

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =1

    @register_to_config
    def __init__(      self			:   Dict				,     snake_case__			:   Optional[int]=2_0_0_0				,     snake_case__			:   Any=0.1				,     snake_case__			:   Dict=2_0				,     snake_case__			:   int=1E-3     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      None
     SCREAMING_SNAKE_CASE		    =      None
     SCREAMING_SNAKE_CASE		    =      None

    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   str				,     snake_case__			:   Union[str, torch.device] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      torch.linspace(1				,     self.config.sampling_eps				,     snake_case__				,     device=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   str				,     snake_case__			:   List[str]=None     ):

     """simple docstring"""






     if self.timesteps is None:
      raise ValueError(
          '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'     )

     # TODO(Patrick) better comments + non-PyTorch
     # postprocess model score
     SCREAMING_SNAKE_CASE		    =      (
         -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     SCREAMING_SNAKE_CASE		    =      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
     SCREAMING_SNAKE_CASE		    =      std.flatten()
     while len(std.shape     ) < len(score.shape     ):
      SCREAMING_SNAKE_CASE		    =      std.unsqueeze(-1     )
     SCREAMING_SNAKE_CASE		    =      -score / std

     # compute
     SCREAMING_SNAKE_CASE		    =      -1.0 / len(self.timesteps     )

     SCREAMING_SNAKE_CASE		    =      self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
     SCREAMING_SNAKE_CASE		    =      beta_t.flatten()
     while len(beta_t.shape     ) < len(x.shape     ):
      SCREAMING_SNAKE_CASE		    =      beta_t.unsqueeze(-1     )
     SCREAMING_SNAKE_CASE		    =      -0.5 * beta_t * x

     SCREAMING_SNAKE_CASE		    =      torch.sqrt(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      drift - diffusion**2 * score
     SCREAMING_SNAKE_CASE		    =      x + drift * dt

     # add noise
     SCREAMING_SNAKE_CASE		    =      randn_tensor(x.shape				,     layout=x.layout				,     generator=snake_case__				,     device=x.device				,     dtype=x.dtype     )
     SCREAMING_SNAKE_CASE		    =      x_mean + diffusion * math.sqrt(-dt     ) * noise

     return x, x_mean




    def __len__(      self			:   Union[str, Any]     ):

     """simple docstring"""






     return self.config.num_train_timesteps




import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

 SCREAMING_SNAKE_CASE		    =      args.model_name_or_path.rstrip('/'						)
 SCREAMING_SNAKE_CASE		    =      args.target_model_path

 print(f"""Load fine-pruned model from {model_name_or_path}"""						)
 SCREAMING_SNAKE_CASE		    =      torch.load(os.path.join(_UpperCamelCase		,				'pytorch_model.bin'						)						)
 SCREAMING_SNAKE_CASE		    =      {}

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

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

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

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


if __name__ == "__main__":
      a_     :    Union[str, 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",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

