'''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_   : Tuple					)    ->  Optional[int]:
  __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						    =       1_28
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (4, 8, 16, 32)
  else:
    __UpperCAmelCase						    =       1_92
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (6, 12, 24, 48)

  if "in22k" in swin_name:
    __UpperCAmelCase						    =       2_18_41
  else:
    __UpperCAmelCase						    =       10_00
    __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_   : int					)    ->  Any:
  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_   : Tuple						,	A_   : int					)    ->  Tuple:
  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_   : Optional[Any]						,	A_   : List[Any]					)    ->  Tuple:
  __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__":
       a_							=      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."""
       )

       a_							=      parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




from ..utils import DummyObject, requires_backends


class 			UpperCAmelCase__    (		metaclass=snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= ['transformers', 'torch', 'note_seq']






  def __init__(				self:			List[str]				,				*__lowerCAmelCase:			Optional[int]				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[int]:




    '''simple docstring'''




    requires_backends(self				,				["transformers", "torch", "note_seq"]   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Optional[int]				,				*__lowerCAmelCase:			Any				,				**__lowerCAmelCase:			List[str]   ) -> Dict:




    '''simple docstring'''




    requires_backends(cls				,				["transformers", "torch", "note_seq"]   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Union[str, Any]				,				*__lowerCAmelCase:			Optional[Any]				,				**__lowerCAmelCase:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    requires_backends(cls				,				["transformers", "torch", "note_seq"]   )



