



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	Union[str, Any]:
    __UpperCAmelCase			 =SwinConfig(image_size=192      )

    if "base" in model_name:
        __UpperCAmelCase			 =6
        __UpperCAmelCase			 =128
        __UpperCAmelCase			 =(2, 2, 18, 2)
        __UpperCAmelCase			 =(4, 8, 16, 32)
    elif "large" in model_name:
        __UpperCAmelCase			 =12
        __UpperCAmelCase			 =192
        __UpperCAmelCase			 =(2, 2, 18, 2)
        __UpperCAmelCase			 =(6, 12, 24, 48)
    else:
        raise ValueError('''Model not supported, only supports base and large variants'''      )

    __UpperCAmelCase			 =window_size
    __UpperCAmelCase			 =embed_dim
    __UpperCAmelCase			 =depths
    __UpperCAmelCase			 =num_heads

    return config




def 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	List[str]:
    if "encoder.mask_token" in name:
        __UpperCAmelCase			 =name.replace('''encoder.mask_token'''				,   '''embeddings.mask_token'''      )
    if "encoder.patch_embed.proj" in name:
        __UpperCAmelCase			 =name.replace('''encoder.patch_embed.proj'''				,   '''embeddings.patch_embeddings.projection'''      )
    if "encoder.patch_embed.norm" in name:
        __UpperCAmelCase			 =name.replace('''encoder.patch_embed.norm'''				,   '''embeddings.norm'''      )
    if "attn.proj" in name:
        __UpperCAmelCase			 =name.replace('''attn.proj'''				,   '''attention.output.dense'''      )
    if "attn" in name:
        __UpperCAmelCase			 =name.replace('''attn'''				,   '''attention.self'''      )
    if "norm1" in name:
        __UpperCAmelCase			 =name.replace('''norm1'''				,   '''layernorm_before'''      )
    if "norm2" in name:
        __UpperCAmelCase			 =name.replace('''norm2'''				,   '''layernorm_after'''      )
    if "mlp.fc1" in name:
        __UpperCAmelCase			 =name.replace('''mlp.fc1'''				,   '''intermediate.dense'''      )
    if "mlp.fc2" in name:
        __UpperCAmelCase			 =name.replace('''mlp.fc2'''				,   '''output.dense'''      )

    if name == "encoder.norm.weight":
        __UpperCAmelCase			 ='''layernorm.weight'''
    if name == "encoder.norm.bias":
        __UpperCAmelCase			 ='''layernorm.bias'''

    if "decoder" in name:
        pass
    else:
        __UpperCAmelCase			 ='''swin.''' + name

    return name




def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__      )   ->	Dict:
    for key in orig_state_dict.copy().keys():
        __UpperCAmelCase			 =orig_state_dict.pop(_lowercase      )

        if "attn_mask" in key:
            pass
        elif "qkv" in key:
            __UpperCAmelCase			 =key.split('''.'''      )
            __UpperCAmelCase			 =int(key_split[2]      )
            __UpperCAmelCase			 =int(key_split[4]      )
            __UpperCAmelCase			 =model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

    return orig_state_dict




def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__				,   snake_case__				,   snake_case__      )   ->	List[str]:
    __UpperCAmelCase			 =torch.load(_lowercase				,   map_location='''cpu'''      )['''model''']

    __UpperCAmelCase			 =get_swin_config(_lowercase      )
    __UpperCAmelCase			 =SwinForMaskedImageModeling(_lowercase      )
    model.eval()

    __UpperCAmelCase			 =convert_state_dict(_lowercase				,   _lowercase      )
    model.load_state_dict(_lowercase      )

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

    __UpperCAmelCase			 =ViTImageProcessor(size={'''height''': 192, '''width''': 192}      )
    __UpperCAmelCase			 =Image.open(requests.get(_lowercase				,   stream=_lowercase      ).raw      )
    __UpperCAmelCase			 =image_processor(images=_lowercase				,   return_tensors='''pt'''      )

    with torch.no_grad():
        __UpperCAmelCase			 =model(**_lowercase      ).logits

    print(outputs.keys()      )
    print('''Looks ok!'''      )

    if pytorch_dump_folder_path is not None:
        print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""      )
        model.save_pretrained(_lowercase      )

        print(f"""Saving image processor to {pytorch_dump_folder_path}"""      )
        image_processor.save_pretrained(_lowercase      )

    if push_to_hub:
        print(f"""Pushing model and image processor for {model_name} to hub"""      )
        model.push_to_hub(f"""microsoft/{model_name}"""      )
        image_processor.push_to_hub(f"""microsoft/{model_name}"""      )


if __name__ == "__main__":
       UpperCamelCase_	   =			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='swin-base-simmim-window6-192',
           type=str,
           choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
           help='Name of the Swin SimMIM model you\'d like to convert.',
       )
       parser.add_argument(
           '--checkpoint_path',
           default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
           type=str,
           help='Path to the original PyTorch checkpoint (.pth file).',
       )
       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 or not to push the converted model to the 🤗 hub.'
       )

       UpperCamelCase_	   =			parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


