

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''simple docstring'''





     lowercase__    :   Optional[int]      =       SwinConfig(image_size=192			)

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

     lowercase__    :   Union[str, Any]      =       window_size
     lowercase__    :   Dict      =       embed_dim
     lowercase__    :   List[str]      =       depths
     lowercase__    :   List[str]      =       num_heads

     return config

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





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

     if name == "encoder.norm.weight":
          lowercase__    :   Dict      =       'layernorm.weight'
     if name == "encoder.norm.bias":
          lowercase__    :   int      =       'layernorm.bias'

     if "decoder" in name:
          pass
     else:
          lowercase__    :   List[Any]      =       'swin.' + name

     return name

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[int]	,     SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          lowercase__    :   str      =       orig_state_dict.pop(SCREAMING_SNAKE_CASE_			)

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

               if "weight" in key:
                    lowercase__    :   Optional[int]      =       val[:dim, :]
                    lowercase__    :   Optional[int]      =       val[
                        dim : dim * 2, :
                    ]
                    lowercase__    :   str      =       val[-dim:, :]
               else:
                    lowercase__    :   Union[str, Any]      =       val[
                        :dim
                    ]
                    lowercase__    :   Optional[Any]      =       val[
                        dim : dim * 2
                    ]
                    lowercase__    :   str      =       val[
                        -dim:
                    ]
          else:
               lowercase__    :   Optional[int]      =       val

     return orig_state_dict

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





     lowercase__    :   int      =       torch.load(SCREAMING_SNAKE_CASE_	,     map_location='cpu'			)['model']

     lowercase__    :   List[str]      =       get_swin_config(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Any      =       SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_			)
     model.eval()

     lowercase__    :   Optional[Any]      =       convert_state_dict(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
     model.load_state_dict(SCREAMING_SNAKE_CASE_			)

     lowercase__    :   Union[str, Any]      =       'http://images.cocodataset.org/val2017/000000039769.jpg'

     lowercase__    :   List[Any]      =       ViTImageProcessor(size={'height': 192, 'width': 192}			)
     lowercase__    :   Dict      =       Image.open(requests.get(SCREAMING_SNAKE_CASE_	,     stream=SCREAMING_SNAKE_CASE_			).raw			)
     lowercase__    :   Union[str, Any]      =       image_processor(images=SCREAMING_SNAKE_CASE_	,     return_tensors='pt'			)

     with torch.no_grad():
          lowercase__    :   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__":
       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)




from __future__ import annotations

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

