






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			SwinConfig(image_size=192				)

				if "base" in model_name:
								lowercase__    =			6
								lowercase__    =			128
								lowercase__    =			(2, 2, 18, 2)
								lowercase__    =			(4, 8, 16, 32)
				elif "large" in model_name:
								lowercase__    =			12
								lowercase__    =			192
								lowercase__    =			(2, 2, 18, 2)
								lowercase__    =			(6, 12, 24, 48)
				else:
								raise ValueError('''Model not supported, only supports base and large variants'''				)

				lowercase__    =			window_size
				lowercase__    =			embed_dim
				lowercase__    =			depths
				lowercase__    =			num_heads

				return config






def   a			(						lowerCamelCase_				):






				'''simple docstring'''

				if "encoder.mask_token" in name:
								lowercase__    =			name.replace('''encoder.mask_token'''		,   '''embeddings.mask_token'''				)
				if "encoder.patch_embed.proj" in name:
								lowercase__    =			name.replace('''encoder.patch_embed.proj'''		,   '''embeddings.patch_embeddings.projection'''				)
				if "encoder.patch_embed.norm" in name:
								lowercase__    =			name.replace('''encoder.patch_embed.norm'''		,   '''embeddings.norm'''				)
				if "attn.proj" in name:
								lowercase__    =			name.replace('''attn.proj'''		,   '''attention.output.dense'''				)
				if "attn" in name:
								lowercase__    =			name.replace('''attn'''		,   '''attention.self'''				)
				if "norm1" in name:
								lowercase__    =			name.replace('''norm1'''		,   '''layernorm_before'''				)
				if "norm2" in name:
								lowercase__    =			name.replace('''norm2'''		,   '''layernorm_after'''				)
				if "mlp.fc1" in name:
								lowercase__    =			name.replace('''mlp.fc1'''		,   '''intermediate.dense'''				)
				if "mlp.fc2" in name:
								lowercase__    =			name.replace('''mlp.fc2'''		,   '''output.dense'''				)

				if name == "encoder.norm.weight":
								lowercase__    =			"layernorm.weight"
				if name == "encoder.norm.bias":
								lowercase__    =			"layernorm.bias"

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

				return name






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''

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

								if "attn_mask" in key:
												pass
								elif "qkv" in key:
												lowercase__    =			key.split('''.'''				)
												lowercase__    =			int(key_split[2]				)
												lowercase__    =			int(key_split[4]				)
												lowercase__    =			model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																lowercase__    =			val[:dim, :]
																lowercase__    =			val[
																    dim : dim * 2, :
																]
																lowercase__    =			val[-dim:, :]
												else:
																lowercase__    =			val[
																    :dim
																]
																lowercase__    =			val[
																    dim : dim * 2
																]
																lowercase__    =			val[
																    -dim:
																]
								else:
												lowercase__    =			val

				return orig_state_dict






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			torch.load(SCREAMING_SNAKE_CASE_		,   map_location='''cpu'''				)["model"]

				lowercase__    =			get_swin_config(SCREAMING_SNAKE_CASE_				)
				lowercase__    =			SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_				)
				model.eval()

				lowercase__    =			convert_state_dict(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_				)
				model.load_state_dict(SCREAMING_SNAKE_CASE_				)

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

				lowercase__    =			ViTImageProcessor(size={'''height''': 192, '''width''': 192}				)
				lowercase__    =			Image.open(requests.get(SCREAMING_SNAKE_CASE_		,   stream=SCREAMING_SNAKE_CASE_				).raw				)
				lowercase__    =			image_processor(images=SCREAMING_SNAKE_CASE_		,   return_tensors='''pt'''				)

				with torch.no_grad():
								lowercase__    =			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__":
							A__	:			Tuple			   = 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.'
							)

							A__	:			Dict			   = parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				"backbone." if is_semantic else ""

  lowerCamelCase :					int    =				[]
  for i in range(config.num_hidden_layers	):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")	)
    rename_keys.append(
        (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")	)
    rename_keys.append(
        (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")	)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (f"""{prefix}cls_token""", "beit.embeddings.cls_token"),
          (f"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
          (f"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
          (f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
      ]	)

  if has_lm_head:
    # mask token + layernorm
    rename_keys.extend(
        [
            ("mask_token", "beit.embeddings.mask_token"),
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
        ]	)
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("fc_norm.weight", "beit.pooler.layernorm.weight"),
            ("fc_norm.bias", "beit.pooler.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]	)

  return rename_keys






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  for i in range(config.num_hidden_layers	):
    lowerCamelCase :					Optional[Any]    =				"backbone." if is_semantic else ""
    # queries, keys and values
    lowerCamelCase :					Optional[Any]    =				state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""	)
    lowerCamelCase :					Optional[Any]    =				state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""	)
    lowerCamelCase :					Tuple    =				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""	)

    lowerCamelCase :					str    =				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase :					Any    =				q_bias
    lowerCamelCase :					Any    =				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase :					Optional[int]    =				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase :					int    =				v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCamelCase :					Any    =				state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""	)
    lowerCamelCase :					Any    =				state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""	)

    lowerCamelCase :					int    =				gamma_a
    lowerCamelCase :					Optional[Any]    =				gamma_a






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				dct.pop(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				val






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase :					Optional[Any]    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  return im






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				False if "rvlcdip" in checkpoint_url else True
  lowerCamelCase :					str    =				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_      ,						use_mask_token=SCREAMING_SNAKE_CASE_	)

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    lowerCamelCase :					Union[str, Any]    =				1024
    lowerCamelCase :					Any    =				4096
    lowerCamelCase :					str    =				24
    lowerCamelCase :					List[Any]    =				16

  # labels
  if "rvlcdip" in checkpoint_url:
    lowerCamelCase :					Optional[Any]    =				16
    lowerCamelCase :					Tuple    =				"huggingface/label-files"
    lowerCamelCase :					List[str]    =				"rvlcdip-id2label.json"
    lowerCamelCase :					str    =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						repo_type="dataset"	)      ,						"r"	)	)
    lowerCamelCase :					Any    =				{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
    lowerCamelCase :					Tuple    =				idalabel
    lowerCamelCase :					Dict    =				{v: k for k, v in idalabel.items()}

  # load state_dict of original model, remove and rename some keys
  lowerCamelCase :					int    =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model"]

  lowerCamelCase :					Tuple    =				create_rename_keys(SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  read_in_q_k_v(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)

  # load HuggingFace model
  lowerCamelCase :					List[Any]    =				BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_	) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  # Check outputs on an image
  lowerCamelCase :					str    =				BeitImageProcessor(
      size=config.image_size      ,						resample=PILImageResampling.BILINEAR      ,						do_center_crop=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				prepare_img()

  lowerCamelCase :					Optional[int]    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)
  lowerCamelCase :					Optional[Any]    =				encoding["pixel_values"]

  lowerCamelCase :					Optional[Any]    =				model(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Dict    =				outputs.logits

  # verify logits
  lowerCamelCase :					List[Any]    =				[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
  assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_	), "Shape of logits not as expected"

  Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
  print(f"""Saving model 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:
    if has_lm_head:
      lowerCamelCase :					Optional[Any]    =				"dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      lowerCamelCase :					Dict    =				"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
    image_processor.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add image processor"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)
    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add model"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
       type=str,
       help='''URL to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   _snake_case             =     parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



