





"""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_			(      SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =SwinConfig()
							__lowerCamelCase      :      List[Any]         =swin_name.split('''_'''   )

							__lowerCamelCase      :      str         =name_split[1]
							__lowerCamelCase      :      List[str]         =int(name_split[4]   )
							__lowerCamelCase      :      int         =int(name_split[3][-1]   )

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

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

							__lowerCamelCase      :      Optional[Any]         =img_size
							__lowerCamelCase      :      str         =num_classes
							__lowerCamelCase      :      str         =embed_dim
							__lowerCamelCase      :      Union[str, Any]         =depths
							__lowerCamelCase      :      List[Any]         =num_heads
							__lowerCamelCase      :      Union[str, Any]         =window_size

							return config



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







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

							if name == "norm.weight":
														__lowerCamelCase      :      str         ='''layernorm.weight'''
							if name == "norm.bias":
														__lowerCamelCase      :      Optional[int]         ='''layernorm.bias'''

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

							return name



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							for key in orig_state_dict.copy().keys():
														__lowerCamelCase      :      Tuple         =orig_state_dict.pop(SCREAMING_SNAKE_CASE   )

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

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

							return orig_state_dict



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         =timm.create_model(SCREAMING_SNAKE_CASE    ,						pretrained=SCREAMING_SNAKE_CASE   )
							timm_model.eval()

							__lowerCamelCase      :      str         =get_swin_config(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =SwinForImageClassification(SCREAMING_SNAKE_CASE   )
							model.eval()

							__lowerCamelCase      :      int         =convert_state_dict(timm_model.state_dict()    ,						SCREAMING_SNAKE_CASE   )
							model.load_state_dict(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Any         ='''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

							__lowerCamelCase      :      str         =timm_model(inputs['''pixel_values''']   )
							__lowerCamelCase      :      Tuple         =model(**SCREAMING_SNAKE_CASE   ).logits

							assert torch.allclose(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-3   )

							print(F'Saving model {swin_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 __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)











"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =[[0] * n for i in range(SCREAMING_SNAKE_CASE   )]
							for i in range(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Union[str, Any]         =y_points[i]

							for i in range(2    ,						SCREAMING_SNAKE_CASE   ):
														for j in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =(
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




