import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = args.pruning_method
       lowerCAmelCase__					     = args.threshold

       lowerCAmelCase__					     = args.model_name_or_path.rstrip('/'     )
       lowerCAmelCase__					     = args.target_model_path

       print(F"""Load fine-pruned model from {model_name_or_path}"""     )
       lowerCAmelCase__					     = torch.load(os.path.join(lowerCAmelCase__     ,  'pytorch_model.bin'     )     )
       lowerCAmelCase__					     = {}

       for name, tensor in model.items():
              if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                     lowerCAmelCase__					     = tensor
                     print(F"""Copied layer {name}"""     )
              elif "classifier" in name or "qa_output" in name:
                     lowerCAmelCase__					     = tensor
                     print(F"""Copied layer {name}"""     )
              elif "bias" in name:
                     lowerCAmelCase__					     = tensor
                     print(F"""Copied layer {name}"""     )
              else:
                     if pruning_method == "magnitude":
                            lowerCAmelCase__					     = MagnitudeBinarizer.apply(inputs=lowerCAmelCase__     ,  threshold=lowerCAmelCase__     )
                            lowerCAmelCase__					     = tensor * mask
                            print(F"""Pruned layer {name}"""     )
                     elif pruning_method == "topK":
                            if "mask_scores" in name:
                                   continue
                            lowerCAmelCase__					     = name[:-6]
                            lowerCAmelCase__					     = model[F"""{prefix_}mask_scores"""]
                            lowerCAmelCase__					     = TopKBinarizer.apply(lowerCAmelCase__     ,  lowerCAmelCase__     )
                            lowerCAmelCase__					     = tensor * mask
                            print(F"""Pruned layer {name}"""     )
                     elif pruning_method == "sigmoied_threshold":
                            if "mask_scores" in name:
                                   continue
                            lowerCAmelCase__					     = name[:-6]
                            lowerCAmelCase__					     = model[F"""{prefix_}mask_scores"""]
                            lowerCAmelCase__					     = ThresholdBinarizer.apply(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                            lowerCAmelCase__					     = tensor * mask
                            print(F"""Pruned layer {name}"""     )
                     elif pruning_method == "l0":
                            if "mask_scores" in name:
                                   continue
                            lowerCAmelCase__					     = name[:-6]
                            lowerCAmelCase__					     = model[F"""{prefix_}mask_scores"""]
                            lowerCAmelCase__     , lowerCAmelCase__					     = -0.1, 1.1
                            lowerCAmelCase__					     = torch.sigmoid(lowerCAmelCase__     )
                            lowerCAmelCase__					     = s * (r - l) + l
                            lowerCAmelCase__					     = s_bar.clamp(min=0.0     ,  max=1.0     )
                            lowerCAmelCase__					     = tensor * mask
                            print(F"""Pruned layer {name}"""     )
                     else:
                            raise ValueError('Unknown pruning method'     )

       if target_model_path is None:
              lowerCAmelCase__					     = os.path.join(
                  os.path.dirname(lowerCAmelCase__     )     ,  F"""bertarized_{os.path.basename(lowerCAmelCase__     )}"""     )

       if not os.path.isdir(lowerCAmelCase__     ):
              shutil.copytree(lowerCAmelCase__     ,  lowerCAmelCase__     )
              print(F"""\nCreated folder {target_model_path}"""     )

       torch.save(lowerCAmelCase__     ,  os.path.join(lowerCAmelCase__     ,  'pytorch_model.bin'     )     )
       print('\nPruned model saved! See you later!'     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			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',
    )

    lowerCAmelCase__     						=			parser.parse_args()

    main(args)

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if not len(lowerCAmelCase__     ) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'     )

       lowerCAmelCase__     , lowerCAmelCase__					     = imgs[0].size
       lowerCAmelCase__					     = Image.new('RGB'     ,  size=(cols * w, rows * h)     )
       lowerCAmelCase__     , lowerCAmelCase__					     = grid.size

       for i, img in enumerate(lowerCAmelCase__     ):
              grid.paste(lowerCAmelCase__     ,  box=(i % cols * w, i // cols * h)     )
       return grid






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  rows=_rows     ,  cols=num_images_per_prompt // _rows     )
       return grid, images


lowerCAmelCase__     						=			parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__     						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__     						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__     						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__     						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase__     						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__     						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
    lowerCAmelCase__     						=			load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, 'unet', unet)
else:
    lowerCAmelCase__     						=			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__     						=			pipeline.to(unet.device)
lowerCAmelCase__						,		lowerCAmelCase__     						=			generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase__     						=			os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
    image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
