




import argparse
import os
import shutil

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



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		args.pruning_method
							lowerCAmelCase   =		args.threshold

							lowerCAmelCase   =		args.model_name_or_path.rstrip('''/'''							)
							lowerCAmelCase   =		args.target_model_path

							print(F"""Load fine-pruned model from {model_name_or_path}"""							)
							lowerCAmelCase   =		torch.load(os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							lowerCAmelCase   =		{}

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

							if target_model_path is None:
														lowerCAmelCase   =		os.path.join(
														    os.path.dirname(_snake_case							)						, F"""bertarized_{os.path.basename(_snake_case							)}"""							)

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

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


if __name__ == "__main__":
		UpperCAmelCase_						=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_						=parser.parse_args()

		main(args)









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=IFInpaintingSuperResolutionPipeline
				__a							: Dict										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__a							: int										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}  )
				__a							: Union[str, Any]										=PipelineTesterMixin.required_optional_params - {"""latents"""}



				def       __snake_case		( self						):
											return self._get_superresolution_dummy_components()



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											if str(UpperCAmelCase_						).startswith('''mps'''						):
																		lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											else:
																		lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

											lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

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

											return inputs



				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available()       , reason='''XFormers attention is only available with CUDA and `xformers` installed'''       , )
				def       __snake_case		( self						):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



				def       __snake_case		( self						):
											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
				def       __snake_case		( self						):
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1E-1						)



				def       __snake_case		( self						):
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



				def       __snake_case		( self						):
											self._test_save_load_local()






				def       __snake_case		( self						):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2       , )



