






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()
a_							=			logging.get_logger(__name__)


def 				_a     (   UpperCamelCase_    : Dict      )       ->      List[Any]:
						"""simple docstring"""







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

						lowerCAmelCase__       					=  "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"
						lowerCAmelCase__       					=  BitConfig(
						    conv_layer=A__			,      num_labels=1_000			,      idalabel=A__			,      labelaid=A__			,      )

						return config


def 				_a     (   UpperCamelCase_    : Dict      )       ->      Optional[Any]:
						"""simple docstring"""







						if "stem.conv" in name:
												lowerCAmelCase__       					=  name.replace("stem.conv"			,      "bit.embedder.convolution"      )
						if "blocks" in name:
												lowerCAmelCase__       					=  name.replace("blocks"			,      "layers"      )
						if "head.fc" in name:
												lowerCAmelCase__       					=  name.replace("head.fc"			,      "classifier.1"      )
						if name.startswith("norm"      ):
												lowerCAmelCase__       					=  "bit." + name
						if "bit" not in name and "classifier" not in name:
												lowerCAmelCase__       					=  "bit.encoder." + name

						return name


def 				_a     (   )       ->      Any:
						"""simple docstring"""







						lowerCAmelCase__       					=  "http://images.cocodataset.org/val2017/000000039769.jpg"
						lowerCAmelCase__       					=  Image.open(requests.get(A__			,      stream=A__      ).raw      )
						return im


@torch.no_grad()
def 				_a     (   UpperCamelCase_    : Tuple			,      UpperCamelCase_    : Optional[Any]			,      UpperCamelCase_    : Dict=False      )       ->      Dict:
						"""simple docstring"""







						lowerCAmelCase__       					=  get_config(A__      )

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

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

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

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

						lowerCAmelCase__       					=  {
						    "bilinear": PILImageResampling.BILINEAR,
						    "bicubic": PILImageResampling.BICUBIC,
						    "nearest": PILImageResampling.NEAREST,
						}

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

						lowerCAmelCase__       					=  prepare_img()
						lowerCAmelCase__       					=  transform(A__      ).unsqueeze(0      )
						lowerCAmelCase__       					=  processor(A__			,      return_tensors="pt"      ).pixel_values

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

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

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

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

						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__":
	a_							=			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.''',
	)

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









'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 parser.add_argument(
     '''--txt2img_unclip''',
     default='''kakaobrain/karlo-v1-alpha''',
     type=str,
     required=False,
     help='''The pretrained txt2img unclip.''',
 )

 lowerCAmelCase__											=	parser.parse_args()

 lowerCAmelCase__											=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 lowerCAmelCase__											=	CLIPImageProcessor()
 lowerCAmelCase__											=	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

 lowerCAmelCase__											=	UnCLIPImageVariationPipeline(
     decoder=txtaimg.decoder,
     text_encoder=txtaimg.text_encoder,
     tokenizer=txtaimg.tokenizer,
     text_proj=txtaimg.text_proj,
     feature_extractor=feature_extractor,
     image_encoder=image_encoder,
     super_res_first=txtaimg.super_res_first,
     super_res_last=txtaimg.super_res_last,
     decoder_scheduler=txtaimg.decoder_scheduler,
     super_res_scheduler=txtaimg.super_res_scheduler,
 )

 imgaimg.save_pretrained(args.dump_path)

