


'''simple docstring'''




import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCAmelCase_      :		Union[str, Any]          =		logging.get_logger(__name__)

enable_full_determinism()







class 					a			(				snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[Any]							  =		UNetaDModel
 __lowerCAmelCase						: List[str]							  =		"""sample"""




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Union[str, Any]						=  4
    _a					:  Union[str, Any]						=  3
    _a					:  List[str]						=  (3_2, 3_2)

    _a					:  Union[str, Any]						=  floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCamelCase_      )
    _a					:  Optional[Any]						=  torch.tensor([1_0]      ).to(lowerCamelCase_      )

    return {"sample": noise, "timestep": time_step}




 @property
 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    return (3, 3_2, 3_2)




 @property
 def   __UpperCamelCase   (    self      )      ->    str:
    return (3, 3_2, 3_2)




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  List[Any]						=  {
        'block_out_channels': (3_2, 6_4),
        'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
        'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
        'attention_head_dim': 3,
        'out_channels': 3,
        'in_channels': 3,
        'layers_per_block': 2,
        'sample_size': 3_2,
    }
    _a					:  Tuple						=  self.dummy_input
    return init_dict, inputs_dict







class 					a			(				snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Dict							  =		UNetaDModel
 __lowerCAmelCase						: Optional[int]							  =		"""sample"""




 @property
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  List[str]						=  4
    _a					:  List[str]						=  4
    _a					:  str						=  (3_2, 3_2)

    _a					:  Any						=  floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCamelCase_      )
    _a					:  Tuple						=  torch.tensor([1_0]      ).to(lowerCamelCase_      )

    return {"sample": noise, "timestep": time_step}




 @property
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return (4, 3_2, 3_2)




 @property
 def   __UpperCamelCase   (    self      )      ->    Dict:
    return (4, 3_2, 3_2)




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  int						=  {
        'sample_size': 3_2,
        'in_channels': 4,
        'out_channels': 4,
        'layers_per_block': 2,
        'block_out_channels': (3_2, 6_4),
        'attention_head_dim': 3_2,
        'down_block_types': ('DownBlock2D', 'DownBlock2D'),
        'up_block_types': ('UpBlock2D', 'UpBlock2D'),
    }
    _a					:  Tuple						=  self.dummy_input
    return init_dict, inputs_dict




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a		,						_a					:  Union[str, Any]						=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'		,      output_loading_info=lowerCamelCase_      )

    self.assertIsNotNone(lowerCamelCase_      )
    self.assertEqual(len(loading_info['missing_keys']      )		,      0      )

    model.to(lowerCamelCase_      )
    _a					:  Optional[Any]						=  model(**self.dummy_input      ).sample

    assert image is not None, "Make sure output is not None"




 @unittest.skipIf(torch_device != 'cuda'		,      'This test is supposed to run on GPU'      )
 def   __UpperCamelCase   (    self      )      ->    Any:
    _a		,						_a					:  Optional[Any]						=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'		,      output_loading_info=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    _a					:  Optional[Any]						=  model(**self.dummy_input      ).sample

    assert image is not None, "Make sure output is not None"




 @unittest.skipIf(torch_device != 'cuda'		,      'This test is supposed to run on GPU'      )
 def   __UpperCamelCase   (    self      )      ->    int:
    # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
    _a		,						_a					:  Union[str, Any]						=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'		,      output_loading_info=lowerCamelCase_      )
    model_accelerate.to(lowerCamelCase_      )
    model_accelerate.eval()

    _a					:  str						=  torch.randn(
        1		,      model_accelerate.config.in_channels		,      model_accelerate.config.sample_size		,      model_accelerate.config.sample_size		,      generator=torch.manual_seed(0      )		,      )
    _a					:  int						=  noise.to(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  torch.tensor([1_0] * noise.shape[0]      ).to(lowerCamelCase_      )

    _a					:  Any						=  model_accelerate(lowerCamelCase_		,      lowerCamelCase_      )['sample']

    # two models don't need to stay in the device at the same time
    del model_accelerate
    torch.cuda.empty_cache()
    gc.collect()

    _a		,						_a					:  Optional[int]						=  UNetaDModel.from_pretrained(
        'fusing/unet-ldm-dummy-update'		,      output_loading_info=lowerCamelCase_		,      low_cpu_mem_usage=lowerCamelCase_      )
    model_normal_load.to(lowerCamelCase_      )
    model_normal_load.eval()
    _a					:  str						=  model_normal_load(lowerCamelCase_		,      lowerCamelCase_      )['sample']

    assert torch_all_close(lowerCamelCase_		,      lowerCamelCase_		,      rtol=1e-3      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Optional[Any]						=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      )
    model.eval()
    model.to(lowerCamelCase_      )

    _a					:  Tuple						=  torch.randn(
        1		,      model.config.in_channels		,      model.config.sample_size		,      model.config.sample_size		,      generator=torch.manual_seed(0      )		,      )
    _a					:  List[str]						=  noise.to(lowerCamelCase_      )
    _a					:  Dict						=  torch.tensor([1_0] * noise.shape[0]      ).to(lowerCamelCase_      )

    with torch.no_grad():
       _a					:  Any						=  model(lowerCamelCase_		,      lowerCamelCase_      ).sample

    _a					:  Optional[int]						=  output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _a					:  List[str]						=  torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]      )
    # fmt: on

    self.assertTrue(torch_all_close(lowerCamelCase_		,      lowerCamelCase_		,      rtol=1e-3      )      )







class 					a			(				snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: str							  =		UNetaDModel
 __lowerCAmelCase						: int							  =		"""sample"""




 @property
 def   __UpperCamelCase   (    self		,      lowerCamelCase_=(3_2, 3_2)      )      ->    List[Any]:
    _a					:  int						=  4
    _a					:  Optional[int]						=  3

    _a					:  Union[str, Any]						=  floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCamelCase_      )
    _a					:  int						=  torch.tensor(batch_size * [1_0]      ).to(dtype=torch.intaa		,      device=lowerCamelCase_      )

    return {"sample": noise, "timestep": time_step}




 @property
 def   __UpperCamelCase   (    self      )      ->    Dict:
    return (3, 3_2, 3_2)




 @property
 def   __UpperCamelCase   (    self      )      ->    str:
    return (3, 3_2, 3_2)




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Any						=  {
        'block_out_channels': [3_2, 6_4, 6_4, 6_4],
        'in_channels': 3,
        'layers_per_block': 1,
        'out_channels': 3,
        'time_embedding_type': 'fourier',
        'norm_eps': 1e-6,
        'mid_block_scale_factor': math.sqrt(2.0      ),
        'norm_num_groups': None,
        'down_block_types': [
            'SkipDownBlock2D',
            'AttnSkipDownBlock2D',
            'SkipDownBlock2D',
            'SkipDownBlock2D',
        ],
        'up_block_types': [
            'SkipUpBlock2D',
            'SkipUpBlock2D',
            'AttnSkipUpBlock2D',
            'SkipUpBlock2D',
        ],
    }
    _a					:  int						=  self.dummy_input
    return init_dict, inputs_dict




 @slow
 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a		,						_a					:  Dict						=  UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'		,      output_loading_info=lowerCamelCase_      )
    self.assertIsNotNone(lowerCamelCase_      )
    self.assertEqual(len(loading_info['missing_keys']      )		,      0      )

    model.to(lowerCamelCase_      )
    _a					:  Any						=  self.dummy_input
    _a					:  str						=  floats_tensor((4, 3) + (2_5_6, 2_5_6)      ).to(lowerCamelCase_      )
    _a					:  Optional[int]						=  noise
    _a					:  List[str]						=  model(**lowerCamelCase_      )

    assert image is not None, "Make sure output is not None"




 @slow
 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Optional[Any]						=  UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'      )
    model.to(lowerCamelCase_      )

    _a					:  Any						=  4
    _a					:  Union[str, Any]						=  3
    _a					:  Optional[int]						=  (2_5_6, 2_5_6)

    _a					:  Any						=  torch.ones((batch_size, num_channels) + sizes      ).to(lowerCamelCase_      )
    _a					:  List[str]						=  torch.tensor(batch_size * [1e-4]      ).to(lowerCamelCase_      )

    with torch.no_grad():
       _a					:  Any						=  model(lowerCamelCase_		,      lowerCamelCase_      ).sample

    _a					:  str						=  output[0, -3:, -3:, -1].flatten().cpu()
    # fmt: off
    _a					:  Optional[int]						=  torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]      )
    # fmt: on

    self.assertTrue(torch_all_close(lowerCamelCase_		,      lowerCamelCase_		,      rtol=1e-2      )      )




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Union[str, Any]						=  UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'      )
    model.to(lowerCamelCase_      )

    _a					:  Union[str, Any]						=  4
    _a					:  List[str]						=  3
    _a					:  Any						=  (3_2, 3_2)

    _a					:  Dict						=  torch.ones((batch_size, num_channels) + sizes      ).to(lowerCamelCase_      )
    _a					:  List[Any]						=  torch.tensor(batch_size * [1e-4]      ).to(lowerCamelCase_      )

    with torch.no_grad():
       _a					:  Union[str, Any]						=  model(lowerCamelCase_		,      lowerCamelCase_      ).sample

    _a					:  int						=  output[0, -3:, -3:, -1].flatten().cpu()
    # fmt: off
    _a					:  List[str]						=  torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]      )
    # fmt: on

    self.assertTrue(torch_all_close(lowerCamelCase_		,      lowerCamelCase_		,      rtol=1e-2      )      )




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    # not required for this model
    pass






'''simple docstring'''




import argparse
import os
import shutil

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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  args.pruning_method
   _a					:  Optional[Any]						=  args.threshold

   _a					:  Union[str, Any]						=  args.model_name_or_path.rstrip('/'      )
   _a					:  Dict						=  args.target_model_path

   print(f'''Load fine-pruned model from {model_name_or_path}'''      )
   _a					:  List[str]						=  torch.load(os.path.join(A		,	'pytorch_model.bin'      )      )
   _a					:  str						=  {}

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

   if target_model_path is None:
      _a					:  Tuple						=  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__":
 UpperCAmelCase_      :		str          =		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",
 )

 UpperCAmelCase_      :		Optional[Any]          =		parser.parse_args()

 main(args)


