
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def  __lowerCamelCase					( _lowercase			)       ->      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  __lowerCamelCase					( _lowercase			)       ->      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  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      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  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      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__":
 _snake_case      = 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.'''
 )

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




def  __lowerCamelCase					( _lowercase = 50000000			)       ->      int:
     UpperCamelCase						    =					set()
     UpperCamelCase						    =					int((limit - 24) ** (1 / 2)			)

     UpperCamelCase						    =					set(range(3						,     prime_square_limit + 1						,     2			)			)
     primes.add(2			)
     for p in range(3						,     prime_square_limit + 1						,     2			):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p						,     prime_square_limit + 1						,     _lowercase			)			)			)

     for primea in primes:
          UpperCamelCase						    =					primea * primea
          for primea in primes:
               UpperCamelCase						    =					primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    UpperCamelCase						    =					primea * primea * primea * primea
                    UpperCamelCase						    =					square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(_lowercase			)

     return len(_lowercase			)


if __name__ == "__main__":
 print(F"{solution() = }")


