



import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					SwinaSRConfig()

							if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														_lowerCAmelCase  :							Optional[int]				     =					4
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														_lowerCAmelCase  :							Optional[int]				     =					4
														_lowerCAmelCase  :							List[Any]				     =					48
														_lowerCAmelCase  :							Optional[int]				     =					"pixelshuffle_aux"
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														_lowerCAmelCase  :							Optional[int]				     =					[6, 6, 6, 6]
														_lowerCAmelCase  :							Union[str, Any]				     =					60
														_lowerCAmelCase  :							str				     =					[6, 6, 6, 6]
														_lowerCAmelCase  :							str				     =					"pixelshuffledirect"
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														_lowerCAmelCase  :							str				     =					4
														_lowerCAmelCase  :							Optional[Any]				     =					"nearest+conv"
							elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
														_lowerCAmelCase  :							Any				     =					1
														_lowerCAmelCase  :							Optional[int]				     =					1
														_lowerCAmelCase  :							Union[str, Any]				     =					126
														_lowerCAmelCase  :							Optional[Any]				     =					7
														_lowerCAmelCase  :							List[str]				     =					2_55.0
														_lowerCAmelCase  :							Optional[int]				     =					""

							return config






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if "patch_embed.proj" in name and "layers" not in name:
														_lowerCAmelCase  :							Optional[int]				     =					name.replace("patch_embed.proj"       ,				"embeddings.patch_embeddings.projection"						)
							if "patch_embed.norm" in name:
														_lowerCAmelCase  :							Optional[Any]				     =					name.replace("patch_embed.norm"       ,				"embeddings.patch_embeddings.layernorm"						)
							if "layers" in name:
														_lowerCAmelCase  :							Optional[Any]				     =					name.replace("layers"       ,				"encoder.stages"						)
							if "residual_group.blocks" in name:
														_lowerCAmelCase  :							int				     =					name.replace("residual_group.blocks"       ,				"layers"						)
							if "attn.proj" in name:
														_lowerCAmelCase  :							Any				     =					name.replace("attn.proj"       ,				"attention.output.dense"						)
							if "attn" in name:
														_lowerCAmelCase  :							str				     =					name.replace("attn"       ,				"attention.self"						)
							if "norm1" in name:
														_lowerCAmelCase  :							int				     =					name.replace("norm1"       ,				"layernorm_before"						)
							if "norm2" in name:
														_lowerCAmelCase  :							Union[str, Any]				     =					name.replace("norm2"       ,				"layernorm_after"						)
							if "mlp.fc1" in name:
														_lowerCAmelCase  :							List[str]				     =					name.replace("mlp.fc1"       ,				"intermediate.dense"						)
							if "mlp.fc2" in name:
														_lowerCAmelCase  :							Optional[Any]				     =					name.replace("mlp.fc2"       ,				"output.dense"						)
							if "q_bias" in name:
														_lowerCAmelCase  :							Optional[Any]				     =					name.replace("q_bias"       ,				"query.bias"						)
							if "k_bias" in name:
														_lowerCAmelCase  :							int				     =					name.replace("k_bias"       ,				"key.bias"						)
							if "v_bias" in name:
														_lowerCAmelCase  :							Union[str, Any]				     =					name.replace("v_bias"       ,				"value.bias"						)
							if "cpb_mlp" in name:
														_lowerCAmelCase  :							Tuple				     =					name.replace("cpb_mlp"       ,				"continuous_position_bias_mlp"						)
							if "patch_embed.proj" in name:
														_lowerCAmelCase  :							Optional[int]				     =					name.replace("patch_embed.proj"       ,				"patch_embed.projection"						)

							if name == "norm.weight":
														_lowerCAmelCase  :							List[str]				     =					"layernorm.weight"
							if name == "norm.bias":
														_lowerCAmelCase  :							Optional[int]				     =					"layernorm.bias"

							if "conv_first" in name:
														_lowerCAmelCase  :							Tuple				     =					name.replace("conv_first"       ,				"first_convolution"						)

							if (
							    "upsample" in name
							    or "conv_before_upsample" in name
							    or "conv_bicubic" in name
							    or "conv_up" in name
							    or "conv_hr" in name
							    or "conv_last" in name
							    or "aux" in name
							):
														# heads
														if "conv_last" in name:
																					_lowerCAmelCase  :							Optional[int]				     =					name.replace("conv_last"       ,				"final_convolution"						)
														if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
																					if "conv_before_upsample.0" in name:
																												_lowerCAmelCase  :							Any				     =					name.replace("conv_before_upsample.0"       ,				"conv_before_upsample"						)
																					if "upsample.0" in name:
																												_lowerCAmelCase  :							Optional[int]				     =					name.replace("upsample.0"       ,				"upsample.convolution_0"						)
																					if "upsample.2" in name:
																												_lowerCAmelCase  :							int				     =					name.replace("upsample.2"       ,				"upsample.convolution_1"						)
																					_lowerCAmelCase  :							Tuple				     =					"upsample." + name
														elif config.upsampler == "pixelshuffledirect":
																					_lowerCAmelCase  :							int				     =					name.replace("upsample.0.weight"       ,				"upsample.conv.weight"						)
																					_lowerCAmelCase  :							Optional[Any]				     =					name.replace("upsample.0.bias"       ,				"upsample.conv.bias"						)
														else:
																					pass
							else:
														_lowerCAmelCase  :							int				     =					"swin2sr." + name

							return name






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase  :							List[str]				     =					orig_state_dict.pop(_lowerCamelCase						)

														if "qkv" in key:
																					_lowerCAmelCase  :							Union[str, Any]				     =					key.split("."						)
																					_lowerCAmelCase  :							Union[str, Any]				     =					int(key_split[1]						)
																					_lowerCAmelCase  :							Tuple				     =					int(key_split[4]						)
																					_lowerCAmelCase  :							Union[str, Any]				     =					config.embed_dim

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

							return orig_state_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					get_config(_lowerCamelCase						)
							_lowerCAmelCase  :							Any				     =					SwinaSRForImageSuperResolution(_lowerCamelCase						)
							model.eval()

							_lowerCAmelCase  :							Tuple				     =					torch.hub.load_state_dict_from_url(_lowerCamelCase       ,				map_location="cpu"						)
							_lowerCAmelCase  :							List[Any]				     =					convert_state_dict(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					model.load_state_dict(_lowerCamelCase       ,				strict=_lowerCamelCase						)

							if len(_lowerCamelCase						) > 0:
														raise ValueError("Missing keys when converting: {}".format(_lowerCamelCase						)						)
							for key in unexpected_keys:
														if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
																					raise ValueError(F"Unexpected key {key} in state_dict"						)

    # verify values
							_lowerCAmelCase  :							str				     =					"https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
							_lowerCAmelCase  :							Dict				     =					Image.open(requests.get(_lowerCamelCase       ,				stream=_lowerCamelCase						).raw						).convert("RGB"						)
							_lowerCAmelCase  :							Dict				     =					SwinaSRImageProcessor()
							# pixel_values = processor(image, return_tensors="pt").pixel_values

							_lowerCAmelCase  :							List[Any]				     =					126 if "Jpeg" in checkpoint_url else 256
							_lowerCAmelCase  :							Optional[Any]				     =					Compose(
							    [
							        Resize((image_size, image_size)						),
							        ToTensor(),
							        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]       ,				std=[0.2_29, 0.2_24, 0.2_25]						),
							    ]						)
							_lowerCAmelCase  :							List[str]				     =					transforms(_lowerCamelCase						).unsqueeze(0						)

							if config.num_channels == 1:
														_lowerCAmelCase  :							str				     =					pixel_values[:, 0, :, :].unsqueeze(1						)

							_lowerCAmelCase  :							int				     =					model(_lowerCamelCase						)

							# assert values
							if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
														_lowerCAmelCase  :							Optional[Any]				     =					torch.Size([1, 3, 512, 512]						)
														_lowerCAmelCase  :							Union[str, Any]				     =					torch.tensor(
														    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]						)
							elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														_lowerCAmelCase  :							List[str]				     =					torch.Size([1, 3, 1_024, 1_024]						)
														_lowerCAmelCase  :							Dict				     =					torch.tensor(
														    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]						)
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														# TODO values didn't match exactly here
														_lowerCAmelCase  :							List[Any]				     =					torch.Size([1, 3, 1_024, 1_024]						)
														_lowerCAmelCase  :							List[Any]				     =					torch.tensor(
														    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]						)
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														_lowerCAmelCase  :							str				     =					torch.Size([1, 3, 512, 512]						)
														_lowerCAmelCase  :							Tuple				     =					torch.tensor(
														    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]						)
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														_lowerCAmelCase  :							Tuple				     =					torch.Size([1, 3, 1_024, 1_024]						)
														_lowerCAmelCase  :							Optional[Any]				     =					torch.tensor(
														    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]						)

							assert (
							    outputs.reconstruction.shape == expected_shape
							), F"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
							assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,				_lowerCamelCase       ,				atol=1e-3						)
							print("Looks ok!"						)

							_lowerCAmelCase  :							str				     =					{
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
							        "swin2SR-classical-sr-x2-64"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
							        "swin2SR-classical-sr-x4-64"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
							        "swin2SR-compressed-sr-x4-48"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
							        "swin2SR-lightweight-x2-64"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
							        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
							    ),
							}
							_lowerCAmelCase  :							Tuple				     =					url_to_name[checkpoint_url]

							if pytorch_dump_folder_path is not None:
														print(F"Saving model {model_name} to {pytorch_dump_folder_path}"						)
														model.save_pretrained(_lowerCamelCase						)
														print(F"Saving image processor to {pytorch_dump_folder_path}"						)
														processor.save_pretrained(_lowerCamelCase						)

							if push_to_hub:
														model.push_to_hub(F"caidas/{model_name}"						)
														processor.push_to_hub(F"caidas/{model_name}"						)


if __name__ == "__main__":
						_snake_case	=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
						    type=str,
						    help="URL of the original Swin2SR checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)
						parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")

						_snake_case	=						parser.parse_args()
						convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase = "https://www.worldometers.info/coronavirus"						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					BeautifulSoup(requests.get(_lowerCamelCase						).text       ,				"html.parser"						)
							_lowerCAmelCase  :							str				     =					soup.findAll("h1"						)
							_lowerCAmelCase  :							Optional[int]				     =					soup.findAll("div"       ,				{"class": "maincounter-number"}						)
							keys += soup.findAll("span"       ,				{"class": "panel-title"}						)
							values += soup.findAll("div"       ,				{"class": "number-table-main"}						)
							return {key.text.strip(): value.text.strip() for key, value in zip(_lowerCamelCase       ,				_lowerCamelCase						)}


if __name__ == "__main__":
						print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
						for key, value in world_covidaa_stats().items():
												print(f'''{key}\n{value}\n''')
