



'''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__    )       ->	Any:






   """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__    )       ->	List[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__    )       ->	Union[str, Any]:






   """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	:       List[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	:       Optional[int]      	=	parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






'''simple docstring'''


_lowerCamelCase	:       int      	=	"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich

