




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def    A						(__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     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    A						(__A   :    Dict       )   ->      str:







     """simple docstring"""


     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    A						(__A   :    Dict   ,				__A   :    List[Any]       )   ->      Any:







     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_          =							orig_state_dict.pop(__A       )

          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    A						(__A   :    str   ,				__A   :    Tuple   ,				__A   :    List[str]   ,				__A   :    List[str]       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							torch.load(__A   ,				map_location='''cpu'''       )['''model''']

     UpperCAmelCase_          =							get_swin_config(__A       )
     UpperCAmelCase_          =							SwinForMaskedImageModeling(__A       )
     model.eval()

     UpperCAmelCase_          =							convert_state_dict(__A   ,				__A       )
     model.load_state_dict(__A       )

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

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

     with torch.no_grad():
          UpperCAmelCase_          =							model(**__A       ).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(__A       )

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

     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_ : Any    		=       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_ : int    		=       parser.parse_args()
    convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_ : int    		=       logging.get_logger(__name__)

snake_case_ : str    		=       {}







class __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''llama'''
   UpperCAmelCase__  :    Dict          =  ['''past_key_values''']







   def __init__(      self		:						str					,						_snake_case		:						List[str]=32000					,						_snake_case		:						int=4096					,						_snake_case		:						List[str]=11008					,						_snake_case		:						Optional[int]=32					,						_snake_case		:						List[Any]=32					,						_snake_case		:						Tuple=None					,						_snake_case		:						int="silu"					,						_snake_case		:						List[Any]=2048					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Any=1e-6					,						_snake_case		:						List[str]=True					,						_snake_case		:						Optional[Any]=0					,						_snake_case		:						Dict=1					,						_snake_case		:						List[Any]=2					,						_snake_case		:						str=1					,						_snake_case		:						Union[str, Any]=False					,						_snake_case		:						str=None					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
             UpperCAmelCase_          =							num_attention_heads

        UpperCAmelCase_          =							num_key_value_heads
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							rms_norm_eps
        UpperCAmelCase_          =							pretraining_tp
        UpperCAmelCase_          =							use_cache
        UpperCAmelCase_          =							rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=_snake_case					,						bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						tie_word_embeddings=_snake_case					,						**_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        if self.rope_scaling is None:
             return

        if not isinstance(self.rope_scaling					,						_snake_case) or len(self.rope_scaling) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F"""got {self.rope_scaling}""")
        UpperCAmelCase_          =							self.rope_scaling.get('''type'''					,						_snake_case)
        UpperCAmelCase_          =							self.rope_scaling.get('''factor'''					,						_snake_case)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""")
        if rope_scaling_factor is None or not isinstance(_snake_case					,						_snake_case) or rope_scaling_factor <= 1.0:
             raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""")






