




'''simple docstring'''






from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class 	SCREAMING_SNAKE_CASE_		(							__lowerCAmelCase					):



 def __init__(	self ,		A ,		A ,		A ,		A ,		A ,		):






        '''simple docstring'''




        super().__init__()

        self.register_modules(vae=lowercase_ ,		text_encoder=lowercase_ ,		tokenizer=lowercase_ ,		unet=lowercase_ ,		scheduler=lowercase_			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        with torch.no_grad():
               lowercase	:		Optional[Any]    =   self.vae.encode(tfms.ToTensor()(lowercase_			).unsqueeze(0			).to(self.device			) * 2 - 1			)
               lowercase	:		Optional[int]    =   0.1_8_2_1_5 * latent.latent_dist.sample()
        return latent



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		str    =   (1 / 0.1_8_2_1_5) * latent
        with torch.no_grad():
               lowercase	:		Dict    =   self.vae.decode(lowercase_			).sample
        lowercase	:		Dict    =   (img / 2 + 0.5).clamp(0 ,		1			)
        lowercase	:		List[Any]    =   img.detach().cpu().permute(0 ,		2 ,		3 ,		1			).numpy()
        lowercase	:		str    =   (img * 255).round().astype('''uint8'''			)
        return Image.fromarray(img[0]			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.tokenizer(
            lowercase_ ,		padding='''max_length''' ,		max_length=self.tokenizer.model_max_length ,		truncation=lowercase_ ,		return_tensors='''pt''' ,		)

        lowercase	:		List[Any]    =   self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

        lowercase	:		str    =   self.tokenizer(
            '''''' ,		padding='''max_length''' ,		max_length=self.tokenizer.model_max_length ,		truncation=lowercase_ ,		return_tensors='''pt''' ,		)

        lowercase	:		Tuple    =   self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

        return torch.cat([uncond_embedding, text_embedding]			)





 def __call__(	self ,		A ,		A ,		A = 0.3 ,		A = 0.6 ,		A = 0.5 ,		A = 42 ,		A = 50 ,		A = 7.5 ,		):






        '''simple docstring'''




        lowercase	:		Tuple    =   steps - int(kmin * steps			)
        lowercase	:		int    =   steps - int(kmax * steps			)

        lowercase	:		List[str]    =   self.prep_text(lowercase_			)

        self.scheduler.set_timesteps(lowercase_			)

        lowercase,  lowercase	:		int    =   img.size
        lowercase	:		Dict    =   self.encode(lowercase_			)

        torch.manual_seed(lowercase_			)
        lowercase	:		List[str]    =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8) ,		).to(self.device			)

        lowercase	:		Tuple    =   self.scheduler.add_noise(
            lowercase_ ,		lowercase_ ,		timesteps=self.scheduler.timesteps[tmax] ,		)

        lowercase	:		List[str]    =   torch.cat([latents] * 2			)

        lowercase	:		str    =   self.scheduler.scale_model_input(lowercase_ ,		self.scheduler.timesteps[tmax]			)

        with torch.no_grad():
               lowercase	:		int    =   self.unet(
                   lowercase_ ,		self.scheduler.timesteps[tmax] ,		encoder_hidden_states=lowercase_ ,		).sample

        lowercase,  lowercase	:		Tuple    =   pred.chunk(2			)
        lowercase	:		Union[str, Any]    =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowercase	:		int    =   self.scheduler.step(lowercase_ ,		self.scheduler.timesteps[tmax] ,		lowercase_			).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
               if i > tmax:
                      if i < tmin:  # layout generation phase
                             lowercase	:		Dict    =   self.scheduler.add_noise(
                                 lowercase_ ,		lowercase_ ,		timesteps=lowercase_ ,		)

                             lowercase	:		Dict    =   (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             lowercase	:		Union[str, Any]    =   torch.cat([input] * 2			)

                      else:  # content generation phase
                             lowercase	:		List[Any]    =   torch.cat([latents] * 2			)

                      lowercase	:		List[Any]    =   self.scheduler.scale_model_input(lowercase_ ,		lowercase_			)

                      with torch.no_grad():
                             lowercase	:		List[Any]    =   self.unet(
                                 lowercase_ ,		lowercase_ ,		encoder_hidden_states=lowercase_ ,		).sample

                      lowercase,  lowercase	:		List[Any]    =   pred.chunk(2			)
                      lowercase	:		Any    =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowercase	:		Union[str, Any]    =   self.scheduler.step(lowercase_ ,		lowercase_ ,		lowercase_			).prev_sample

        return self.decode(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)





