import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = SwinConfig(image_size=1_9_2     )

       if "base" in model_name:
              lowerCAmelCase__					     = 6
              lowerCAmelCase__					     = 1_2_8
              lowerCAmelCase__					     = (2, 2, 1_8, 2)
              lowerCAmelCase__					     = (4, 8, 1_6, 3_2)
       elif "large" in model_name:
              lowerCAmelCase__					     = 1_2
              lowerCAmelCase__					     = 1_9_2
              lowerCAmelCase__					     = (2, 2, 1_8, 2)
              lowerCAmelCase__					     = (6, 1_2, 2_4, 4_8)
       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 				__lowerCamelCase		(						lowerCAmelCase__     ):
       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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for key in orig_state_dict.copy().keys():
              lowerCAmelCase__					     = orig_state_dict.pop(lowerCAmelCase__     )

              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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = torch.load(lowerCAmelCase__     ,  map_location='cpu'     )['model']

       lowerCAmelCase__					     = get_swin_config(lowerCAmelCase__     )
       lowerCAmelCase__					     = SwinForMaskedImageModeling(lowerCAmelCase__     )
       model.eval()

       lowerCAmelCase__					     = convert_state_dict(lowerCAmelCase__     ,  lowerCAmelCase__     )
       model.load_state_dict(lowerCAmelCase__     )

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

       lowerCAmelCase__					     = ViTImageProcessor(size={'height': 1_9_2, 'width': 1_9_2}     )
       lowerCAmelCase__					     = Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__     ).raw     )
       lowerCAmelCase__					     = image_processor(images=lowerCAmelCase__     ,  return_tensors='pt'     )

       with torch.no_grad():
              lowerCAmelCase__					     = model(**lowerCAmelCase__     ).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(lowerCAmelCase__     )

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

       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__":
    lowerCAmelCase__     						=			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.'
    )

    lowerCAmelCase__     						=			parser.parse_args()
    convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       for i in range(lowerCAmelCase__     ):
              for j in range(i + 1     ,  lowerCAmelCase__     ):
                     if numbers[j] < numbers[i]:
                            lowerCAmelCase__     , lowerCAmelCase__					     = numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    lowerCAmelCase__     						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__     						=			[int(item) for item in user_input.split(',')]
    print(exchange_sort(unsorted))
