



'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						DiTPipeline
					A_						    =						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
					A_						    =						PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					A_						    =						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
					A_						    =						False




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Tuple:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Union[str, Any]       =		TransformeraDModel(
						    sample_size=16     ,  num_layers=2     ,  patch_size=4     ,  attention_head_dim=8     ,  num_attention_heads=2     ,  in_channels=4     ,  out_channels=8     ,  attention_bias=UpperCamelCase_     ,  activation_fn='gelu-approximate'     ,  num_embeds_ada_norm=1000     ,  norm_type='ada_norm_zero'     ,  norm_elementwise_affine=UpperCamelCase_     ,  )
						_lowercase					:       Optional[Any]       =		AutoencoderKL()
						_lowercase					:       Tuple       =		DDIMScheduler()
						_lowercase					:       Tuple       =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
						return components




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	List[str]=0					)  ->       List[str]:



						'''simple docstring'''





						if str(UpperCamelCase_					).startswith('mps'					):
							_lowercase					:       Any       =		torch.manual_seed(UpperCamelCase_					)
						else:
							_lowercase					:       Dict       =		torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
						_lowercase					:       Any       =		{
						    'class_labels': [1],
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}
						return inputs




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		'cpu'

						_lowercase					:       Union[str, Any]       =		self.get_dummy_components()
						_lowercase					:       Tuple       =		self.pipeline_class(**UpperCamelCase_					)
						pipe.to(UpperCamelCase_					)
						pipe.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       List[str]       =		self.get_dummy_inputs(UpperCamelCase_					)
						_lowercase					:       Optional[int]       =		pipe(**UpperCamelCase_					).images
						_lowercase					:       Dict       =		image[0, -3:, -3:, -1]

						self.assertEqual(image.shape     ,  (1, 16, 16, 3)					)
						_lowercase					:       Tuple       =		np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]					)
						_lowercase					:       Union[str, Any]       =		np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(UpperCamelCase_     ,  1E-3					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



						'''simple docstring'''





						self._test_inference_batch_single_identical(relax_max_difference=UpperCamelCase_     ,  expected_max_diff=1E-3					)




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



						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)






@require_torch_gpu
@slow
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		torch.manual_seed(0					)

						_lowercase					:       Dict       =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'					)
						pipe.to('cuda'					)

						_lowercase					:       List[str]       =		['vase', 'umbrella', 'white shark', 'white wolf']
						_lowercase					:       List[str]       =		pipe.get_label_ids(UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		pipe(UpperCamelCase_     ,  generator=UpperCamelCase_     ,  num_inference_steps=40     ,  output_type='np'					).images

						for word, image in zip(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       List[str]       =		load_numpy(
							    F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''					)
							assert np.abs((expected_image - image).max()					) < 1E-2




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       List[str]       =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'					)
						_lowercase					:       Tuple       =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
						pipe.to('cuda'					)

						_lowercase					:       Any       =		['vase', 'umbrella']
						_lowercase					:       int       =		pipe.get_label_ids(UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		torch.manual_seed(0					)
						_lowercase					:       int       =		pipe(UpperCamelCase_     ,  generator=UpperCamelCase_     ,  num_inference_steps=25     ,  output_type='np'					).images

						for word, image in zip(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Dict       =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    F'''/dit/{word}_512.npy'''					)

							assert np.abs((expected_image - image).max()					) < 1E-1





'''simple docstring'''

import argparse
import os
import shutil

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







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Tuple       =		args.pruning_method
	_lowercase					:       int       =		args.threshold

	_lowercase					:       str       =		args.model_name_or_path.rstrip('/'					)
	_lowercase					:       Dict       =		args.target_model_path

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

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

	if target_model_path is None:
		_lowercase					:       List[Any]       =		os.path.join(
		    os.path.dirname(_lowercase					),				f'''bertarized_{os.path.basename(_lowercase					)}'''					)

	if not os.path.isdir(_lowercase					):
		shutil.copytree(_lowercase,				_lowercase					)
		print(f'''\nCreated folder {target_model_path}'''					)

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


if __name__ == "__main__":
		_A				:    Union[str, Any]     						=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''',
		)

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

		main(args)
