


import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  snake_case__						= SwinConfig()
  snake_case__						= swin_name.split('''_'''				)

  snake_case__						= name_split[1]
  snake_case__						= int(name_split[4]				)
  snake_case__						= int(name_split[3][-1]				)

  if model_size == "tiny":
    snake_case__						= 96
    snake_case__						= (2, 2, 6, 2)
    snake_case__						= (3, 6, 12, 24)
  elif model_size == "small":
    snake_case__						= 96
    snake_case__						= (2, 2, 18, 2)
    snake_case__						= (3, 6, 12, 24)
  elif model_size == "base":
    snake_case__						= 128
    snake_case__						= (2, 2, 18, 2)
    snake_case__						= (4, 8, 16, 32)
  else:
    snake_case__						= 192
    snake_case__						= (2, 2, 18, 2)
    snake_case__						= (6, 12, 24, 48)

  if "in22k" in swin_name:
    snake_case__						= 2_1841
  else:
    snake_case__						= 1000
    snake_case__						= '''huggingface/label-files'''
    snake_case__						= '''imagenet-1k-id2label.json'''
    snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
    snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    snake_case__						= idalabel
    snake_case__						= {v: k for k, v in idalabel.items()}

  snake_case__						= img_size
  snake_case__						= num_classes
  snake_case__						= embed_dim
  snake_case__						= depths
  snake_case__						= num_heads
  snake_case__						= window_size

  return config







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  if "patch_embed.proj" in name:
    snake_case__						= name.replace('''patch_embed.proj'''							,   '''embeddings.patch_embeddings.projection'''				)
  if "patch_embed.norm" in name:
    snake_case__						= name.replace('''patch_embed.norm'''							,   '''embeddings.norm'''				)
  if "layers" in name:
    snake_case__						= '''encoder.''' + name
  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 == "norm.weight":
    snake_case__						= '''layernorm.weight'''
  if name == "norm.bias":
    snake_case__						= '''layernorm.bias'''

  if "head" in name:
    snake_case__						= name.replace('''head'''							,   '''classifier'''				)
  else:
    snake_case__						= '''swin.''' + name

  return name







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:
  for key in orig_state_dict.copy().keys():
    snake_case__						= orig_state_dict.pop(__lowerCAmelCase				)

    if "mask" in key:
      continue
    elif "qkv" in key:
      snake_case__						= key.split('''.'''				)
      snake_case__						= int(key_split[1]				)
      snake_case__						= int(key_split[3]				)
      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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				int:
  snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				)
  timm_model.eval()

  snake_case__						= get_swin_config(__lowerCAmelCase				)
  snake_case__						= SwinForImageClassification(__lowerCAmelCase				)
  model.eval()

  snake_case__						= convert_state_dict(timm_model.state_dict()							,   __lowerCAmelCase				)
  model.load_state_dict(__lowerCAmelCase				)

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

  snake_case__						= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''							,   '''-'''				)				)				)
  snake_case__						= Image.open(requests.get(__lowerCAmelCase							,   stream=__lowerCAmelCase				).raw				)
  snake_case__						= image_processor(images=__lowerCAmelCase							,   return_tensors='''pt'''				)

  snake_case__						= timm_model(inputs['''pixel_values''']				)
  snake_case__						= model(**__lowerCAmelCase				).logits

  assert torch.allclose(__lowerCAmelCase							,   __lowerCAmelCase							,   atol=1e-3				)

  print(F"""Saving model {swin_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 __name__ == "__main__":
   lowerCamelCase__							:						Union[str, Any]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin timm model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   lowerCamelCase__							:						Dict							     =   parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



"""simple docstring"""




import operator



def     __magic_name__		(					UpperCamelCase     :		list							,		UpperCamelCase     :		bool = False							,		UpperCamelCase     :		list | None = None     )     ->						list:
    a__		=			operator.lt if reverse else operator.gt
    a__		=			solution or []

    if not arr:
        return solution

    a__		=			[arr.pop(0     )]
    for i, item in enumerate(UpperCamelCase     ):
        if _operator(UpperCamelCase							,		sublist[-1]     ):
            sublist.append(UpperCamelCase     )
            arr.pop(UpperCamelCase     )

    #  merging sublist into solution list
    if not solution:
        solution.extend(UpperCamelCase     )
    else:
        while sublist:
            a__		=			sublist.pop(0     )
            for i, xx in enumerate(UpperCamelCase     ):
                if not _operator(UpperCamelCase							,		UpperCamelCase     ):
                    solution.insert(UpperCamelCase							,		UpperCamelCase     )
                    break
            else:
                solution.append(UpperCamelCase     )

    strand_sort(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    return solution


if __name__ == "__main__":
    assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
    assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]


