

import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   int	,							*lowerCAmelCase			:   List[Any]	,							**lowerCAmelCase			:   Tuple   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use YolosImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )






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      snake_case_				(__A					:						Optional[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						SwinConfig()
   __lowerCAmelCase  :      List[Any]    =						swin_name.split("""_"""					)

   __lowerCAmelCase  :      Dict    =						name_split[1]
   __lowerCAmelCase  :      Optional[Any]    =						int(name_split[4]					)
   __lowerCAmelCase  :      List[Any]    =						int(name_split[3][-1]					)

   if model_size == "tiny":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      List[Any]    =						(2, 2, 6, 2)
      __lowerCAmelCase  :      Optional[Any]    =						(3, 6, 1_2, 2_4)
   elif model_size == "small":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      Optional[int]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      Optional[int]    =						(3, 6, 1_2, 2_4)
   elif model_size == "base":
      __lowerCAmelCase  :      List[Any]    =						1_2_8
      __lowerCAmelCase  :      Tuple    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(4, 8, 1_6, 3_2)
   else:
      __lowerCAmelCase  :      List[Any]    =						1_9_2
      __lowerCAmelCase  :      List[str]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      __lowerCAmelCase  :      Dict    =						2_1_8_4_1
   else:
      __lowerCAmelCase  :      Optional[Any]    =						1_0_0_0
      __lowerCAmelCase  :      Union[str, Any]    =						"""huggingface/label-files"""
      __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
      __lowerCAmelCase  :      Any    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
      __lowerCAmelCase  :      int    =						{int(__A					): v for k, v in idalabel.items()}
      __lowerCAmelCase  :      str    =						idalabel
      __lowerCAmelCase  :      int    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :      Optional[Any]    =						img_size
   __lowerCAmelCase  :      Optional[Any]    =						num_classes
   __lowerCAmelCase  :      Tuple    =						embed_dim
   __lowerCAmelCase  :      Union[str, Any]    =						depths
   __lowerCAmelCase  :      Optional[Any]    =						num_heads
   __lowerCAmelCase  :      Tuple    =						window_size

   return config





def      snake_case_				(__A					:						int					)      ->     Optional[Any]:
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if "layers" in name:
      __lowerCAmelCase  :      int    =						"""encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      str    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

   if name == "norm.weight":
      __lowerCAmelCase  :      Dict    =						"""layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase  :      Optional[int]    =						"""layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase  :      int    =						name.replace("""head""" ,       """classifier"""					)
   else:
      __lowerCAmelCase  :      List[str]    =						"""swin.""" + name

   return name





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     int:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Tuple    =						orig_state_dict.pop(__A					)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase  :      Any    =						key.split("""."""					)
         __lowerCAmelCase  :      Union[str, Any]    =						int(key_split[1]					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Optional[int]    =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCAmelCase  :      List[str]    =						val[:dim, :]
            __lowerCAmelCase  :      List[Any]    =						val[
                dim : dim * 2, :
            ]
            __lowerCAmelCase  :      str    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      str    =						val[
                :dim
            ]
            __lowerCAmelCase  :      int    =						val[
                dim : dim * 2
            ]
            __lowerCAmelCase  :      int    =						val[
                -dim:
            ]
      else:
         __lowerCAmelCase  :      Tuple    =						val

   return orig_state_dict





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						timm.create_model(__A ,       pretrained=__A					)
   timm_model.eval()

   __lowerCAmelCase  :      str    =						get_swin_config(__A					)
   __lowerCAmelCase  :      Any    =						SwinForImageClassification(__A					)
   model.eval()

   __lowerCAmelCase  :      str    =						convert_state_dict(timm_model.state_dict() ,       __A					)
   model.load_state_dict(__A					)

   __lowerCAmelCase  :      str    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __lowerCAmelCase  :      Any    =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,       """-"""					)					)					)
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=__A ,       return_tensors="""pt"""					)

   __lowerCAmelCase  :      Tuple    =						timm_model(inputs["""pixel_values"""]					)
   __lowerCAmelCase  :      Dict    =						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__":
   __UpperCAmelCase								=			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."""
   )

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


