



'''simple docstring'''


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 							__lowerCamelCase							(  A__       )			->      str:






   """simple docstring"""
   UpperCamelCase      							=    SwinConfig()
   UpperCamelCase      							=    swin_name.split('_'       )

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

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

   if "in22k" in swin_name:
      UpperCamelCase      							=    21_841
   else:
      UpperCamelCase      							=    1_000
      UpperCamelCase      							=    'huggingface/label-files'
      UpperCamelCase      							=    'imagenet-1k-id2label.json'
      UpperCamelCase      							=    json.load(open(hf_hub_download(__a       ,		__a       ,		repo_type='dataset'       )       ,		'r'       )       )
      UpperCamelCase      							=    {int(__a       ): v for k, v in idalabel.items()}
      UpperCamelCase      							=    idalabel
      UpperCamelCase      							=    {v: k for k, v in idalabel.items()}

   UpperCamelCase      							=    img_size
   UpperCamelCase      							=    num_classes
   UpperCamelCase      							=    embed_dim
   UpperCamelCase      							=    depths
   UpperCamelCase      							=    num_heads
   UpperCamelCase      							=    window_size

   return config







def 							__lowerCamelCase							(  A__       )			->      Union[str, Any]:






   """simple docstring"""
   if "patch_embed.proj" in name:
      UpperCamelCase      							=    name.replace('patch_embed.proj'       ,		'embeddings.patch_embeddings.projection'       )
   if "patch_embed.norm" in name:
      UpperCamelCase      							=    name.replace('patch_embed.norm'       ,		'embeddings.norm'       )
   if "layers" in name:
      UpperCamelCase      							=    'encoder.' + name
   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 == "norm.weight":
      UpperCamelCase      							=    'layernorm.weight'
   if name == "norm.bias":
      UpperCamelCase      							=    'layernorm.bias'

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

   return name







def 							__lowerCamelCase							(  A__       ,		A__       )			->      Optional[int]:






   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      UpperCamelCase      							=    orig_state_dict.pop(__a       )

      if "mask" in key:
         continue
      elif "qkv" in key:
         UpperCamelCase      							=    key.split('.'       )
         UpperCamelCase      							=    int(key_split[1]       )
         UpperCamelCase      							=    int(key_split[3]       )
         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 							__lowerCamelCase							(  A__       ,		A__       )			->      Tuple:






   """simple docstring"""
   UpperCamelCase      							=    timm.create_model(__a       ,		pretrained=__a       )
   timm_model.eval()

   UpperCamelCase      							=    get_swin_config(__a       )
   UpperCamelCase      							=    SwinForImageClassification(__a       )
   model.eval()

   UpperCamelCase      							=    convert_state_dict(timm_model.state_dict()       ,		__a       )
   model.load_state_dict(__a       )

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

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

   UpperCamelCase      							=    timm_model(inputs['pixel_values']       )
   UpperCamelCase      							=    model(**__a       ).logits

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

   print(F"""Saving model {swin_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 __name__ == "__main__":
   _lowerCamelCase      :			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      :			Tuple       =      parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





def   _lowercase(					__a				:						list[int]     ):
							a__      =len(__a     )
							for i in range(__a     ):
														for j in range(i + 1    ,     __a     ):
																					if numbers[j] < numbers[i]:
																												a__    ,     a__      =numbers[j], numbers[i]
							return numbers


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

