


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					str    =				SwinConfig(image_size=192	)

  if "base" in model_name:
    lowerCamelCase :					Optional[Any]    =				6
    lowerCamelCase :					List[str]    =				128
    lowerCamelCase :					str    =				(2, 2, 18, 2)
    lowerCamelCase :					Optional[Any]    =				(4, 8, 16, 32)
  elif "large" in model_name:
    lowerCamelCase :					List[str]    =				12
    lowerCamelCase :					Optional[Any]    =				192
    lowerCamelCase :					int    =				(2, 2, 18, 2)
    lowerCamelCase :					str    =				(6, 12, 24, 48)
  else:
    raise ValueError("Model not supported, only supports base and large variants"	)

  lowerCamelCase :					str    =				window_size
  lowerCamelCase :					Union[str, Any]    =				embed_dim
  lowerCamelCase :					str    =				depths
  lowerCamelCase :					Dict    =				num_heads

  return config






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "encoder.mask_token" in name:
    lowerCamelCase :					Union[str, Any]    =				name.replace("encoder.mask_token"      ,						"embeddings.mask_token"	)
  if "encoder.patch_embed.proj" in name:
    lowerCamelCase :					Any    =				name.replace("encoder.patch_embed.proj"      ,						"embeddings.patch_embeddings.projection"	)
  if "encoder.patch_embed.norm" in name:
    lowerCamelCase :					Union[str, Any]    =				name.replace("encoder.patch_embed.norm"      ,						"embeddings.norm"	)
  if "attn.proj" in name:
    lowerCamelCase :					List[str]    =				name.replace("attn.proj"      ,						"attention.output.dense"	)
  if "attn" in name:
    lowerCamelCase :					str    =				name.replace("attn"      ,						"attention.self"	)
  if "norm1" in name:
    lowerCamelCase :					Any    =				name.replace("norm1"      ,						"layernorm_before"	)
  if "norm2" in name:
    lowerCamelCase :					Optional[int]    =				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 :					List[Any]    =				name.replace("mlp.fc2"      ,						"output.dense"	)

  if name == "encoder.norm.weight":
    lowerCamelCase :					Optional[int]    =				"layernorm.weight"
  if name == "encoder.norm.bias":
    lowerCamelCase :					int    =				"layernorm.bias"

  if "decoder" in name:
    pass
  else:
    lowerCamelCase :					int    =				"swin." + name

  return name






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					Dict    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if "attn_mask" in key:
      pass
    elif "qkv" in key:
      lowerCamelCase :					Optional[Any]    =				key.split("."	)
      lowerCamelCase :					Union[str, Any]    =				int(key_split[2]	)
      lowerCamelCase :					List[Any]    =				int(key_split[4]	)
      lowerCamelCase :					Union[str, Any]    =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        lowerCamelCase :					Union[str, Any]    =				val[:dim, :]
        lowerCamelCase :					Optional[Any]    =				val[
            dim : dim * 2, :
        ]
        lowerCamelCase :					Any    =				val[-dim:, :]
      else:
        lowerCamelCase :					List[Any]    =				val[
            :dim
        ]
        lowerCamelCase :					List[str]    =				val[
            dim : dim * 2
        ]
        lowerCamelCase :					Union[str, Any]    =				val[
            -dim:
        ]
    else:
      lowerCamelCase :					str    =				val

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model"]

  lowerCamelCase :					Optional[int]    =				get_swin_config(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_	)
  model.eval()

  lowerCamelCase :					List[str]    =				convert_state_dict(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

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

  lowerCamelCase :					List[Any]    =				ViTImageProcessor(size={"height": 192, "width": 192}	)
  lowerCamelCase :					str    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  lowerCamelCase :					Any    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)

  with torch.no_grad():
    lowerCamelCase :					Union[str, Any]    =				model(**SCREAMING_SNAKE_CASE_	).logits

  print(outputs.keys()	)
  print("Looks ok!"	)

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(SCREAMING_SNAKE_CASE_	)

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)

  if push_to_hub:
    print(f"""Pushing model and image processor for {model_name} to hub"""	)
    model.push_to_hub(f"""microsoft/{model_name}"""	)
    image_processor.push_to_hub(f"""microsoft/{model_name}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''swin-base-simmim-window6-192''',
       type=str,
       choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
       help='''Name of the Swin SimMIM model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--checkpoint_path''',
       default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
       type=str,
       help='''Path to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )
   parser.add_argument(
       '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
   )

   _snake_case             =     parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case             =     logging.get_logger(__name__)
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





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



