




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		[tensor.shape for tensor in tensor_list]
							return all(shape == shapes[0] for shape in shapes[1:]							)


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





				'''simple docstring'''



				__a							: List[Any]										=StableDiffusionLatentUpscalePipeline
				__a							: Optional[int]										=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """height""",
				    """width""",
				    """cross_attention_kwargs""",
				    """negative_prompt_embeds""",
				    """prompt_embeds""",
				}
				__a							: Dict										=PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
				__a							: Tuple										=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__a							: List[str]										=frozenset(
				    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__a							: Union[str, Any]										=frozenset([]  )

				__a							: Optional[int]										=True



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		1
											lowerCAmelCase   =		4
											lowerCAmelCase   =		(16, 16)

											lowerCAmelCase   =		floats_tensor((batch_size, num_channels) + sizes       , rng=random.Random(0						)						).to(UpperCAmelCase_						)
											return image



				def       __snake_case		( self						):
											torch.manual_seed(0						)
											lowerCAmelCase   =		UNetaDConditionModel(
											    act_fn='''gelu'''       , attention_head_dim=8       , norm_num_groups=UpperCAmelCase_       , block_out_channels=[32, 32, 64, 64]       , time_cond_proj_dim=1_60       , conv_in_kernel=1       , conv_out_kernel=1       , cross_attention_dim=32       , down_block_types=(
											        '''KDownBlock2D''',
											        '''KCrossAttnDownBlock2D''',
											        '''KCrossAttnDownBlock2D''',
											        '''KCrossAttnDownBlock2D''',
											    )       , in_channels=8       , mid_block_type=UpperCAmelCase_       , only_cross_attention=UpperCAmelCase_       , out_channels=5       , resnet_time_scale_shift='''scale_shift'''       , time_embedding_type='''fourier'''       , timestep_post_act='''gelu'''       , up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')       , )
											lowerCAmelCase   =		AutoencoderKL(
											    block_out_channels=[32, 32, 64, 64]       , in_channels=3       , out_channels=3       , down_block_types=[
											        '''DownEncoderBlock2D''',
											        '''DownEncoderBlock2D''',
											        '''DownEncoderBlock2D''',
											        '''DownEncoderBlock2D''',
											    ]       , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       , latent_channels=4       , )
											lowerCAmelCase   =		EulerDiscreteScheduler(prediction_type='''sample'''						)
											lowerCAmelCase   =		CLIPTextConfig(
											    bos_token_id=0       , eos_token_id=2       , hidden_size=32       , intermediate_size=37       , layer_norm_eps=1E-0_5       , num_attention_heads=4       , num_hidden_layers=5       , pad_token_id=1       , vocab_size=10_00       , hidden_act='''quick_gelu'''       , projection_dim=5_12       , )
											lowerCAmelCase   =		CLIPTextModel(UpperCAmelCase_						)
											lowerCAmelCase   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

											lowerCAmelCase   =		{
											    '''unet''': model.eval(),
											    '''vae''': vae.eval(),
											    '''scheduler''': scheduler,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											}

											return 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   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': self.dummy_image.cpu(),
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''numpy''',
											}
											return inputs



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''cpu'''

											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		self.pipeline_class(**UpperCAmelCase_						)
											pipe.to(UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

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

											self.assertEqual(image.shape       , (1, 2_56, 2_56, 3)						)
											lowerCAmelCase   =		np.array(
											    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]						)
											lowerCAmelCase   =		np.abs(image_slice.flatten() - expected_slice						).max()
											self.assertLessEqual(UpperCAmelCase_       , 1E-3						)



				def       __snake_case		( self						):
											super().test_attention_slicing_forward_pass(expected_max_diff=7E-3						)



				def       __snake_case		( self						):
											super().test_cpu_offload_forward_pass(expected_max_diff=3E-3						)



				def       __snake_case		( self						):
											super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)



				def       __snake_case		( self						):
											super().test_inference_batch_single_identical(expected_max_diff=7E-3						)



				def       __snake_case		( self						):
											super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3						)



				def       __snake_case		( self						):
											super().test_save_load_local(expected_max_difference=3E-3						)



				def       __snake_case		( self						):
											super().test_save_load_optional_components(expected_max_difference=3E-3						)






				def       __snake_case		( self						):
											lowerCAmelCase   =		[
											    '''DDIMScheduler''',
											    '''DDPMScheduler''',
											    '''PNDMScheduler''',
											    '''HeunDiscreteScheduler''',
											    '''EulerAncestralDiscreteScheduler''',
											    '''KDPM2DiscreteScheduler''',
											    '''KDPM2AncestralDiscreteScheduler''',
											    '''DPMSolverSDEScheduler''',
											]
											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		self.pipeline_class(**UpperCAmelCase_						)

											# make sure that PNDM does not need warm-up
											pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase_						)

											pipe.to(UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)
											lowerCAmelCase   =		self.get_dummy_inputs(UpperCAmelCase_						)
											lowerCAmelCase   =		2

											lowerCAmelCase   =		[]
											for scheduler_enum in KarrasDiffusionSchedulers:
																		if scheduler_enum.name in skip_schedulers:
																									# no sigma schedulers are not supported
																									# no schedulers
																									continue

																		lowerCAmelCase   =		getattr(UpperCAmelCase_       , scheduler_enum.name						)
																		lowerCAmelCase   =		scheduler_cls.from_config(pipe.scheduler.config						)
																		lowerCAmelCase   =		pipe(**UpperCAmelCase_						)[0]
																		outputs.append(UpperCAmelCase_						)

											assert check_same_shape(UpperCAmelCase_						)




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





				'''simple docstring'''



				def       __snake_case		( self						):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



				def       __snake_case		( self						):
											lowerCAmelCase   =		torch.manual_seed(33						)

											lowerCAmelCase   =		StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''       , torch_dtype=torch.floataa						)
											pipe.to('''cuda'''						)

											lowerCAmelCase   =		StableDiffusionLatentUpscalePipeline.from_pretrained(
											    '''stabilityai/sd-x2-latent-upscaler'''       , torch_dtype=torch.floataa						)
											upscaler.to('''cuda'''						)

											lowerCAmelCase   =		'''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

											lowerCAmelCase   =		pipe(UpperCAmelCase_       , generator=UpperCAmelCase_       , output_type='''latent'''						).images

											lowerCAmelCase   =		upscaler(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , num_inference_steps=20       , guidance_scale=0       , generator=UpperCAmelCase_       , output_type='''np'''       , ).images[0]

											lowerCAmelCase   =		load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''						)
											assert np.abs((expected_image - image).mean()						) < 5E-2






				def       __snake_case		( self						):
											lowerCAmelCase   =		torch.manual_seed(33						)

											lowerCAmelCase   =		StableDiffusionLatentUpscalePipeline.from_pretrained(
											    '''stabilityai/sd-x2-latent-upscaler'''       , torch_dtype=torch.floataa						)
											upscaler.to('''cuda'''						)

											lowerCAmelCase   =		'''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''						)

											lowerCAmelCase   =		upscaler(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , num_inference_steps=20       , guidance_scale=0       , generator=UpperCAmelCase_       , output_type='''np'''       , ).images[0]

											lowerCAmelCase   =		load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''						)
											assert np.abs((expected_image - image).max()						) < 5E-2









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)



