"""simple docstring"""







import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Union[str, Any]						)    ->     Union[str, Any]:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		args.serialization_dir
     __A					:					List[Any]								=		args.pruning_method
     __A					:					List[Any]								=		args.threshold

     __A					:					int								=		torch.load(os.path.join(_SCREAMING_SNAKE_CASE  ,       'pytorch_model.bin'						)  ,       map_location='cpu'						)

     __A					:					List[Any]								=		0  # Number of remaining (not pruned) params in the encoder
     __A					:					Dict								=		0  # Number of params in the encoder

     print('name'.ljust(60  ,       ' '						)  ,       'Remaining Weights %'  ,       'Remaining Weight'						)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __A					:					Dict								=		TopKBinarizer.apply(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __A					:					Dict								=		ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						).sum().item()
               elif pruning_method == "l0":
                    __A						,		__A					:					str								=		-0.1, 1.1
                    __A					:					int								=		torch.sigmoid(_SCREAMING_SNAKE_CASE						)
                    __A					:					Any								=		s * (r - l) + l
                    __A					:					Optional[int]								=		s_bar.clamp(min=0.0  ,       max=1.0						)
                    __A					:					Dict								=		(mask > 0.0).sum().item()
               else:
                    raise ValueError('Unknown pruning method'						)
               remaining_count += mask_ones
               print(name.ljust(60  ,       ' '						)  ,       str(round(100 * mask_ones / param.numel()  ,       3						)						).ljust(20  ,       ' '						)  ,       str(_SCREAMING_SNAKE_CASE						)						)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''						)
     print('Remaining Weights (global) %: '  ,       100 * remaining_count / encoder_count						)


if __name__ == "__main__":
       lowerCamelCase      : Union[str, Any]       =argparse.ArgumentParser()

       parser.add_argument(
           '''--pruning_method''',
           choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
           type=str,
           required=True,
           help=(
               '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
               ''' pruning)'''
           ),
       )
       parser.add_argument(
           '''--threshold''',
           type=float,
           required=False,
           help=(
               '''For `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(
           '''--serialization_dir''',
           type=str,
           required=True,
           help='''Folder containing the model that was previously fine-pruned''',
       )

       lowerCamelCase      : List[Any]       =parser.parse_args()

       main(args)




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



