'''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	(      UpperCAmelCase__       :					Tuple )			->      Any:
							lowercase_					:			Optional[Any]										=       SwinConfig()
							lowercase_					:			Union[str, Any]										=       swin_name.split("""_""" )

							lowercase_					:			List[Any]										=       name_split[1]
							lowercase_					:			Union[str, Any]										=       int(name_split[4] )
							lowercase_					:			Dict										=       int(name_split[3][-1] )

							if model_size == "tiny":
														lowercase_					:			Any										=       96
														lowercase_					:			Optional[int]										=       (2, 2, 6, 2)
														lowercase_					:			Tuple										=       (3, 6, 12, 24)
							elif model_size == "small":
														lowercase_					:			List[Any]										=       96
														lowercase_					:			Tuple										=       (2, 2, 18, 2)
														lowercase_					:			List[Any]										=       (3, 6, 12, 24)
							elif model_size == "base":
														lowercase_					:			Optional[int]										=       128
														lowercase_					:			List[Any]										=       (2, 2, 18, 2)
														lowercase_					:			Union[str, Any]										=       (4, 8, 16, 32)
							else:
														lowercase_					:			List[str]										=       192
														lowercase_					:			List[Any]										=       (2, 2, 18, 2)
														lowercase_					:			List[str]										=       (6, 12, 24, 48)

							if "in22k" in swin_name:
														lowercase_					:			Optional[Any]										=       21841
							else:
														lowercase_					:			Dict										=       1000
														lowercase_					:			Union[str, Any]										=       """huggingface/label-files"""
														lowercase_					:			Union[str, Any]										=       """imagenet-1k-id2label.json"""
														lowercase_					:			Union[str, Any]										=       json.load(open(hf_hub_download(UpperCAmelCase__      ,      UpperCAmelCase__      ,      repo_type="""dataset""" )      ,      """r""" ) )
														lowercase_					:			Tuple										=       {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
														lowercase_					:			Any										=       idalabel
														lowercase_					:			Optional[int]										=       {v: k for k, v in idalabel.items()}

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

							return config




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

							if name == "norm.weight":
														lowercase_					:			Optional[Any]										=       """layernorm.weight"""
							if name == "norm.bias":
														lowercase_					:			Tuple										=       """layernorm.bias"""

							if "head" in name:
														lowercase_					:			Union[str, Any]										=       name.replace("""head"""      ,      """classifier""" )
							else:
														lowercase_					:			Dict										=       """swin.""" + name

							return name




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					Optional[int] )			->      List[str]:
							for key in orig_state_dict.copy().keys():
														lowercase_					:			str										=       orig_state_dict.pop(UpperCAmelCase__ )

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

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

							return orig_state_dict




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					Any )			->      Optional[Any]:
							lowercase_					:			int										=       timm.create_model(UpperCAmelCase__      ,      pretrained=UpperCAmelCase__ )
							timm_model.eval()

							lowercase_					:			Optional[int]										=       get_swin_config(UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       SwinForImageClassification(UpperCAmelCase__ )
							model.eval()

							lowercase_					:			str										=       convert_state_dict(timm_model.state_dict()      ,      UpperCAmelCase__ )
							model.load_state_dict(UpperCAmelCase__ )

							lowercase_					:			Union[str, Any]										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""

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

							lowercase_					:			str										=       timm_model(inputs["""pixel_values"""] )
							lowercase_					:			Dict										=       model(**UpperCAmelCase__ ).logits

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

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

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


if __name__ == "__main__":
						_lowercase						: Union[str, Any]						=						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."
						)

						_lowercase						: Optional[int]						=						parser.parse_args()
						convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






'''simple docstring'''




_lowercase						: str						=						tuple[float, float, float]
_lowercase						: List[Any]						=						tuple[float, float, float]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad )			->      Vectorad:
							lowercase_					:			List[str]										=       end_pointa[0] - end_pointa[0]
							lowercase_					:			Union[str, Any]										=       end_pointa[1] - end_pointa[1]
							lowercase_					:			List[Any]										=       end_pointa[2] - end_pointa[2]
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					Vectorad )			->      Vectorad:
							lowercase_					:			List[Any]										=       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							lowercase_					:			Union[str, Any]										=       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							lowercase_					:			List[str]										=       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					int )			->      bool:
							return tuple(round(UpperCAmelCase__      ,      UpperCAmelCase__ ) for x in vector ) == (0, 0, 0)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					int = 10 )			->      bool:
							lowercase_					:			Dict										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      UpperCAmelCase__ )





