




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
      _UpperCAmelCase      =			SwinConfig(image_size=1_9_2       )

      if "base" in model_name:
            _UpperCAmelCase      =			6
            _UpperCAmelCase      =			1_2_8
            _UpperCAmelCase      =			(2, 2, 1_8, 2)
            _UpperCAmelCase      =			(4, 8, 1_6, 3_2)
      elif "large" in model_name:
            _UpperCAmelCase      =			1_2
            _UpperCAmelCase      =			1_9_2
            _UpperCAmelCase      =			(2, 2, 1_8, 2)
            _UpperCAmelCase      =			(6, 1_2, 2_4, 4_8)
      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       )						->				Any:
      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       )						->				Optional[Any]:
      for key in orig_state_dict.copy().keys():
            _UpperCAmelCase      =			orig_state_dict.pop(snake_case       )

            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       )						->				str:
      _UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )["""model"""]

      _UpperCAmelCase      =			get_swin_config(snake_case       )
      _UpperCAmelCase      =			SwinForMaskedImageModeling(snake_case       )
      model.eval()

      _UpperCAmelCase      =			convert_state_dict(snake_case			,     snake_case       )
      model.load_state_dict(snake_case       )

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

      _UpperCAmelCase      =			ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2}       )
      _UpperCAmelCase      =			Image.open(requests.get(snake_case			,     stream=snake_case       ).raw       )
      _UpperCAmelCase      =			image_processor(images=snake_case			,     return_tensors="""pt"""       )

      with torch.no_grad():
            _UpperCAmelCase      =			model(**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(snake_case       )

            print(f"Saving image processor to {pytorch_dump_folder_path}"       )
            image_processor.save_pretrained(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__":
    a													=			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."
    )

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





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(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()