


"""simple docstring"""



import argparse
import os
import shutil

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







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		int:


    """simple docstring"""





    snake_case					:   str         =					args.pruning_method
    snake_case					:   Dict         =					args.threshold

    snake_case					:   List[str]         =					args.model_name_or_path.rstrip("/"				)
    snake_case					:   Optional[Any]         =					args.target_model_path

    print(F'Load fine-pruned model from {model_name_or_path}'				)
    snake_case					:   Union[str, Any]         =					torch.load(os.path.join(_lowerCamelCase		,							"pytorch_model.bin"				)				)
    snake_case					:   Tuple         =					{}

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

    if target_model_path is None:
        snake_case					:   List[str]         =					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__":
  __snake_case     :   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""",
  )

  __snake_case     :   str         =					parser.parse_args()

  main(args)









"""simple docstring"""



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[]
       snake_case					:   Tuple         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Any         =					self.in_channels if i == 0 else self.out_channels

           snake_case					:   Optional[int]         =					FlaxResnetBlockaD(
               in_channels=UpperCamelCase__		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

           snake_case					:   Dict         =					FlaxTransformeraDModel(
               in_channels=self.out_channels		,			n_heads=self.num_attention_heads		,			d_head=self.out_channels // self.num_attention_heads		,			depth=1		,			use_linear_projection=self.use_linear_projection		,			only_cross_attention=self.only_cross_attention		,			use_memory_efficient_attention=self.use_memory_efficient_attention		,			dtype=self.dtype		,			)
           attentions.append(UpperCamelCase__		)

       snake_case					:   List[Any]         =					resnets
       snake_case					:   List[Any]         =					attentions

       if self.add_downsample:
           snake_case					:   Optional[Any]         =					FlaxDownsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Tuple         =					()

       for resnet, attn in zip(self.resnets		,			self.attentions		):
           snake_case					:   str         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   int         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           output_states += (hidden_states,)

       if self.add_downsample:
           snake_case					:   Any         =					self.downsamplers_a(UpperCamelCase__		)
           output_states += (hidden_states,)

       return hidden_states, output_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]

       for i in range(self.num_layers		):
           snake_case					:   List[str]         =					self.in_channels if i == 0 else self.out_channels

           snake_case					:   str         =					FlaxResnetBlockaD(
               in_channels=UpperCamelCase__		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)
       snake_case					:   str         =					resnets

       if self.add_downsample:
           snake_case					:   str         =					FlaxDownsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					()

       for resnet in self.resnets:
           snake_case					:   Optional[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           output_states += (hidden_states,)

       if self.add_downsample:
           snake_case					:   Tuple         =					self.downsamplers_a(UpperCamelCase__		)
           output_states += (hidden_states,)

       return hidden_states, output_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       snake_case					:   Union[str, Any]         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Optional[Any]         =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
           snake_case					:   Union[str, Any]         =					self.prev_output_channel if i == 0 else self.out_channels

           snake_case					:   Optional[Any]         =					FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

           snake_case					:   int         =					FlaxTransformeraDModel(
               in_channels=self.out_channels		,			n_heads=self.num_attention_heads		,			d_head=self.out_channels // self.num_attention_heads		,			depth=1		,			use_linear_projection=self.use_linear_projection		,			only_cross_attention=self.only_cross_attention		,			use_memory_efficient_attention=self.use_memory_efficient_attention		,			dtype=self.dtype		,			)
           attentions.append(UpperCamelCase__		)

       snake_case					:   str         =					resnets
       snake_case					:   List[Any]         =					attentions

       if self.add_upsample:
           snake_case					:   str         =					FlaxUpsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	Optional[int]:

       '''simple docstring'''




       for resnet, attn in zip(self.resnets		,			self.attentions		):
           # pop res hidden states
           snake_case					:   Dict         =					res_hidden_states_tuple[-1]
           snake_case					:   Optional[int]         =					res_hidden_states_tuple[:-1]
           snake_case					:   List[Any]         =					jnp.concatenate((hidden_states, res_hidden_states)		,			axis=-1		)

           snake_case					:   List[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   Any         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       if self.add_upsample:
           snake_case					:   int         =					self.upsamplers_a(UpperCamelCase__		)

       return hidden_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   str         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Union[str, Any]         =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
           snake_case					:   Optional[Any]         =					self.prev_output_channel if i == 0 else self.out_channels

           snake_case					:   Dict         =					FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

       snake_case					:   Optional[int]         =					resnets

       if self.add_upsample:
           snake_case					:   Dict         =					FlaxUpsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	int:

       '''simple docstring'''




       for resnet in self.resnets:
           # pop res hidden states
           snake_case					:   int         =					res_hidden_states_tuple[-1]
           snake_case					:   List[Any]         =					res_hidden_states_tuple[:-1]
           snake_case					:   Optional[int]         =					jnp.concatenate((hidden_states, res_hidden_states)		,			axis=-1		)

           snake_case					:   Dict         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       if self.add_upsample:
           snake_case					:   Optional[int]         =					self.upsamplers_a(UpperCamelCase__		)

       return hidden_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Dict         =					[
           FlaxResnetBlockaD(
               in_channels=self.in_channels		,			out_channels=self.in_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
       ]

       snake_case					:   Any         =					[]

       for _ in range(self.num_layers		):
           snake_case					:   Union[str, Any]         =					FlaxTransformeraDModel(
               in_channels=self.in_channels		,			n_heads=self.num_attention_heads		,			d_head=self.in_channels // self.num_attention_heads		,			depth=1		,			use_linear_projection=self.use_linear_projection		,			use_memory_efficient_attention=self.use_memory_efficient_attention		,			dtype=self.dtype		,			)
           attentions.append(UpperCamelCase__		)

           snake_case					:   List[Any]         =					FlaxResnetBlockaD(
               in_channels=self.in_channels		,			out_channels=self.in_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					resnets
       snake_case					:   str         =					attentions







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.resnets[0](UpperCamelCase__		,			UpperCamelCase__		)
       for attn, resnet in zip(self.attentions		,			self.resnets[1:]		):
           snake_case					:   Union[str, Any]         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       return hidden_states





