





"""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       __A (     a_  :Tuple)						->						Union[str, Any]:
				__a	:  Tuple         =					SwinConfig()
				__a	:  Tuple         =					swin_name.split('''_''')

				__a	:  int         =					name_split[1]
				__a	:  Optional[Any]         =					int(name_split[4])
				__a	:  str         =					int(name_split[3][-1])

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

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

				__a	:  List[str]         =					img_size
				__a	:  List[Any]         =					num_classes
				__a	:  List[str]         =					embed_dim
				__a	:  Optional[Any]         =					depths
				__a	:  Any         =					num_heads
				__a	:  List[str]         =					window_size

				return config

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

				if name == "norm.weight":
								__a	:  int         =					'''layernorm.weight'''
				if name == "norm.bias":
								__a	:  Any         =					'''layernorm.bias'''

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

				return name

def       __A (     a_  :List[Any]				,    a_  :Optional[Any])						->						int:
				for key in orig_state_dict.copy().keys():
								__a	:  Union[str, Any]         =					orig_state_dict.pop(a_)

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

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

				return orig_state_dict

def       __A (     a_  :List[Any]				,    a_  :Optional[int])						->						List[str]:
				__a	:  Optional[Any]         =					timm.create_model(a_				,    pretrained=a_)
				timm_model.eval()

				__a	:  List[str]         =					get_swin_config(a_)
				__a	:  Union[str, Any]         =					SwinForImageClassification(a_)
				model.eval()

				__a	:  List[str]         =					convert_state_dict(timm_model.state_dict()				,    a_)
				model.load_state_dict(a_)

				__a	:  Tuple         =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

				__a	:  Tuple         =					AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''				,    '''-''')))
				__a	:  Tuple         =					Image.open(requests.get(a_				,    stream=a_).raw)
				__a	:  Tuple         =					image_processor(images=a_				,    return_tensors='''pt''')

				__a	:  List[Any]         =					timm_model(inputs['''pixel_values'''])
				__a	:  Optional[Any]         =					model(**a_).logits

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

				print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""")
				model.save_pretrained(a_)

				print(F"""Saving image processor to {pytorch_dump_folder_path}""")
				image_processor.save_pretrained(a_)


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)






"""simple docstring"""






def       __A (     a_  :int = 1_00_00_00)						->						int:
				__a	:  Tuple         =					[i - 1 for i in range(limit + 1)]

				for i in range(2				,    limit + 1):
								if phi[i] == i - 1:
												for j in range(2 * i				,    limit + 1				,    a_):
																phi[j] -= phi[j] // i

				return sum(phi[2 : limit + 1])


if __name__ == "__main__":
		print(solution())