'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def       A_ (			_lowerCamelCase      :				Union[str, Any]     ):
      _lowerCAmelCase		 = SwinConfig(image_size=192     )

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

      _lowerCAmelCase		 = window_size
      _lowerCAmelCase		 = embed_dim
      _lowerCAmelCase		 = depths
      _lowerCAmelCase		 = num_heads

      return config







def       A_ (			_lowerCamelCase      :				Dict     ):



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

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

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

      return name







def       A_ (			_lowerCamelCase      :				Optional[Any]	,	_lowerCamelCase      :				int     ):



      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase		 = orig_state_dict.pop(UpperCAmelCase__     )

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

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

      return orig_state_dict







def       A_ (			_lowerCamelCase      :				Tuple	,	_lowerCamelCase      :				Optional[int]	,	_lowerCamelCase      :				Optional[Any]	,	_lowerCamelCase      :				Dict     ):
      _lowerCAmelCase		 = torch.load(UpperCAmelCase__	,	map_location='cpu'     )['model']

      _lowerCAmelCase		 = get_swin_config(UpperCAmelCase__     )
      _lowerCAmelCase		 = SwinForMaskedImageModeling(UpperCAmelCase__     )
      model.eval()

      _lowerCAmelCase		 = convert_state_dict(UpperCAmelCase__	,	UpperCAmelCase__     )
      model.load_state_dict(UpperCAmelCase__     )

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

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

      with torch.no_grad():
            _lowerCAmelCase		 = model(**UpperCAmelCase__     ).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(UpperCAmelCase__     )

            print(F"Saving image processor to {pytorch_dump_folder_path}"     )
            image_processor.save_pretrained(UpperCAmelCase__     )

      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 ..utils import DummyObject, requires_backends





class  lowerCamelCase__	(    metaclass=UpperCAmelCase_):
     """simple docstring"""



     _A      =      ['transformers', 'torch', 'note_seq']







     def __init__(self					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(self					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )