




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = RePaintPipeline
					_lowerCamelCase		  = IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					_lowerCamelCase		  = PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					_lowerCamelCase		  = IMAGE_INPAINTING_BATCH_PARAMS
					_lowerCamelCase		  = False

					def 	lowercase_							(				self					):







						'''simple docstring'''




						torch.manual_seed(0					)
						torch.manual_seed(0					)
						lowerCamelCase_							:		str							   =							UNetaDModel(
						    block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=3 ,       out_channels=3 ,       down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,       up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,       )
						lowerCamelCase_							:		Optional[Any]							   =							RePaintScheduler()
						lowerCamelCase_							:		List[str]							   =							{'unet': unet, 'scheduler': scheduler}
						return components

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase=0					):







						'''simple docstring'''




						if str(__lowercase					).startswith('mps'					):
							lowerCamelCase_							:		str							   =							torch.manual_seed(__lowercase					)
						else:
							lowerCamelCase_							:		Union[str, Any]							   =							torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
						lowerCamelCase_							:		Dict							   =							np.random.RandomState(__lowercase					).standard_normal((1, 3, 32, 32)					)
						lowerCamelCase_							:		Union[str, Any]							   =							torch.from_numpy(__lowercase					).to(device=__lowercase ,       dtype=torch.floataa					)
						lowerCamelCase_							:		int							   =							(image > 0).to(device=__lowercase ,       dtype=torch.floataa					)
						lowerCamelCase_							:		List[str]							   =							{
						    'image': image,
						    'mask_image': mask,
						    'generator': generator,
						    'num_inference_steps': 5,
						    'eta': 0.0,
						    'jump_length': 2,
						    'jump_n_sample': 2,
						    'output_type': 'numpy',
						}
						return inputs

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_							:		int							   =							self.get_dummy_components()
						lowerCamelCase_							:		Dict							   =							RePaintPipeline(**__lowercase					)
						lowerCamelCase_							:		Dict							   =							sd_pipe.to(__lowercase					)
						sd_pipe.set_progress_bar_config(disable=__lowercase					)

						lowerCamelCase_							:		Tuple							   =							self.get_dummy_inputs(__lowercase					)
						lowerCamelCase_							:		Dict							   =							sd_pipe(**__lowercase					).images
						lowerCamelCase_							:		List[Any]							   =							image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						lowerCamelCase_							:		Any							   =							np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

					@skip_mps
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return super().test_save_load_local()

					@unittest.skip('non-deterministic pipeline'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return super().test_inference_batch_single_identical()

					@skip_mps
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return super().test_dict_tuple_outputs_equivalent()

					@skip_mps
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return super().test_save_load_optional_components()





					@skip_mps
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def 	lowercase_							(				self					):







						'''simple docstring'''




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





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
						    'repaint/celeba_hq_256.png'					)
						lowerCamelCase_							:		str							   =							load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'					)
						lowerCamelCase_							:		Any							   =							load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
						    'repaint/celeba_hq_256_result.npy'					)

						lowerCamelCase_							:		Tuple							   =							'google/ddpm-ema-celebahq-256'
						lowerCamelCase_							:		Dict							   =							UNetaDModel.from_pretrained(__lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							RePaintScheduler.from_pretrained(__lowercase					)

						lowerCamelCase_							:		Tuple							   =							RePaintPipeline(unet=__lowercase ,       scheduler=__lowercase					).to(__lowercase					)
						repaint.set_progress_bar_config(disable=__lowercase					)
						repaint.enable_attention_slicing()

						lowerCamelCase_							:		Optional[int]							   =							torch.manual_seed(0					)
						lowerCamelCase_							:		Optional[int]							   =							repaint(
						    __lowercase ,       __lowercase ,       num_inference_steps=250 ,       eta=0.0 ,       jump_length=10 ,       jump_n_sample=10 ,       generator=__lowercase ,       output_type='np' ,       )
						lowerCamelCase_							:		Dict							   =							output.images[0]

						assert image.shape == (256, 256, 3)
						assert np.abs(expected_image - image					).mean() < 1E-2



'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       args.serialization_dir
      SCREAMING_SNAKE_CASE_      :			List[str]     =       args.pruning_method
      SCREAMING_SNAKE_CASE_      :			str     =       args.threshold

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.load(os.path.join(SCREAMING_SNAKE_CASE_  ,  "pytorch_model.bin"    )  ,  map_location="cpu"    )

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       0  # Number of remaining (not pruned) params in the encoder
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       0  # Number of params in the encoder

      print("name".ljust(6_0  ,  " "    )  ,  "Remaining Weights %"  ,  "Remaining Weight"    )
      for name, param in st.items():
            if "encoder" not in name:
                  continue

            if "mask_scores" in name:
                  if pruning_method == "topK":
                        SCREAMING_SNAKE_CASE_      :			int     =       TopKBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        SCREAMING_SNAKE_CASE_      :			Tuple     =       ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "l0":
                        SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       -0.1, 1.1
                        SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.sigmoid(SCREAMING_SNAKE_CASE_    )
                        SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       s * (r - l) + l
                        SCREAMING_SNAKE_CASE_      :			str     =       s_bar.clamp(min=0.0  ,  max=1.0    )
                        SCREAMING_SNAKE_CASE_      :			Dict     =       (mask > 0.0).sum().item()
                  else:
                        raise ValueError("Unknown pruning method"    )
                  remaining_count += mask_ones
                  print(name.ljust(6_0  ,  " "    )  ,  str(round(1_0_0 * mask_ones / param.numel()  ,  3    )    ).ljust(2_0  ,  " "    )  ,  str(SCREAMING_SNAKE_CASE_    )    )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(""    )
      print("Remaining Weights (global) %: "  ,  1_0_0 * remaining_count / encoder_count    )


if __name__ == "__main__":
      snake_case_       =       argparse.ArgumentParser()

      parser.add_argument(
          '--pruning_method',
          choices=['l0', 'topK', 'sigmoied_threshold'],
          type=str,
          required=True,
          help=(
              'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
              ' pruning)'
          ),
      )
      parser.add_argument(
          '--threshold',
          type=float,
          required=False,
          help=(
              'For `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(
          '--serialization_dir',
          type=str,
          required=True,
          help='Folder containing the model that was previously fine-pruned',
      )

      snake_case_       =       parser.parse_args()

      main(args)

