

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    __UpperCAmelCase (	__a      :			Tuple	)			->     int:

					"""simple docstring"""

					_a	:    Any         =		SwinConfig()
					_a	:    Union[str, Any]         =		swin_name.split('''_'''	)

					_a	:    Optional[int]         =		name_split[1]
					_a	:    Dict         =		int(name_split[4]	)
					_a	:    Dict         =		int(name_split[3][-1]	)

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

					if "in22k" in swin_name:
										_a	:    Optional[int]         =		21_841
					else:
										_a	:    Union[str, Any]         =		1_000
										_a	:    List[Any]         =		'''huggingface/label-files'''
										_a	:    Tuple         =		'''imagenet-1k-id2label.json'''
										_a	:    Optional[Any]         =		json.load(open(hf_hub_download(__a		,__a		,repo_type='''dataset'''	)		,'''r'''	)	)
										_a	:    Tuple         =		{int(__a	): v for k, v in idalabel.items()}
										_a	:    Union[str, Any]         =		idalabel
										_a	:    Any         =		{v: k for k, v in idalabel.items()}

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

					return config



def    __UpperCAmelCase (	__a      :			Dict	)			->     int:

					"""simple docstring"""

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

					if name == "norm.weight":
										_a	:    List[Any]         =		'''layernorm.weight'''
					if name == "norm.bias":
										_a	:    List[str]         =		'''layernorm.bias'''

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

					return name



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			Tuple	)			->     Any:

					"""simple docstring"""

					for key in orig_state_dict.copy().keys():
										_a	:    Optional[int]         =		orig_state_dict.pop(__a	)

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

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

					return orig_state_dict



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			List[str]	)			->     str:

					"""simple docstring"""

					_a	:    Optional[int]         =		timm.create_model(__a		,pretrained=__a	)
					timm_model.eval()

					_a	:    List[Any]         =		get_swin_config(__a	)
					_a	:    Tuple         =		SwinForImageClassification(__a	)
					model.eval()

					_a	:    Optional[int]         =		convert_state_dict(timm_model.state_dict()		,__a	)
					model.load_state_dict(__a	)

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

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

					_a	:    Optional[int]         =		timm_model(inputs['''pixel_values''']	)
					_a	:    List[str]         =		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)






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



