



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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							Any						)->	Dict:
    if not len(UpperCamelCase_						) == rows * cols:
        raise ValueError('''The specified number of rows and columns are not correct.'''						)

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

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int			,							UpperCamelCase_					:							Optional[int]="robotic cat with wings"			,							UpperCamelCase_					:							List[Any]=7.5			,							UpperCamelCase_					:							Optional[int]=5_0			,							UpperCamelCase_					:							List[Any]=1			,							UpperCamelCase_					:							Dict=4_2			,							)->	Tuple:
    A__					=       torch.Generator(pipeline.device						).manual_seed(UpperCamelCase_						)
    A__					=       pipeline(
        UpperCamelCase_			,							guidance_scale=UpperCamelCase_			,							num_inference_steps=UpperCamelCase_			,							generator=UpperCamelCase_			,							num_images_per_prompt=UpperCamelCase_			,							).images
    A__					=       int(math.sqrt(UpperCamelCase_						)						)
    A__					=       image_grid(UpperCamelCase_			,							rows=_rows			,							cols=num_images_per_prompt // _rows						)
    return grid, images


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

_lowercase    	=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_lowercase    	=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
   _lowercase    	=		load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, "unet", unet)
else:
   _lowercase    	=		unet.to(torch.device("cuda", args.cuda_id))
_lowercase    	=		pipeline.to(unet.device)
_lowercase	,	_lowercase    	=		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()))))
_lowercase    	=		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)))





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''std_conv''' if '''bit''' in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

    # load state_dict of original model
    A__					=       timm_model.state_dict()
    for key in state_dict.copy().keys():
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

    # create image processor
    A__					=       create_transform(**resolve_data_config({}			,							model=UpperCamelCase_						)						)
    A__					=       transform.transforms

    A__					=       {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    A__					=       BitImageProcessor(
        do_resize=UpperCamelCase_			,							size={'''shortest_edge''': timm_transforms[0].size}			,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,							do_center_crop=UpperCamelCase_			,							crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,							do_normalize=UpperCamelCase_			,							image_mean=timm_transforms[-1].mean.tolist()			,							image_std=timm_transforms[-1].std.tolist()			,							)

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

    # verify pixel values
    assert torch.allclose(UpperCamelCase_			,							UpperCamelCase_						)

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

    print('''Logits:'''			,							logits[0, :3]						)
    print('''Predicted class:'''			,							model.config.idalabel[logits.argmax(-1						).item()]						)
    A__					=       timm_model(UpperCamelCase_						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(UpperCamelCase_			,							outputs.logits			,							atol=1E-3						)
    print('''Looks ok!'''						)

    if pytorch_dump_folder_path is not None:
        Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"						)
        model.save_pretrained(UpperCamelCase_						)
        processor.save_pretrained(UpperCamelCase_						)

    if push_to_hub:
        print(f"Pushing model {model_name} and processor to the hub"						)
        model.push_to_hub(f"ybelkada/{model_name}"						)
        processor.push_to_hub(f"ybelkada/{model_name}"						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="resnetv2_50x1_bitm",
       type=str,
       help="Name of the BiT timm model you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub",
       action="store_true",
       help="Whether to push the model to the hub.",
   )

   _lowercase    	=		parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
