


"""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			( _SCREAMING_SNAKE_CASE			)      ->			List[str]:
							snake_case_          =						SwinConfig()
							snake_case_          =						swin_name.split("""_"""			)

							snake_case_          =						name_split[1]
							snake_case_          =						int(name_split[4]			)
							snake_case_          =						int(name_split[3][-1]			)

							if model_size == "tiny":
														snake_case_          =						96
														snake_case_          =						(2, 2, 6, 2)
														snake_case_          =						(3, 6, 12, 24)
							elif model_size == "small":
														snake_case_          =						96
														snake_case_          =						(2, 2, 18, 2)
														snake_case_          =						(3, 6, 12, 24)
							elif model_size == "base":
														snake_case_          =						128
														snake_case_          =						(2, 2, 18, 2)
														snake_case_          =						(4, 8, 16, 32)
							else:
														snake_case_          =						192
														snake_case_          =						(2, 2, 18, 2)
														snake_case_          =						(6, 12, 24, 48)

							if "in22k" in swin_name:
														snake_case_          =						21_841
							else:
														snake_case_          =						1_000
														snake_case_          =						"""huggingface/label-files"""
														snake_case_          =						"""imagenet-1k-id2label.json"""
														snake_case_          =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	repo_type="""dataset"""			)			,	"""r"""			)			)
														snake_case_          =						{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
														snake_case_          =						idalabel
														snake_case_          =						{v: k for k, v in idalabel.items()}

							snake_case_          =						img_size
							snake_case_          =						num_classes
							snake_case_          =						embed_dim
							snake_case_          =						depths
							snake_case_          =						num_heads
							snake_case_          =						window_size

							return config







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							if "patch_embed.proj" in name:
														snake_case_          =						name.replace("""patch_embed.proj"""			,	"""embeddings.patch_embeddings.projection"""			)
							if "patch_embed.norm" in name:
														snake_case_          =						name.replace("""patch_embed.norm"""			,	"""embeddings.norm"""			)
							if "layers" in name:
														snake_case_          =						"""encoder.""" + name
							if "attn.proj" in name:
														snake_case_          =						name.replace("""attn.proj"""			,	"""attention.output.dense"""			)
							if "attn" in name:
														snake_case_          =						name.replace("""attn"""			,	"""attention.self"""			)
							if "norm1" in name:
														snake_case_          =						name.replace("""norm1"""			,	"""layernorm_before"""			)
							if "norm2" in name:
														snake_case_          =						name.replace("""norm2"""			,	"""layernorm_after"""			)
							if "mlp.fc1" in name:
														snake_case_          =						name.replace("""mlp.fc1"""			,	"""intermediate.dense"""			)
							if "mlp.fc2" in name:
														snake_case_          =						name.replace("""mlp.fc2"""			,	"""output.dense"""			)

							if name == "norm.weight":
														snake_case_          =						"""layernorm.weight"""
							if name == "norm.bias":
														snake_case_          =						"""layernorm.bias"""

							if "head" in name:
														snake_case_          =						name.replace("""head"""			,	"""classifier"""			)
							else:
														snake_case_          =						"""swin.""" + name

							return name







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							for key in orig_state_dict.copy().keys():
														snake_case_          =						orig_state_dict.pop(_SCREAMING_SNAKE_CASE			)

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

																					if "weight" in key:
																												snake_case_          =						val[:dim, :]
																												snake_case_          =						val[
																												    dim : dim * 2, :
																												]
																												snake_case_          =						val[-dim:, :]
																					else:
																												snake_case_          =						val[
																												    :dim
																												]
																												snake_case_          =						val[
																												    dim : dim * 2
																												]
																												snake_case_          =						val[
																												    -dim:
																												]
														else:
																					snake_case_          =						val

							return orig_state_dict







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						timm.create_model(_SCREAMING_SNAKE_CASE			,	pretrained=_SCREAMING_SNAKE_CASE			)
							timm_model.eval()

							snake_case_          =						get_swin_config(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						SwinForImageClassification(_SCREAMING_SNAKE_CASE			)
							model.eval()

							snake_case_          =						convert_state_dict(timm_model.state_dict()			,	_SCREAMING_SNAKE_CASE			)
							model.load_state_dict(_SCREAMING_SNAKE_CASE			)

							snake_case_          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

							snake_case_          =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""			,	"""-"""			)			)			)
							snake_case_          =						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,	stream=_SCREAMING_SNAKE_CASE			).raw			)
							snake_case_          =						image_processor(images=_SCREAMING_SNAKE_CASE			,	return_tensors="""pt"""			)

							snake_case_          =						timm_model(inputs["""pixel_values"""]			)
							snake_case_          =						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__":
				__SCREAMING_SNAKE_CASE							:      int						=      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.'
				)

				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      parser.parse_args()
				convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



