
'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						SwinConfig(image_size=192						)

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

    A       :   List[Any]					=						window_size
    A       :   Tuple					=						embed_dim
    A       :   Optional[int]					=						depths
    A       :   List[str]					=						num_heads

    return config







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



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

    if name == "encoder.norm.weight":
        A       :   str					=						'''layernorm.weight'''
    if name == "encoder.norm.bias":
        A       :   int					=						'''layernorm.bias'''

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

    return name







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    for key in orig_state_dict.copy().keys():
        A       :   Tuple					=						orig_state_dict.pop(snake_case__						)

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

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

    return orig_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Optional[int]					=						torch.load(snake_case__		,				map_location='''cpu'''						)['''model''']

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

    A       :   Dict					=						convert_state_dict(snake_case__		,				snake_case__						)
    model.load_state_dict(snake_case__						)

    A       :   List[str]					=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

    with torch.no_grad():
        A       :   Any					=						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[str]    		=			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			:				Optional[Any]    		=			parser.parse_args()
      convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






