CASE_ ,self ).__init__(in_features=SCREAMING_SNAKE_CASE_ ,out_features=SCREAMING_SNAKE_CASE_ ,bias=SCREAMING_SNAKE_CASE_ )
										assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
										snake_case__    :   List[Any]							     =   pruning_method

										if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																snake_case__    :   Union[str, Any]							     =   mask_scale
																snake_case__    :   str							     =   mask_init
																snake_case__    :   List[Any]							     =   nn.Parameter(torch.empty(self.weight.size() ) )
																self.init_mask()

				def  _lowerCamelCase   (				self				:       str ):
										if self.mask_init == "constant":
																init.constant_(self.mask_scores ,val=self.mask_scale )
										elif self.mask_init == "uniform":
																init.uniform_(self.mask_scores ,a=-self.mask_scale ,b=self.mask_scale )
										elif self.mask_init == "kaiming":
																init.kaiming_uniform_(self.mask_scores ,a=math.sqrt(5 ) )





				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       torch.tensor ,SCREAMING_SNAKE_CASE_				:       float ):
										# Get the mask
										if self.pruning_method == "topK":
																snake_case__    :   Optional[Any]							     =   TopKBinarizer.apply(self.mask_scores ,SCREAMING_SNAKE_CASE_ )
										elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																snake_case__    :   Optional[int]							     =   '''sigmoied''' in self.pruning_method
																snake_case__    :   Optional[int]							     =   ThresholdBinarizer.apply(self.mask_scores ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
										elif self.pruning_method == "magnitude":
																snake_case__    :   Optional[Any]							     =   MagnitudeBinarizer.apply(self.weight ,SCREAMING_SNAKE_CASE_ )
										elif self.pruning_method == "l0":
																snake_case__     ,		snake_case__     ,		snake_case__    :   int							     =   -0.1, 1.1, 2 / 3
																if self.training:
																						snake_case__    :   Union[str, Any]							     =   torch.zeros_like(self.mask_scores ).uniform_().clamp(0.0001 ,0.9999 )
																						snake_case__    :   List[Any]							     =   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b )
																else:
																						snake_case__    :   Any							     =   torch.sigmoid(self.mask_scores )
																snake_case__    :   Tuple							     =   s * (r - l) + l
																snake_case__    :   str							     =   s_bar.clamp(min=0.0 ,max=1.0 )
										# Mask weights with computed mask
										snake_case__    :   Dict							     =   mask * self.weight
										# Compute output (linear layer) with masked weights
										return nn.functional.linear(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.bias )








"""simple docstring"""





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						if "model" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''model.''' ,     ''''''						)
						if "norm1" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''norm1''' ,     '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''norm2''' ,     '''output.LayerNorm'''						)
						if "norm" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''norm''' ,     '''LayerNorm'''						)
						if "transformer" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												snake_case__    :   Optional[Any]							     =   orig_key.replace(f'''transformer_{layer_num}''' ,     f'''encoder.layer.{layer_num}'''						)
						if "mha.attn" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''mha.attn''' ,     '''attention.self'''						)
						if "mha" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''mha''' ,     '''attention'''						)
						if "W_q" in orig_key:
												snake_case__    :   Optional[int]							     =   orig_key.replace('''W_q''' ,     '''self.query'''						)
						if "W_k" in orig_key:
												snake_case__    :   Tuple							     =   orig_key.replace('''W_k''' ,     '''self.key'''						)
						if "W_v" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''W_v''' ,     '''self.value'''						)
						if "ff.0" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''ff.0''' ,     '''intermediate.dense'''						)
						if "ff.2" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''ff.2''' ,     '''output.dense'''						)
						if "ff" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''ff''' ,     '''output.dense'''						)
						if "mlm_class" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''mlm.mlm_class''' ,     '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												snake_case__    :   List[Any]							     =   orig_key.replace('''mlm''' ,     '''cls.predictions.transform'''						)
						if "backbone.backbone.encoders" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.replace('''backbone.backbone.encoders''' ,     '''encoder.layer'''						)
						if "cls" not in orig_key:
												snake_case__    :   Union[str, Any]							     =   '''mra.''' + orig_key

						return orig_key

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case__    :   Optional[int]							     =   val

						snake_case__    :   Union[str, Any]							     =   orig_state_dict['''cls.predictions.decoder.bias''']
						snake_case__    :   Any							     =   torch.arange(lowerCamelCase						).expand((1, -1)						) + 2

						return orig_state_dict

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   torch.load(lowerCamelCase ,     map_location='''cpu'''						)['''model_state_dict''']
						snake_case__    :   Optional[Any]							     =   MraConfig.from_json_file(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   MraForMaskedLM(lowerCamelCase						)

						snake_case__    :   List[Any]							     =   convert_checkpoint_helper(config.max_position_embeddings ,     lowerCamelCase						)

						print(model.load_state_dict(lowerCamelCase						)						)
						model.eval()
						model.save_pretrained(lowerCamelCase						)

						print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)


if __name__ == "__main__":
							_lowercase		 =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for Mra model config.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							_lowercase		 =      parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


