





'''simple docstring'''

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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								lowercase         = 4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								lowercase         = 4
								lowercase         = 48
								lowercase         = "pixelshuffle_aux"
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								lowercase         = [6, 6, 6, 6]
								lowercase         = 60
								lowercase         = [6, 6, 6, 6]
								lowercase         = "pixelshuffledirect"
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								lowercase         = 4
								lowercase         = "nearest+conv"
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								lowercase         = 1
								lowercase         = 1
								lowercase         = 126
								lowercase         = 7
								lowercase         = 2_55.0
								lowercase         = ""

				return config







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






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

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

				if "conv_first" in name:
								lowercase         = 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:
												lowercase         = name.replace("conv_last"		,							"final_convolution"							)
								if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
												if "conv_before_upsample.0" in name:
																lowercase         = name.replace("conv_before_upsample.0"		,							"conv_before_upsample"							)
												if "upsample.0" in name:
																lowercase         = name.replace("upsample.0"		,							"upsample.convolution_0"							)
												if "upsample.2" in name:
																lowercase         = name.replace("upsample.2"		,							"upsample.convolution_1"							)
												lowercase         = "upsample." + name
								elif config.upsampler == "pixelshuffledirect":
												lowercase         = name.replace("upsample.0.weight"		,							"upsample.conv.weight"							)
												lowercase         = name.replace("upsample.0.bias"		,							"upsample.conv.bias"							)
								else:
												pass
				else:
								lowercase         = "swin2sr." + name

				return name







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				for key in orig_state_dict.copy().keys():
								lowercase         = orig_state_dict.pop(lowerCAmelCase_							)

								if "qkv" in key:
												lowercase         = key.split("."							)
												lowercase         = int(key_split[1]							)
												lowercase         = int(key_split[4]							)
												lowercase         = config.embed_dim

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

				return orig_state_dict







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = get_config(lowerCAmelCase_							)
				lowercase         = SwinaSRForImageSuperResolution(lowerCAmelCase_							)
				model.eval()

				lowercase         = torch.hub.load_state_dict_from_url(lowerCAmelCase_		,							map_location="cpu"							)
				lowercase         = convert_state_dict(lowerCAmelCase_		,							lowerCAmelCase_							)
				lowercase   ,							lowercase         = 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
				lowercase         = "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
				lowercase         = Image.open(requests.get(lowerCAmelCase_		,							stream=lowerCAmelCase_							).raw							).convert("RGB"							)
				lowercase         = SwinaSRImageProcessor()
				# pixel_values = processor(image, return_tensors="pt").pixel_values

				lowercase         = 126 if "Jpeg" in checkpoint_url else 256
				lowercase         = 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]							),
				    ]							)
				lowercase         = transforms(lowerCAmelCase_							).unsqueeze(0							)

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

				lowercase         = model(lowerCAmelCase_							)

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								lowercase         = torch.Size([1, 3, 512, 512]							)
								lowercase         = 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:
								lowercase         = torch.Size([1, 3, 1024, 1024]							)
								lowercase         = 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
								lowercase         = torch.Size([1, 3, 1024, 1024]							)
								lowercase         = 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:
								lowercase         = torch.Size([1, 3, 512, 512]							)
								lowercase         = 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:
								lowercase         = torch.Size([1, 3, 1024, 1024]							)
								lowercase         = 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!"							)

				lowercase         = {
				    "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"
				    ),
				}
				lowercase         = 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__":
			__lowerCamelCase : List[Any]											=			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.")

			__lowerCamelCase : Any											=			parser.parse_args()
			convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''

from functools import lru_cache







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = 2
				lowercase         = set()
				while i * i <= n:
								if n % i:
												i += 1
								else:
												n //= i
												factors.add(lowerCAmelCase_							)
				if n > 1:
								factors.add(lowerCAmelCase_							)
				return factors







@lru_cache
def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return len(unique_prime_factors(lowerCAmelCase_							)							)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return len(set(lowerCAmelCase_							)							) in (0, 1)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = 2

				while True:
								# Increment each value of a generated range
								lowercase         = [base + i for i in range(lowerCAmelCase_							)]

								# Run elements through out unique_prime_factors function
								# Append our target number to the end.
								lowercase         = [upf_len(lowerCAmelCase_							) for x in group]
								checker.append(lowerCAmelCase_							)

								# If all numbers in the list are equal, return the group variable.
								if equality(lowerCAmelCase_							):
												return group

								# Increment our base variable by 1
								base += 1







def 							UpperCAmelCase_    (	lowerCAmelCase_ = 4							):

				"""simple docstring"""






				lowercase         = run(lowerCAmelCase_							)
				return results[0] if len(lowerCAmelCase_							) else None


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




