



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   __lowerCamelCase													=    SwinvaConfig()
   __lowerCamelCase													=    swinva_name.split('''_'''							)

   __lowerCamelCase													=    name_split[1]
   if "to" in name_split[3]:
      __lowerCamelCase													=    int(name_split[3][-3:]							)
   else:
      __lowerCamelCase													=    int(name_split[3]							)
   if "to" in name_split[2]:
      __lowerCamelCase													=    int(name_split[2][-2:]							)
   else:
      __lowerCamelCase													=    int(name_split[2][6:]							)

   if model_size == "tiny":
      __lowerCamelCase													=    96
      __lowerCamelCase													=    (2, 2, 6, 2)
      __lowerCamelCase													=    (3, 6, 12, 24)
   elif model_size == "small":
      __lowerCamelCase													=    96
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (3, 6, 12, 24)
   elif model_size == "base":
      __lowerCamelCase													=    1_28
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (4, 8, 16, 32)
   else:
      __lowerCamelCase													=    1_92
      __lowerCamelCase													=    (2, 2, 18, 2)
      __lowerCamelCase													=    (6, 12, 24, 48)

   if "to" in swinva_name:
      __lowerCamelCase													=    (12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      __lowerCamelCase													=    2_18_41
      __lowerCamelCase													=    '''huggingface/label-files'''
      __lowerCamelCase													=    '''imagenet-22k-id2label.json'''
      __lowerCamelCase													=    json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset'''							) ,'''r'''							)							)
      __lowerCamelCase													=    {int(_UpperCamelCase							): v for k, v in idalabel.items()}
      __lowerCamelCase													=    idalabel
      __lowerCamelCase													=    {v: k for k, v in idalabel.items()}

   else:
      __lowerCamelCase													=    10_00
      __lowerCamelCase													=    '''huggingface/label-files'''
      __lowerCamelCase													=    '''imagenet-1k-id2label.json'''
      __lowerCamelCase													=    json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset'''							) ,'''r'''							)							)
      __lowerCamelCase													=    {int(_UpperCamelCase							): v for k, v in idalabel.items()}
      __lowerCamelCase													=    idalabel
      __lowerCamelCase													=    {v: k for k, v in idalabel.items()}

   __lowerCamelCase													=    img_size
   __lowerCamelCase													=    num_classes
   __lowerCamelCase													=    embed_dim
   __lowerCamelCase													=    depths
   __lowerCamelCase													=    num_heads
   __lowerCamelCase													=    window_size

   return config





def 		a__			(		_UpperCamelCase			: Tuple							):
   if "patch_embed.proj" in name:
      __lowerCamelCase													=    name.replace('''patch_embed.proj''' ,'''embeddings.patch_embeddings.projection'''							)
   if "patch_embed.norm" in name:
      __lowerCamelCase													=    name.replace('''patch_embed.norm''' ,'''embeddings.norm'''							)
   if "layers" in name:
      __lowerCamelCase													=    '''encoder.''' + name
   if "attn.proj" in name:
      __lowerCamelCase													=    name.replace('''attn.proj''' ,'''attention.output.dense'''							)
   if "attn" in name:
      __lowerCamelCase													=    name.replace('''attn''' ,'''attention.self'''							)
   if "norm1" in name:
      __lowerCamelCase													=    name.replace('''norm1''' ,'''layernorm_before'''							)
   if "norm2" in name:
      __lowerCamelCase													=    name.replace('''norm2''' ,'''layernorm_after'''							)
   if "mlp.fc1" in name:
      __lowerCamelCase													=    name.replace('''mlp.fc1''' ,'''intermediate.dense'''							)
   if "mlp.fc2" in name:
      __lowerCamelCase													=    name.replace('''mlp.fc2''' ,'''output.dense'''							)
   if "q_bias" in name:
      __lowerCamelCase													=    name.replace('''q_bias''' ,'''query.bias'''							)
   if "k_bias" in name:
      __lowerCamelCase													=    name.replace('''k_bias''' ,'''key.bias'''							)
   if "v_bias" in name:
      __lowerCamelCase													=    name.replace('''v_bias''' ,'''value.bias'''							)
   if "cpb_mlp" in name:
      __lowerCamelCase													=    name.replace('''cpb_mlp''' ,'''continuous_position_bias_mlp'''							)
   if name == "norm.weight":
      __lowerCamelCase													=    '''layernorm.weight'''
   if name == "norm.bias":
      __lowerCamelCase													=    '''layernorm.bias'''

   if "head" in name:
      __lowerCamelCase													=    name.replace('''head''' ,'''classifier'''							)
   else:
      __lowerCamelCase													=    '''swinv2.''' + name

   return name





def 		a__			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Dict							):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase													=    orig_state_dict.pop(_UpperCamelCase							)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCamelCase													=    key.split('''.'''							)
         __lowerCamelCase													=    int(key_split[1]							)
         __lowerCamelCase													=    int(key_split[3]							)
         __lowerCamelCase													=    model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCamelCase													=    val[:dim, :]
            __lowerCamelCase													=    val[dim : dim * 2, :]
            __lowerCamelCase													=    val[-dim:, :]
         else:
            __lowerCamelCase													=    val[:dim]
            __lowerCamelCase													=    val[
                dim : dim * 2
            ]
            __lowerCamelCase													=    val[-dim:]
      else:
         __lowerCamelCase													=    val

   return orig_state_dict





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Optional[Any]							):
   __lowerCamelCase													=    timm.create_model(_UpperCamelCase ,pretrained=_UpperCamelCase							)
   timm_model.eval()

   __lowerCamelCase													=    get_swinva_config(_UpperCamelCase							)
   __lowerCamelCase													=    SwinvaForImageClassification(_UpperCamelCase							)
   model.eval()

   __lowerCamelCase													=    convert_state_dict(timm_model.state_dict() ,_UpperCamelCase							)
   model.load_state_dict(_UpperCamelCase							)

   __lowerCamelCase													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

   __lowerCamelCase													=    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' ,'''-'''							)							)							)
   __lowerCamelCase													=    Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase							).raw							)
   __lowerCamelCase													=    image_processor(images=_UpperCamelCase ,return_tensors='''pt'''							)

   __lowerCamelCase													=    timm_model(inputs['''pixel_values''']							)
   __lowerCamelCase													=    model(**_UpperCamelCase							).logits

   assert torch.allclose(_UpperCamelCase ,_UpperCamelCase ,atol=1e-3							)

   print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""							)
   model.save_pretrained(_UpperCamelCase							)

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
   image_processor.save_pretrained(_UpperCamelCase							)

   model.push_to_hub(
       repo_path_or_name=Path(_UpperCamelCase ,_UpperCamelCase							) ,organization='''nandwalritik''' ,commit_message='''Add model''' ,)


if __name__ == "__main__":
  a_			=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swinv2_name""",
      default="""swinv2_tiny_patch4_window8_256""",
      type=str,
      help="""Name of the Swinv2 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."""
  )

  a_			=					parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)











import torch

from diffusers import StableDiffusionPipeline


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

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

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






