





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def A_      (						A__							)    ->						Tuple:
       a__		:      Any					=							SwinConfig(image_size=192							)

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

       a__		:      List[Any]					=							window_size
       a__		:      str					=							embed_dim
       a__		:      Optional[int]					=							depths
       a__		:      Dict					=							num_heads

       return config



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

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

       if "decoder" in name:
              pass
       else:
              a__		:      Union[str, Any]					=							'swin.' + name

       return name



def A_      (						A__						, A__							)    ->						Any:
       for key in orig_state_dict.copy().keys():
              a__		:      Dict					=							orig_state_dict.pop(snake_case__							)

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

                     if "weight" in key:
                            a__		:      Optional[Any]					=							val[:dim, :]
                            a__		:      List[Any]					=							val[
                                dim : dim * 2, :
                            ]
                            a__		:      Optional[Any]					=							val[-dim:, :]
                     else:
                            a__		:      List[Any]					=							val[
                                :dim
                            ]
                            a__		:      Tuple					=							val[
                                dim : dim * 2
                            ]
                            a__		:      Optional[Any]					=							val[
                                -dim:
                            ]
              else:
                     a__		:      List[Any]					=							val

       return orig_state_dict



def A_      (						A__						, A__						, A__						, A__							)    ->						int:
       a__		:      List[Any]					=							torch.load(snake_case__						, map_location='cpu'							)['model']

       a__		:      Optional[Any]					=							get_swin_config(snake_case__							)
       a__		:      Union[str, Any]					=							SwinForMaskedImageModeling(snake_case__							)
       model.eval()

       a__		:      Any					=							convert_state_dict(snake_case__						, snake_case__							)
       model.load_state_dict(snake_case__							)

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

       a__		:      int					=							ViTImageProcessor(size={'height': 192, 'width': 192}							)
       a__		:      Union[str, Any]					=							Image.open(requests.get(snake_case__						, stream=snake_case__							).raw							)
       a__		:      Tuple					=							image_processor(images=snake_case__						, return_tensors='pt'							)

       with torch.no_grad():
              a__		:      int					=							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__":
    lowercase :			List[Any]     		=    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."""
    )

    lowercase :			List[str]     		=    parser.parse_args()
    convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




import unittest

from knapsack import knapsack as k
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   0
					lowercase_	    =   [0]
					lowercase_	    =   [0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)

					lowercase_	    =   [6_0]
					lowercase_	    =   [1_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)
	def 		_lowercase    (			self    :     int			)			->	str:
					lowercase_	    =   3
					lowercase_	    =   [1, 2, 3]
					lowercase_	    =   [3, 2, 1]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				5			)




	def 		_lowercase    (			self    :     str			)			->	Tuple:
					lowercase_	    =   5_0
					lowercase_	    =   [6_0, 1_0_0, 1_2_0]
					lowercase_	    =   [1_0, 2_0, 3_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				2_2_0			)


if __name__ == "__main__":
						unittest.main()




