




import argparse
import os
import shutil

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

def 			a   (_lowerCAmelCase     ):
       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(_SCREAMING_SNAKE_CASE  ,    '''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=_SCREAMING_SNAKE_CASE  ,    threshold=_SCREAMING_SNAKE_CASE     )
                            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(_SCREAMING_SNAKE_CASE  ,    _SCREAMING_SNAKE_CASE     )
                            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(_SCREAMING_SNAKE_CASE  ,    _SCREAMING_SNAKE_CASE  ,    _SCREAMING_SNAKE_CASE     )
                            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(_SCREAMING_SNAKE_CASE     )
                            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(_SCREAMING_SNAKE_CASE     )  ,    F"bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE     )}"     )

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

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


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

   __SCREAMING_SNAKE_CASE 						=parser.parse_args()

   main(args)








import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
			import jax






@require_flax
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					FlaxAutoencoderKL


				@property
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    jax.random.PRNGKey(0		)
											SCREAMING_SNAKE_CASE_   	=    jax.random.uniform(_lowerCamelCase		,					((batch_size, num_channels) + sizes)		)

											return {"sample": image, "prng_key": prng_key}





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': [32, 64],
											    '''in_channels''': 3,
											    '''out_channels''': 3,
											    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
											    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
											    '''latent_channels''': 4,
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


