


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    SwinConfig(image_size=1_92    )

     if "base" in model_name:
          snake_case					=    6
          snake_case					=    1_28
          snake_case					=    (2, 2, 18, 2)
          snake_case					=    (4, 8, 16, 32)
     elif "large" in model_name:
          snake_case					=    12
          snake_case					=    1_92
          snake_case					=    (2, 2, 18, 2)
          snake_case					=    (6, 12, 24, 48)
     else:
          raise ValueError('''Model not supported, only supports base and large variants'''    )

     snake_case					=    window_size
     snake_case					=    embed_dim
     snake_case					=    depths
     snake_case					=    num_heads

     return config
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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

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

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

     return name
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     for key in orig_state_dict.copy().keys():
          snake_case					=    orig_state_dict.pop(UpperCamelCase_    )

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

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

     return orig_state_dict
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    torch.load(UpperCamelCase_							,map_location='''cpu'''    )['''model''']

     snake_case					=    get_swin_config(UpperCamelCase_    )
     snake_case					=    SwinForMaskedImageModeling(UpperCamelCase_    )
     model.eval()

     snake_case					=    convert_state_dict(UpperCamelCase_							,UpperCamelCase_    )
     model.load_state_dict(UpperCamelCase_    )

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

     snake_case					=    ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}    )
     snake_case					=    Image.open(requests.get(UpperCamelCase_							,stream=UpperCamelCase_    ).raw    )
     snake_case					=    image_processor(images=UpperCamelCase_							,return_tensors='''pt'''    )

     with torch.no_grad():
          snake_case					=    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__":
 _SCREAMING_SNAKE_CASE      :			Union[str, 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."
 )

 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    [[0] * n for i in range(UpperCamelCase_    )]
     for i in range(UpperCamelCase_    ):
          snake_case					=    y_points[i]

     for i in range(2							,UpperCamelCase_    ):
          for j in range(UpperCamelCase_							,UpperCamelCase_    ):
               snake_case					=    (
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




