




"""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 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





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


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   torch.manual_seed(0		)
   _snake_case							:		Tuple								=  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=a_,      activation_fn="""gelu-approximate""",      num_embeds_ada_norm=1_000,      norm_type="""ada_norm_zero""",      norm_elementwise_affine=a_,      )
   _snake_case							:		List[Any]								=  AutoencoderKL()
   _snake_case							:		Any								=  DDIMScheduler()
   _snake_case							:		Optional[Any]								=  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
   return components


 def  UpperCamelCase_   (				self:		Any,      a_:		Dict,      a_:		List[Any]=0		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  """cpu"""

   _snake_case							:		str								=  self.get_dummy_components()
   _snake_case							:		Union[str, Any]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		int								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Any								=  pipe(**a_		).images
   _snake_case							:		Tuple								=  image[0, -3:, -3:, -1]

   self.assertEqual(image.shape,      (1, 16, 16, 3)		)
   _snake_case							:		List[str]								=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]		)
   _snake_case							:		Optional[Any]								=  np.abs(image_slice.flatten() - expected_slice		).max()
   self.assertLessEqual(a_,      1E-3		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   self._test_inference_batch_single_identical(relax_max_difference=a_,      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		):




   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)







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







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  torch.manual_seed(0		)

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

   _snake_case							:		Optional[Any]								=  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
   _snake_case							:		List[str]								=  pipe.get_label_ids(a_		)

   _snake_case							:		List[str]								=  pipe(a_,      generator=a_,      num_inference_steps=40,      output_type="""np"""		).images

   for word, image in zip(a_,      a_		):
     _snake_case							:		Dict								=  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:		Dict		):




   '''simple docstring'''



   _snake_case							:		Dict								=  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""		)
   _snake_case							:		List[Any]								=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
   pipe.to("""cuda"""		)

   _snake_case							:		Dict								=  ["""vase""", """umbrella"""]
   _snake_case							:		List[str]								=  pipe.get_label_ids(a_		)

   _snake_case							:		Tuple								=  torch.manual_seed(0		)
   _snake_case							:		Dict								=  pipe(a_,      generator=a_,      num_inference_steps=25,      output_type="""np"""		).images

   for word, image in zip(a_,      a_		):
     _snake_case							:		int								=  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 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

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

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

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				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',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
