

'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase_						   =			logging.get_logger(__name__)  # pylint: disable=invalid-name







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   ['''safety_checker''', '''feature_extractor''']

				def __init__(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	lowercase_ = True,	)						->       Tuple:
					super().__init__()
					if hasattr(scheduler.config,	'steps_offset'   ) and scheduler.config.steps_offset != 1:
						snake_case							 =				(
						    F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
						    F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
						    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
						    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
						    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
						    ' file'
						)
						deprecate('steps_offset!=1',	'1.0.0',	A_,	standard_warn=A_   )
						snake_case							 =				dict(scheduler.config   )
						snake_case							 =				1
						snake_case							 =				FrozenDict(A_   )

					self.register_modules(
					    vae=A_,	text_encoder=A_,	tokenizer=A_,	unet=A_,	scheduler=A_,	safety_checker=A_,	feature_extractor=A_,	)
					self.register_to_config(requires_safety_checker=A_   )

				@property
				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					return {k: getattr(self,	A_   ) for k in self.config.keys() if not k.startswith('_'   )}

				def 				_lowerCamelCase						(					self,	lowercase_ = "auto"   )						->       List[str]:
					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						snake_case							 =				self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(A_   )

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					self.enable_attention_slicing(A_   )

				@torch.no_grad()
				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_ = 0.8,	lowercase_ = 50,	lowercase_ = 7.5,	lowercase_ = None,	lowercase_ = 1,	lowercase_ = 0.0,	lowercase_ = None,	lowercase_ = "pil",	lowercase_ = True,	lowercase_ = None,	lowercase_ = 1,	)						->       str:
					# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
					return StableDiffusionInpaintPipelineLegacy(**self.components   )(
					    prompt=A_,	image=A_,	mask_image=A_,	strength=A_,	num_inference_steps=A_,	guidance_scale=A_,	negative_prompt=A_,	num_images_per_prompt=A_,	eta=A_,	generator=A_,	output_type=A_,	return_dict=A_,	callback=A_,	)

				@torch.no_grad()
				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_ = 0.8,	lowercase_ = 50,	lowercase_ = 7.5,	lowercase_ = None,	lowercase_ = 1,	lowercase_ = 0.0,	lowercase_ = None,	lowercase_ = "pil",	lowercase_ = True,	lowercase_ = None,	lowercase_ = 1,	**lowercase_,	)						->       int:
					# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
					return StableDiffusionImgaImgPipeline(**self.components   )(
					    prompt=A_,	image=A_,	strength=A_,	num_inference_steps=A_,	guidance_scale=A_,	negative_prompt=A_,	num_images_per_prompt=A_,	eta=A_,	generator=A_,	output_type=A_,	return_dict=A_,	callback=A_,	callback_steps=A_,	)






				@torch.no_grad()
				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = 512,	lowercase_ = 512,	lowercase_ = 50,	lowercase_ = 7.5,	lowercase_ = None,	lowercase_ = 1,	lowercase_ = 0.0,	lowercase_ = None,	lowercase_ = None,	lowercase_ = "pil",	lowercase_ = True,	lowercase_ = None,	lowercase_ = 1,	)						->       Optional[int]:
					# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
					return StableDiffusionPipeline(**self.components   )(
					    prompt=A_,	height=A_,	width=A_,	num_inference_steps=A_,	guidance_scale=A_,	negative_prompt=A_,	num_images_per_prompt=A_,	eta=A_,	generator=A_,	latents=A_,	output_type=A_,	return_dict=A_,	callback=A_,	callback_steps=A_,	)





'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def     __magic_name__       ( A							)			->		Dict:
	snake_case							 =				args.serialization_dir
	snake_case							 =				args.pruning_method
	snake_case							 =				args.threshold

	snake_case							 =				torch.load(os.path.join(A					,   'pytorch_model.bin'							)					,   map_location='cpu'							)

	snake_case							 =				0  # Number of remaining (not pruned) params in the encoder
	snake_case							 =				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":
				snake_case							 =				TopKBinarizer.apply(A					,   A							).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							 =				ThresholdBinarizer.apply(A					,   A					,   A							).sum().item()
			elif pruning_method == "l0":
				snake_case , snake_case							 =				-0.1, 1.1
				snake_case							 =				torch.sigmoid(A							)
				snake_case							 =				s * (r - l) + l
				snake_case							 =				s_bar.clamp(min=0.0					,   max=1.0							)
				snake_case							 =				(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(A							)							)
		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__":
						lowerCAmelCase_						   =			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",
						)

						lowerCAmelCase_						   =			parser.parse_args()

						main(args)





