


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				SwinConfig(image_size=192 )

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

      snake_case_	=				window_size
      snake_case_	=				embed_dim
      snake_case_	=				depths
      snake_case_	=				num_heads

      return config






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      if "encoder.mask_token" in name:
            snake_case_	=				name.replace('''encoder.mask_token''' , '''embeddings.mask_token''' )
      if "encoder.patch_embed.proj" in name:
            snake_case_	=				name.replace('''encoder.patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
      if "encoder.patch_embed.norm" in name:
            snake_case_	=				name.replace('''encoder.patch_embed.norm''' , '''embeddings.norm''' )
      if "attn.proj" in name:
            snake_case_	=				name.replace('''attn.proj''' , '''attention.output.dense''' )
      if "attn" in name:
            snake_case_	=				name.replace('''attn''' , '''attention.self''' )
      if "norm1" in name:
            snake_case_	=				name.replace('''norm1''' , '''layernorm_before''' )
      if "norm2" in name:
            snake_case_	=				name.replace('''norm2''' , '''layernorm_after''' )
      if "mlp.fc1" in name:
            snake_case_	=				name.replace('''mlp.fc1''' , '''intermediate.dense''' )
      if "mlp.fc2" in name:
            snake_case_	=				name.replace('''mlp.fc2''' , '''output.dense''' )

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

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

      return name






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            snake_case_	=				orig_state_dict.pop(SCREAMING_SNAKE_CASE )

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

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

      return orig_state_dict






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''model''']

      snake_case_	=				get_swin_config(SCREAMING_SNAKE_CASE )
      snake_case_	=				SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE )
      model.eval()

      snake_case_	=				convert_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      model.load_state_dict(SCREAMING_SNAKE_CASE )

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

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

      with torch.no_grad():
            snake_case_	=				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__":
   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)



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  list[int]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )

      # Initialize Result
      snake_case_	=				[]

      # Traverse through all denomination
      for denomination in reversed(SCREAMING_SNAKE_CASE ):
            # Find denominations
            while int(SCREAMING_SNAKE_CASE ) >= int(SCREAMING_SNAKE_CASE ):
                  total_value -= int(SCREAMING_SNAKE_CASE )
                  answer.append(SCREAMING_SNAKE_CASE )  # Append the "answers" array

      return answer


# Driver Code
if __name__ == "__main__":
   UpperCAmelCase      =   []
   UpperCAmelCase      =   """0"""

   if (
       input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
       == "y"
   ):
      UpperCAmelCase      =   int(input("""Enter the number of denominations you want to add: """).strip())

      for i in range(0, n):
         denominations.append(int(input(f'''Denomination {i}: ''').strip()))
      UpperCAmelCase      =   input("""Enter the change you want to make in Indian Currency: """).strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase      =   [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase      =   input("""Enter the change you want to make: """).strip()

   if int(value) == 0 or int(value) < 0:
      print("""The total value cannot be zero or negative.""")

   else:
      print(f'''Following is minimal change for {value}: ''')
      UpperCAmelCase      =   find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=""" """)