



'''simple docstring'''





import argparse
import os
import shutil

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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  args.pruning_method
     _a      =  args.threshold

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

     print(F'''Load fine-pruned model from {model_name_or_path}''')
     _a      =  torch.load(os.path.join(__A  ,	'''pytorch_model.bin'''))
     _a      =  {}

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

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

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

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


if __name__ == "__main__":
  lowercase_		       =  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",
  )

  lowercase_		       =  parser.parse_args()

  main(args)








'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class __A			:



 '''simple docstring'''







 __lowerCamelCase    :       CommonSchedulerState

 # setable values
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       Optional[int]          =       None



 @classmethod
 def 					a__  (cls ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



      return cls(common=A ,   init_noise_sigma=A ,   timesteps=A       )






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       DDPMSchedulerState






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCamelCase    :       jnp.dtype



 @property
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self ,   A = 1_000 ,   A = 0.0001 ,   A = 0.02 ,   A = "linear" ,   A = None ,   A = "fixed_small" ,   A = True ,   A = "epsilon" ,   A = jnp.floataa ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  dtype



 def 					a__  (self ,   A = None       )    ->     DDPMSchedulerState:


      """simple docstring"""



      if common is None:
           _a      =  CommonSchedulerState.create(self       )

      # standard deviation of the initial noise distribution
      _a      =  jnp.array(1.0 ,   dtype=self.dtype       )

      _a      =  jnp.arange(0 ,   self.config.num_train_timesteps       ).round()[::-1]

      return DDPMSchedulerState.create(
          common=A ,   init_noise_sigma=A ,   timesteps=A ,   )



 def 					a__  (self ,   A ,   A ,   A = None       )    ->     jnp.ndarray:


      """simple docstring"""



      return sample



 def 					a__  (self ,   A ,   A ,   A = ()       )    ->     DDPMSchedulerState:


      """simple docstring"""



      _a      =  self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      _a      =  (jnp.arange(0 ,   A       ) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=A ,   timesteps=A ,   )



 def 					a__  (self ,   A ,   A ,   A=None ,   A=None       )    ->     int:


      """simple docstring"""



      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      _a      =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           _a      =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           _a      =  jnp.clip(A ,   a_min=1E-20       )
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           _a      =  jnp.log(jnp.clip(A ,   a_min=1E-20       )       )
      elif variance_type == "fixed_large":
           _a      =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           _a      =  jnp.log(state.common.betas[t]       )
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           _a      =  variance
           _a      =  state.common.betas[t]
           _a      =  (predicted_variance + 1) / 2
           _a      =  frac * max_log + (1 - frac) * min_log

      return variance



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = True ,   )    ->     Union[FlaxDDPMSchedulerOutput, Tuple]:


      """simple docstring"""



      _a      =  timestep

      if key is None:
           _a      =  jax.random.PRNGKey(0       )

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           _a  ,    _a      =  jnp.split(A ,   sample.shape[1] ,   axis=1       )
      else:
           _a      =  None

      # 1. compute alphas, betas
      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )
      _a      =  1 - alpha_prod_t
      _a      =  1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
           _a      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           _a      =  model_output
      elif self.config.prediction_type == "v_prediction":
           _a      =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
               ''' for the FlaxDDPMScheduler.'''       )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           _a      =  jnp.clip(A ,   -1 ,   1       )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _a      =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      _a      =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _a      =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           _a      =  jax.random.split(A ,   num=1       )
           _a      =  jax.random.normal(A ,   shape=model_output.shape ,   dtype=self.dtype       )
           return (self._get_variance(A ,   A ,   predicted_variance=A       ) ** 0.5) * noise

      _a      =  jnp.where(t > 0 ,   random_variance() ,   jnp.zeros(model_output.shape ,   dtype=self.dtype       )       )

      _a      =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A ,   state=A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return add_noise_common(state.common ,   A ,   A ,   A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return get_velocity_common(state.common ,   A ,   A ,   A       )



 def __len__(self       )    ->     Tuple:


      """simple docstring"""



      return self.config.num_train_timesteps



