




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Dict			 =					SwinConfig(image_size=192    )

       if "base" in model_name:
              lowerCamelCase   :      int			 =					6
              lowerCamelCase   :      str			 =					128
              lowerCamelCase   :      int			 =					(2, 2, 18, 2)
              lowerCamelCase   :      Optional[int]			 =					(4, 8, 16, 32)
       elif "large" in model_name:
              lowerCamelCase   :      Union[str, Any]			 =					12
              lowerCamelCase   :      List[Any]			 =					192
              lowerCamelCase   :      int			 =					(2, 2, 18, 2)
              lowerCamelCase   :      Optional[int]			 =					(6, 12, 24, 48)
       else:
              raise ValueError("Model not supported, only supports base and large variants"    )

       lowerCamelCase   :      List[Any]			 =					window_size
       lowerCamelCase   :      List[Any]			 =					embed_dim
       lowerCamelCase   :      Tuple			 =					depths
       lowerCamelCase   :      Any			 =					num_heads

       return config

def      A      (      _SCREAMING_SNAKE_CASE    )	-> str:
       if "encoder.mask_token" in name:
              lowerCamelCase   :      Optional[Any]			 =					name.replace("encoder.mask_token" ,"embeddings.mask_token"    )
       if "encoder.patch_embed.proj" in name:
              lowerCamelCase   :      Optional[int]			 =					name.replace("encoder.patch_embed.proj" ,"embeddings.patch_embeddings.projection"    )
       if "encoder.patch_embed.norm" in name:
              lowerCamelCase   :      int			 =					name.replace("encoder.patch_embed.norm" ,"embeddings.norm"    )
       if "attn.proj" in name:
              lowerCamelCase   :      Dict			 =					name.replace("attn.proj" ,"attention.output.dense"    )
       if "attn" in name:
              lowerCamelCase   :      Tuple			 =					name.replace("attn" ,"attention.self"    )
       if "norm1" in name:
              lowerCamelCase   :      Any			 =					name.replace("norm1" ,"layernorm_before"    )
       if "norm2" in name:
              lowerCamelCase   :      List[str]			 =					name.replace("norm2" ,"layernorm_after"    )
       if "mlp.fc1" in name:
              lowerCamelCase   :      int			 =					name.replace("mlp.fc1" ,"intermediate.dense"    )
       if "mlp.fc2" in name:
              lowerCamelCase   :      Optional[Any]			 =					name.replace("mlp.fc2" ,"output.dense"    )

       if name == "encoder.norm.weight":
              lowerCamelCase   :      Optional[int]			 =					"layernorm.weight"
       if name == "encoder.norm.bias":
              lowerCamelCase   :      Any			 =					"layernorm.bias"

       if "decoder" in name:
              pass
       else:
              lowerCamelCase   :      Optional[int]			 =					"swin." + name

       return name

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Union[str, Any]:
       for key in orig_state_dict.copy().keys():
              lowerCamelCase   :      List[Any]			 =					orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

              if "attn_mask" in key:
                     pass
              elif "qkv" in key:
                     lowerCamelCase   :      Optional[Any]			 =					key.split("."    )
                     lowerCamelCase   :      Union[str, Any]			 =					int(key_split[2]    )
                     lowerCamelCase   :      Any			 =					int(key_split[4]    )
                     lowerCamelCase   :      Any			 =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                     if "weight" in key:
                            lowerCamelCase   :      List[str]			 =					val[:dim, :]
                            lowerCamelCase   :      List[str]			 =					val[
                                dim : dim * 2, :
                            ]
                            lowerCamelCase   :      List[str]			 =					val[-dim:, :]
                     else:
                            lowerCamelCase   :      Tuple			 =					val[
                                :dim
                            ]
                            lowerCamelCase   :      List[Any]			 =					val[
                                dim : dim * 2
                            ]
                            lowerCamelCase   :      Dict			 =					val[
                                -dim:
                            ]
              else:
                     lowerCamelCase   :      List[str]			 =					val

       return orig_state_dict

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Dict			 =					torch.load(_SCREAMING_SNAKE_CASE ,map_location="cpu"    )["model"]

       lowerCamelCase   :      Dict			 =					get_swin_config(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[Any]			 =					SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE    )
       model.eval()

       lowerCamelCase   :      Optional[int]			 =					convert_state_dict(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Any			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"

       lowerCamelCase   :      str			 =					ViTImageProcessor(size={"height": 192, "width": 192}    )
       lowerCamelCase   :      Optional[Any]			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       lowerCamelCase   :      Tuple			 =					image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    )

       with torch.no_grad():
              lowerCamelCase   :      List[str]			 =					model(**_SCREAMING_SNAKE_CASE    ).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(_SCREAMING_SNAKE_CASE    )

              print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
              image_processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

       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__":
       SCREAMING_SNAKE_CASE__       :      str  =   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.'
       )

       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












import math

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * power_factor

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






