




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							args.serialization_dir
    a__  			=							args.pruning_method
    a__  			=							args.threshold

    a__  			=							torch.load(os.path.join(_SCREAMING_SNAKE_CASE ,     """pytorch_model.bin"""		) ,     map_location="""cpu"""		)

    a__  			=							0  # Number of remaining (not pruned) params in the encoder
    a__  			=							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__  			=							TopKBinarizer.apply(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE		).sum().item()
            elif pruning_method == "sigmoied_threshold":
                a__  			=							ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE		).sum().item()
            elif pruning_method == "l0":
                a__  			=							-0.1, 1.1
                a__  			=							torch.sigmoid(_SCREAMING_SNAKE_CASE		)
                a__  			=							s * (r - l) + l
                a__  			=							s_bar.clamp(min=0.0 ,     max=1.0		)
                a__  			=							(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__":
 SCREAMING_SNAKE_CASE_    :       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''',
 )

 SCREAMING_SNAKE_CASE_    :       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					,       )



