




'''simple docstring'''


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()
UpperCAmelCase_ 		=      logging.get_logger(__name__)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]			):
  __a			=					'huggingface/label-files'
  __a			=					'imagenet-1k-id2label.json'
  __a			=					json.load(open(hf_hub_download(a_				,					a_				,					repo_type='dataset'			)				,					'r'			)			)
  __a			=					{int(a_			): 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=a_				,					num_labels=1000				,					idalabel=a_				,					labelaid=a_				,					)

  return config


def 						SCREAMING_SNAKE_CASE       (      a_	:							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 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  __a			=					Image.open(requests.get(a_				,					stream=a_			).raw			)
  return im


@torch.no_grad()
def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]				,					a_	:							Optional[Any]				,					a_	:							List[str]=False			):
  __a			=					get_config(a_			)

  # load original model from timm
  __a			=					create_model(a_				,					pretrained=a_			)
  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(a_			)
    __a			=					val.squeeze() if 'head' in key else val

  # load HuggingFace model
  __a			=					BitForImageClassification(a_			)
  model.eval()
  model.load_state_dict(a_			)

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

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

  __a			=					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()				,					)

  __a			=					prepare_img()
  __a			=					transform(a_			).unsqueeze(0			)
  __a			=					processor(a_				,					return_tensors='pt'			).pixel_values

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

  # verify logits
  with torch.no_grad():
    __a			=					model(a_			)
    __a			=					outputs.logits

  print('Logits:'				,					logits[0, :3]			)
  print('Predicted class:'				,					model.config.idalabel[logits.argmax(-1			).item()]			)
  __a			=					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__":
  UpperCAmelCase_ 		=      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.",
  )

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






'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



