
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    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[str]       =					DiTPipeline
	_UpperCAmelCase       :		Union[str, Any]       =					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	_UpperCAmelCase       :		Optional[int]       =					PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	_UpperCAmelCase       :		Tuple       =					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	_UpperCAmelCase       :		Union[str, Any]       =					False



	def  A							(  self	:    int	):


				'''simple docstring'''


				torch.manual_seed(0	)
				_snake_case		=			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=lowercase			,  activation_fn='gelu-approximate'			,  num_embeds_ada_norm=1_000			,  norm_type='ada_norm_zero'			,  norm_elementwise_affine=lowercase			,  )
				_snake_case		=			AutoencoderKL()
				_snake_case		=			DDIMScheduler()
				_snake_case		=			{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
				return components



	def  A							(  self	:    Optional[Any]			,  lowercase	:    Dict			,  lowercase	:    int=0	):


				'''simple docstring'''


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



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'

				_snake_case		=			self.get_dummy_components()
				_snake_case		=			self.pipeline_class(**lowercase	)
				pipe.to(lowercase	)
				pipe.set_progress_bar_config(disable=lowercase	)

				_snake_case		=			self.get_dummy_inputs(lowercase	)
				_snake_case		=			pipe(**lowercase	).images
				_snake_case		=			image[0, -3:, -3:, -1]

				self.assertEqual(image.shape			,  (1, 16, 16, 3)	)
				_snake_case		=			np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]	)
				_snake_case		=			np.abs(image_slice.flatten() - expected_slice	).max()
				self.assertLessEqual(lowercase			,  1E-3	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				self._test_inference_batch_single_identical(relax_max_difference=lowercase			,  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  A							(  self	:    str	):


				'''simple docstring'''


				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3	)




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





	'''simple docstring'''



	def  A							(  self	:    Any	):


				'''simple docstring'''


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



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			torch.manual_seed(0	)

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

				_snake_case		=			['vase', 'umbrella', 'white shark', 'white wolf']
				_snake_case		=			pipe.get_label_ids(lowercase	)

				_snake_case		=			pipe(lowercase			,  generator=lowercase			,  num_inference_steps=40			,  output_type='np'	).images

				for word, image in zip(lowercase			,  lowercase	):
							_snake_case		=			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  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'	)
				_snake_case		=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
				pipe.to('cuda'	)

				_snake_case		=			['vase', 'umbrella']
				_snake_case		=			pipe.get_label_ids(lowercase	)

				_snake_case		=			torch.manual_seed(0	)
				_snake_case		=			pipe(lowercase			,  generator=lowercase			,  num_inference_steps=25			,  output_type='np'	).images

				for word, image in zip(lowercase			,  lowercase	):
							_snake_case		=			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

import argparse
import os
import shutil

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






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

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

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

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

			if target_model_path is None:
						_snake_case		=			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__":
			_lowerCamelCase       :				Dict       =			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''',
			)

			_lowerCamelCase       :				int       =			parser.parse_args()

			main(args)