
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort

class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    # TODO: is there an appropriate internal test set?
    _a		:						List[str]=				"ssube/stable-diffusion-x4-upscaler-onnx"
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=0      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      floats_tensor((1, 3, 128, 128)		,rng=random.Random(snake_case      )      )
           lowercase							:     str       	=      torch.manual_seed(snake_case      )
           lowercase							:     Dict       	=      {
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Tuple       	=      self.get_dummy_inputs()
           lowercase							:     List[str]       	=      pipe(**snake_case      ).images
           lowercase							:     List[str]       	=      image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 512, 512, 3)
           lowercase							:     List[Any]       	=      np.array(
               [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]      )
           assert np.abs(image_slice - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     Optional[int]       	=      PNDMScheduler.from_config(pipe.scheduler.config		,skip_prk_steps=snake_case      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Union[str, Any]       	=      self.get_dummy_inputs()
           lowercase							:     Tuple       	=      pipe(**snake_case      ).images
           lowercase							:     List[Any]       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     List[str]       	=      np.array(
               [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]      )
           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     Dict       	=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

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

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     Any       	=      np.array(
               [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     str       	=      EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     List[Any]       	=      self.get_dummy_inputs()
           lowercase							:     Dict       	=      pipe(**snake_case      ).images
           lowercase							:     Any       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     Optional[int]       	=      np.array(
               [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]      )
           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider"""      )
           lowercase							:     List[Any]       	=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     Tuple       	=      self.get_dummy_inputs()
           lowercase							:     Optional[int]       	=      pipe(**snake_case      ).images
           lowercase							:     Dict       	=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase							:     str       	=      np.array(
               [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]      )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case		(      unittest.TestCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      ort.SessionOptions()
           lowercase							:     Tuple       	=      False
           return options
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           lowercase							:     Dict       	=      init_image.resize((128, 128)      )
           # using the PNDM scheduler by default
           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     List[str]       	=      """A fantasy landscape, trending on artstation"""

           lowercase							:     str       	=      torch.manual_seed(0      )
           lowercase							:     Any       	=      pipe(
               prompt=snake_case		,image=snake_case		,guidance_scale=7.5		,num_inference_steps=10		,generator=snake_case		,output_type="""np"""		,)
           lowercase							:     Optional[Any]       	=      output.images
           lowercase							:     str       	=      images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           lowercase							:     Optional[int]       	=      np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           lowercase							:     Dict       	=      init_image.resize((128, 128)      )
           lowercase							:     Union[str, Any]       	=      LMSDiscreteScheduler.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,subfolder="""scheduler"""      )
           lowercase							:     List[Any]       	=      OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,scheduler=snake_case		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=snake_case      )

           lowercase							:     int       	=      """A fantasy landscape, trending on artstation"""

           lowercase							:     Tuple       	=      torch.manual_seed(0      )
           lowercase							:     Any       	=      pipe(
               prompt=snake_case		,image=snake_case		,guidance_scale=7.5		,num_inference_steps=20		,generator=snake_case		,output_type="""np"""		,)
           lowercase							:     str       	=      output.images
           lowercase							:     int       	=      images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           lowercase							:     Tuple       	=      np.array(
               [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


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)
