


'''simple docstring'''




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    int:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 @property
 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  int						=  1
    _a					:  Union[str, Any]						=  3
    _a					:  Optional[Any]						=  (3_2, 3_2)

    _a					:  int						=  floats_tensor((batch_size, num_channels) + sizes		,      rng=random.Random(0      )      ).to(lowerCamelCase_      )
    return image




 @property
 def   __UpperCamelCase   (    self      )      ->    int:
    torch.manual_seed(0      )
    _a					:  Dict						=  UNetaDConditionModel(
        block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=4		,      out_channels=4		,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,      cross_attention_dim=3_2		,      )
    return model




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    torch.manual_seed(0      )
    _a					:  str						=  AutoencoderKL(
        block_out_channels=[3_2, 6_4]		,      in_channels=3		,      out_channels=3		,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,      latent_channels=4		,      )
    return model




 @property
 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    torch.manual_seed(0      )
    _a					:  Optional[int]						=  CLIPTextConfig(
        bos_token_id=0		,      eos_token_id=2		,      hidden_size=3_2		,      intermediate_size=3_7		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_0_0_0		,      )
    return CLIPTextModel(lowerCamelCase_      )




 @property
 def   __UpperCamelCase   (    self      )      ->    int:
    def extract(*lowerCamelCase_		,      **lowerCamelCase_      ):







       class 					a			:
        '''simple docstring'''




        def __init__(    self      )      ->    int:
           _a					:  List[str]						=  torch.ones([0]      )




        def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Optional[int]:
           self.pixel_values.to(lowerCamelCase_      )
           return self





       return Out()

    return extract




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Union[str, Any]						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
    _a					:  Tuple						=  self.dummy_cond_unet
    _a					:  str						=  DDIMScheduler(
        beta_start=0.00085		,      beta_end=0.012		,      beta_schedule='scaled_linear'		,      clip_sample=lowerCamelCase_		,      set_alpha_to_one=lowerCamelCase_		,      )

    _a					:  Dict						=  self.dummy_vae
    _a					:  str						=  self.dummy_text_encoder
    _a					:  Any						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

    # make sure here that pndm scheduler skips prk
    _a					:  Optional[Any]						=  StableDiffusionPipeline(
        unet=lowerCamelCase_		,      scheduler=lowerCamelCase_		,      vae=lowerCamelCase_		,      text_encoder=lowerCamelCase_		,      tokenizer=lowerCamelCase_		,      safety_checker=lowerCamelCase_		,      feature_extractor=self.dummy_extractor		,      )
    _a					:  List[str]						=  sd_pipe.to(lowerCamelCase_      )
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

    _a					:  List[str]						=  'A painting of a squirrel eating a burger'

    _a					:  List[Any]						=  torch.Generator(device=lowerCamelCase_      ).manual_seed(0      )
    _a					:  Dict						=  sd_pipe([prompt]		,      generator=lowerCamelCase_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='np'      )
    _a					:  List[Any]						=  output.images

    _a					:  str						=  torch.Generator(device=lowerCamelCase_      ).manual_seed(0      )
    _a					:  int						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='np'		,      return_dict=lowerCamelCase_		,      )[0]

    _a					:  Optional[Any]						=  image[0, -3:, -3:, -1]
    _a					:  List[Any]						=  image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    _a					:  Tuple						=  np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]      )

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  int						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
    _a					:  List[Any]						=  self.dummy_cond_unet
    _a					:  str						=  PNDMScheduler(skip_prk_steps=lowerCamelCase_      )
    _a					:  Optional[int]						=  self.dummy_vae
    _a					:  Union[str, Any]						=  self.dummy_text_encoder
    _a					:  Optional[int]						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

    # make sure here that pndm scheduler skips prk
    _a					:  List[str]						=  StableDiffusionPipeline(
        unet=lowerCamelCase_		,      scheduler=lowerCamelCase_		,      vae=lowerCamelCase_		,      text_encoder=lowerCamelCase_		,      tokenizer=lowerCamelCase_		,      safety_checker=lowerCamelCase_		,      feature_extractor=self.dummy_extractor		,      )
    _a					:  Optional[int]						=  sd_pipe.to(lowerCamelCase_      )
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

    _a					:  int						=  'A painting of a squirrel eating a burger'
    _a					:  Tuple						=  torch.Generator(device=lowerCamelCase_      ).manual_seed(0      )
    _a					:  Dict						=  sd_pipe([prompt]		,      generator=lowerCamelCase_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='np'      )

    _a					:  Optional[Any]						=  output.images

    _a					:  List[str]						=  torch.Generator(device=lowerCamelCase_      ).manual_seed(0      )
    _a					:  Union[str, Any]						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='np'		,      return_dict=lowerCamelCase_		,      )[0]

    _a					:  Optional[int]						=  image[0, -3:, -3:, -1]
    _a					:  Optional[Any]						=  image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    _a					:  List[str]						=  np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]      )

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  int						=  StableDiffusionPipeline.from_pretrained(
        'hf-internal-testing/tiny-stable-diffusion-lms-pipe'		,      safety_checker=lowerCamelCase_      )
    assert isinstance(lowerCamelCase_		,      lowerCamelCase_      )
    assert isinstance(pipe.scheduler		,      lowerCamelCase_      )
    assert pipe.safety_checker is None

    _a					:  Optional[Any]						=  pipe('example prompt'		,      num_inference_steps=2      ).images[0]
    assert image is not None

    # check that there's no error when saving a pipeline with one of the models being None
    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(lowerCamelCase_      )
       _a					:  List[str]						=  StableDiffusionPipeline.from_pretrained(lowerCamelCase_      )

    # sanity check that the pipeline still works
    assert pipe.safety_checker is None
    _a					:  Any						=  pipe('example prompt'		,      num_inference_steps=2      ).images[0]
    assert image is not None




 @unittest.skipIf(torch_device != 'cuda'		,      'This test requires a GPU'      )
 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Optional[int]						=  self.dummy_cond_unet
    _a					:  List[Any]						=  PNDMScheduler(skip_prk_steps=lowerCamelCase_      )
    _a					:  List[Any]						=  self.dummy_vae
    _a					:  int						=  self.dummy_text_encoder
    _a					:  List[str]						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

    # put models in fp16
    _a					:  int						=  unet.half()
    _a					:  str						=  vae.half()
    _a					:  int						=  bert.half()

    # make sure here that pndm scheduler skips prk
    _a					:  int						=  StableDiffusionPipeline(
        unet=lowerCamelCase_		,      scheduler=lowerCamelCase_		,      vae=lowerCamelCase_		,      text_encoder=lowerCamelCase_		,      tokenizer=lowerCamelCase_		,      safety_checker=lowerCamelCase_		,      feature_extractor=self.dummy_extractor		,      )
    _a					:  int						=  sd_pipe.to(lowerCamelCase_      )
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

    _a					:  Tuple						=  'A painting of a squirrel eating a burger'
    _a					:  Any						=  sd_pipe([prompt]		,      num_inference_steps=2		,      output_type='np'      ).images

    assert image.shape == (1, 6_4, 6_4, 3)







@nightly
@require_torch_gpu
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  List[str]						=  StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		,      safety_checker=lowerCamelCase_      )
    _a					:  Optional[Any]						=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
    _a					:  Optional[int]						=  sd_pipe.to(lowerCamelCase_      )
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

    _a					:  List[Any]						=  (
        'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
        ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
        ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
        ' children from bahnhof zoo, detailed '
    )
    _a					:  int						=  4_0_0_3_6_6_0_3_4_6
    _a					:  Optional[int]						=  7

    # without safety guidance (sld_guidance_scale = 0)
    _a					:  Union[str, Any]						=  torch.manual_seed(lowerCamelCase_      )
    _a					:  Tuple						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=lowerCamelCase_		,      num_inference_steps=5_0		,      output_type='np'		,      width=5_1_2		,      height=5_1_2		,      sld_guidance_scale=0		,      )

    _a					:  str						=  output.images
    _a					:  List[str]						=  image[0, -3:, -3:, -1]
    _a					:  Union[str, Any]						=  [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2

    # without safety guidance (strong configuration)
    _a					:  Optional[Any]						=  torch.manual_seed(lowerCamelCase_      )
    _a					:  Tuple						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=lowerCamelCase_		,      num_inference_steps=5_0		,      output_type='np'		,      width=5_1_2		,      height=5_1_2		,      sld_guidance_scale=2_0_0_0		,      sld_warmup_steps=7		,      sld_threshold=0.025		,      sld_momentum_scale=0.5		,      sld_mom_beta=0.7		,      )

    _a					:  Tuple						=  output.images
    _a					:  str						=  image[0, -3:, -3:, -1]
    _a					:  Tuple						=  [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  int						=  StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		,      safety_checker=lowerCamelCase_      )
    _a					:  List[str]						=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
    _a					:  List[Any]						=  sd_pipe.to(lowerCamelCase_      )
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

    _a					:  str						=  'padme amidala taking a bath artwork, safe for work, no nudity'
    _a					:  Tuple						=  2_7_3_4_9_7_1_7_5_5
    _a					:  str						=  7

    _a					:  Optional[Any]						=  torch.manual_seed(lowerCamelCase_      )
    _a					:  Tuple						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=lowerCamelCase_		,      num_inference_steps=5_0		,      output_type='np'		,      width=5_1_2		,      height=5_1_2		,      sld_guidance_scale=0		,      )

    _a					:  Optional[int]						=  output.images
    _a					:  str						=  image[0, -3:, -3:, -1]
    _a					:  List[str]						=  [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2

    _a					:  Tuple						=  torch.manual_seed(lowerCamelCase_      )
    _a					:  Optional[int]						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=lowerCamelCase_		,      num_inference_steps=5_0		,      output_type='np'		,      width=5_1_2		,      height=5_1_2		,      sld_guidance_scale=2_0_0_0		,      sld_warmup_steps=7		,      sld_threshold=0.025		,      sld_momentum_scale=0.5		,      sld_mom_beta=0.7		,      )

    _a					:  List[Any]						=  output.images
    _a					:  int						=  image[0, -3:, -3:, -1]
    _a					:  Optional[int]						=  [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  str						=  StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      )
    _a					:  Union[str, Any]						=  sd_pipe.to(lowerCamelCase_      )
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

    _a					:  Union[str, Any]						=  (
        'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
        ' leyendecker'
    )
    _a					:  List[str]						=  1_0_4_4_3_5_5_2_3_4
    _a					:  Union[str, Any]						=  1_2

    _a					:  Tuple						=  torch.manual_seed(lowerCamelCase_      )
    _a					:  List[Any]						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=lowerCamelCase_		,      num_inference_steps=5_0		,      output_type='np'		,      width=5_1_2		,      height=5_1_2		,      sld_guidance_scale=0		,      )

    _a					:  int						=  output.images
    _a					:  Optional[int]						=  image[0, -3:, -3:, -1]
    _a					:  str						=  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]      )

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-7

    _a					:  int						=  torch.manual_seed(lowerCamelCase_      )
    _a					:  Any						=  sd_pipe(
        [prompt]		,      generator=lowerCamelCase_		,      guidance_scale=lowerCamelCase_		,      num_inference_steps=5_0		,      output_type='np'		,      width=5_1_2		,      height=5_1_2		,      sld_guidance_scale=2_0_0_0		,      sld_warmup_steps=7		,      sld_threshold=0.025		,      sld_momentum_scale=0.5		,      sld_mom_beta=0.7		,      )

    _a					:  Dict						=  output.images
    _a					:  str						=  image[0, -3:, -3:, -1]
    _a					:  Union[str, Any]						=  np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]      )
    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






'''simple docstring'''




import argparse
import os
import shutil

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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  args.pruning_method
   _a					:  Optional[Any]						=  args.threshold

   _a					:  Union[str, Any]						=  args.model_name_or_path.rstrip('/'      )
   _a					:  Dict						=  args.target_model_path

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

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

   if target_model_path is None:
      _a					:  Tuple						=  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__":
 UpperCAmelCase_      :		str          =		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_      :		Optional[Any]          =		parser.parse_args()

 main(args)


