




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 				A__ (snake_case   :		Optional[int]				)     ->						List[str]:
					__UpperCamelCase					:	Dict             =      SwinConfig()
					__UpperCamelCase					:	Optional[int]             =      swin_name.split("""_"""				)

					__UpperCamelCase					:	Union[str, Any]             =      name_split[1]
					__UpperCamelCase					:	str             =      int(name_split[4]				)
					__UpperCamelCase					:	Optional[int]             =      int(name_split[3][-1]				)

					if model_size == "tiny":
										__UpperCamelCase					:	Optional[int]             =      96
										__UpperCamelCase					:	Optional[int]             =      (2, 2, 6, 2)
										__UpperCamelCase					:	Optional[int]             =      (3, 6, 12, 24)
					elif model_size == "small":
										__UpperCamelCase					:	Optional[int]             =      96
										__UpperCamelCase					:	Union[str, Any]             =      (2, 2, 18, 2)
										__UpperCamelCase					:	Dict             =      (3, 6, 12, 24)
					elif model_size == "base":
										__UpperCamelCase					:	int             =      1_28
										__UpperCamelCase					:	Optional[Any]             =      (2, 2, 18, 2)
										__UpperCamelCase					:	List[Any]             =      (4, 8, 16, 32)
					else:
										__UpperCamelCase					:	Optional[Any]             =      1_92
										__UpperCamelCase					:	List[str]             =      (2, 2, 18, 2)
										__UpperCamelCase					:	Dict             =      (6, 12, 24, 48)

					if "in22k" in swin_name:
										__UpperCamelCase					:	Union[str, Any]             =      2_18_41
					else:
										__UpperCamelCase					:	Optional[Any]             =      10_00
										__UpperCamelCase					:	int             =      """huggingface/label-files"""
										__UpperCamelCase					:	Tuple             =      """imagenet-1k-id2label.json"""
										__UpperCamelCase					:	Tuple             =      json.load(open(hf_hub_download(snake_case		,	snake_case		,	repo_type="""dataset"""				)		,	"""r"""				)				)
										__UpperCamelCase					:	int             =      {int(snake_case				): v for k, v in idalabel.items()}
										__UpperCamelCase					:	List[Any]             =      idalabel
										__UpperCamelCase					:	List[str]             =      {v: k for k, v in idalabel.items()}

					__UpperCamelCase					:	List[Any]             =      img_size
					__UpperCamelCase					:	Any             =      num_classes
					__UpperCamelCase					:	str             =      embed_dim
					__UpperCamelCase					:	str             =      depths
					__UpperCamelCase					:	int             =      num_heads
					__UpperCamelCase					:	str             =      window_size

					return config





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

					if name == "norm.weight":
										__UpperCamelCase					:	int             =      """layernorm.weight"""
					if name == "norm.bias":
										__UpperCamelCase					:	List[Any]             =      """layernorm.bias"""

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

					return name





def 				A__ (snake_case   :		Union[str, Any]		,	snake_case   :		int				)     ->						Any:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase					:	List[Any]             =      orig_state_dict.pop(snake_case				)

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

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

					return orig_state_dict





def 				A__ (snake_case   :		Tuple		,	snake_case   :		Optional[Any]				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      timm.create_model(snake_case		,	pretrained=snake_case				)
					timm_model.eval()

					__UpperCamelCase					:	Tuple             =      get_swin_config(snake_case				)
					__UpperCamelCase					:	Dict             =      SwinForImageClassification(snake_case				)
					model.eval()

					__UpperCamelCase					:	Union[str, Any]             =      convert_state_dict(timm_model.state_dict()		,	snake_case				)
					model.load_state_dict(snake_case				)

					__UpperCamelCase					:	Optional[Any]             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""

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

					__UpperCamelCase					:	Union[str, Any]             =      timm_model(inputs["""pixel_values"""]				)
					__UpperCamelCase					:	List[str]             =      model(**snake_case				).logits

					assert torch.allclose(snake_case		,	snake_case		,	atol=1e-3				)

					print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
					model.save_pretrained(snake_case				)

					print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
					image_processor.save_pretrained(snake_case				)


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)












def 				A__ (snake_case   :		int				)     ->						bool:
					if number < 0:
										raise ValueError("""number must not be negative"""				)
					return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()






