

'''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,						)    ->							str:
					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						    F'''The configuration file of this scheduler: {scheduler} is outdated.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',						lowercase_,						standard_warn=lowercase_ )
						snake_case							=					dict(scheduler.config )
						snake_case							=					1
						snake_case							=					FrozenDict(lowercase_ )

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

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

				def 					_lowerCamelCase    (       self,						lowercase_ = "auto" )    ->							Optional[int]:
					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(lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					self.enable_attention_slicing(lowercase_ )

				@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,						)    ->							List[Any]:
					# 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=lowercase_,						image=lowercase_,						mask_image=lowercase_,						strength=lowercase_,						num_inference_steps=lowercase_,						guidance_scale=lowercase_,						negative_prompt=lowercase_,						num_images_per_prompt=lowercase_,						eta=lowercase_,						generator=lowercase_,						output_type=lowercase_,						return_dict=lowercase_,						callback=lowercase_,						)

				@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_,						)    ->							str:
					# 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=lowercase_,						image=lowercase_,						strength=lowercase_,						num_inference_steps=lowercase_,						guidance_scale=lowercase_,						negative_prompt=lowercase_,						num_images_per_prompt=lowercase_,						eta=lowercase_,						generator=lowercase_,						output_type=lowercase_,						return_dict=lowercase_,						callback=lowercase_,						callback_steps=lowercase_,						)






				@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,						)    ->							Dict:
					# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
					return StableDiffusionPipeline(**self.components )(
					    prompt=lowercase_,						height=lowercase_,						width=lowercase_,						num_inference_steps=lowercase_,						guidance_scale=lowercase_,						negative_prompt=lowercase_,						num_images_per_prompt=lowercase_,						eta=lowercase_,						generator=lowercase_,						latents=lowercase_,						output_type=lowercase_,						return_dict=lowercase_,						callback=lowercase_,						callback_steps=lowercase_,						)








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	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)





