






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 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = SwinaSRConfig()

						if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												UpperCamelCase__         = 4
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												UpperCamelCase__         = 4
												UpperCamelCase__         = 48
												UpperCamelCase__         = """pixelshuffle_aux"""
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												UpperCamelCase__         = [6, 6, 6, 6]
												UpperCamelCase__         = 60
												UpperCamelCase__         = [6, 6, 6, 6]
												UpperCamelCase__         = """pixelshuffledirect"""
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												UpperCamelCase__         = 4
												UpperCamelCase__         = """nearest+conv"""
						elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
												UpperCamelCase__         = 1
												UpperCamelCase__         = 1
												UpperCamelCase__         = 126
												UpperCamelCase__         = 7
												UpperCamelCase__         = 255.0
												UpperCamelCase__         = """"""

						return config







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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

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

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

						return name







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						for key in orig_state_dict.copy().keys():
												UpperCamelCase__         = orig_state_dict.pop(_A					)

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

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

						return orig_state_dict







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = get_config(_A					)
						UpperCamelCase__         = SwinaSRForImageSuperResolution(_A					)
						model.eval()

						UpperCamelCase__         = torch.hub.load_state_dict_from_url(_A						,      map_location="""cpu"""					)
						UpperCamelCase__         = convert_state_dict(_A						,      _A					)
						UpperCamelCase__    ,   UpperCamelCase__         = model.load_state_dict(_A						,      strict=_A					)

						if len(_A					) > 0:
												raise ValueError("""Missing keys when converting: {}""".format(_A					)					)
						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
						UpperCamelCase__         = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
						UpperCamelCase__         = Image.open(requests.get(_A						,      stream=_A					).raw					).convert("""RGB"""					)
						UpperCamelCase__         = SwinaSRImageProcessor()
						# pixel_values = processor(image, return_tensors="pt").pixel_values

						UpperCamelCase__         = 126 if """Jpeg""" in checkpoint_url else 256
						UpperCamelCase__         = Compose(
						    [
						        Resize((image_size, image_size)					),
						        ToTensor(),
						        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]						,      std=[0.2_2_9, 0.2_2_4, 0.2_2_5]					),
						    ]					)
						UpperCamelCase__         = transforms(_A					).unsqueeze(0					)

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

						UpperCamelCase__         = model(_A					)

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												UpperCamelCase__         = torch.Size([1, 3, 512, 512]					)
												UpperCamelCase__         = torch.tensor(
												    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]					)
						elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												UpperCamelCase__         = torch.Size([1, 3, 1024, 1024]					)
												UpperCamelCase__         = torch.tensor(
												    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]					)
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												# TODO values didn't match exactly here
												UpperCamelCase__         = torch.Size([1, 3, 1024, 1024]					)
												UpperCamelCase__         = torch.tensor(
												    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]					)
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												UpperCamelCase__         = torch.Size([1, 3, 512, 512]					)
												UpperCamelCase__         = torch.tensor(
												    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]					)
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												UpperCamelCase__         = torch.Size([1, 3, 1024, 1024]					)
												UpperCamelCase__         = torch.tensor(
												    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]					)

						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]						,      _A						,      atol=1e-3					)
						print("""Looks ok!"""					)

						UpperCamelCase__         = {
						    """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"""
						    ),
						}
						UpperCamelCase__         = 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(_A					)
												print(f"Saving image processor to {pytorch_dump_folder_path}"					)
												processor.save_pretrained(_A					)

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


if __name__ == "__main__":
					lowerCamelCase_				: Union[str, 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_				: Union[str, Any]    	=							parser.parse_args()
					convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowerCamelCase_				: List[str]    	=							TypeVar('''T''')
lowerCamelCase_				: Optional[int]    	=							TypeVar('''U''')
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Dict ,      lowercase							:						T | None ,      lowercase							:						U | None			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = key
									UpperCamelCase__         = val
									UpperCamelCase__         = None
									UpperCamelCase__         = None







			def __repr__(   self							:						List[Any]			)							->    str:

									'''simple docstring'''







									return (
									    f"Node: key: {self.key}, val: {self.val}, "
									    f"has next: {bool(self.next			)}, has prev: {bool(self.prev			)}"
									)
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any]			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.rear, self.head






			def __repr__(   self							:						int			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = ["""DoubleLinkedList"""]
									UpperCamelCase__         = self.head
									while node.next is not None:
															rep.append(str(lowercase			)			)
															UpperCamelCase__         = node.next
									rep.append(str(self.rear			)			)
									return ",\n    ".join(lowercase			)






			def 				A      (   self							:						str ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = self.rear.prev

									# All nodes other than self.head are guaranteed to have non-None previous
									assert previous is not None

									UpperCamelCase__         = node
									UpperCamelCase__         = previous
									UpperCamelCase__         = node
									UpperCamelCase__         = self.rear







			def 				A      (   self							:						Any ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    DoubleLinkedListNode[T, U] | None:

									'''simple docstring'''







									if node.prev is None or node.next is None:
															return None

									UpperCamelCase__         = node.next
									UpperCamelCase__         = node.prev
									UpperCamelCase__         = None
									UpperCamelCase__         = None
									return node
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			__a							:							dict[Callable[[T], U], LRUCache[T, U]]					     =					{}






			def __init__(   self							:						int ,      lowercase							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = DoubleLinkedList()
									UpperCamelCase__         = capacity
									UpperCamelCase__         = 0
									UpperCamelCase__         = 0
									UpperCamelCase__         = 0
									UpperCamelCase__         = {}






			def __repr__(   self							:						Any			)							->    str:

									'''simple docstring'''







									return (
									    f"CacheInfo(hits={self.hits}, misses={self.miss}, "
									    f"capacity={self.capacity}, current size={self.num_keys})"
									)






			def __contains__(   self							:						Any ,      lowercase							:						T			)							->    bool:

									'''simple docstring'''







									return key in self.cache






			def 				A      (   self							:						Tuple ,      lowercase							:						T			)							->    U | None:

									'''simple docstring'''







									if key in self.cache:
															self.hits += 1
															UpperCamelCase__         = self.cache[key]
															UpperCamelCase__         = self.list.remove(self.cache[key]			)
															assert node == value_node

															# node is guaranteed not None because it is in self.cache
															assert node is not None
															self.list.add(lowercase			)
															return node.val
									self.miss += 1
									return None






			def 				A      (   self							:						Dict ,      lowercase							:						T ,      lowercase							:						U			)							->    None:

									'''simple docstring'''







									if key not in self.cache:
															if self.num_keys >= self.capacity:
																					# delete first node (oldest) when over capacity
																					UpperCamelCase__         = self.list.head.next

																					# guaranteed to have a non-None first node when num_keys > 0
																					# explain to type checker via assertions
																					assert first_node is not None
																					assert first_node.key is not None
																					assert (
																					    self.list.remove(lowercase			) is not None
																					)  # node guaranteed to be in list assert node.key is not None

																					del self.cache[first_node.key]
																					self.num_keys -= 1
															UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
															self.list.add(self.cache[key]			)
															self.num_keys += 1

									else:
															# bump node to the end of the list, update value
															UpperCamelCase__         = self.list.remove(self.cache[key]			)
															assert node is not None  # node guaranteed to be in list
															UpperCamelCase__         = value
															self.list.add(lowercase			)







			@classmethod
			def 				A      (   cls							:						Optional[int] ,      lowercase							:						int = 1_2_8			)							->    Callable[[Callable[[T], U]], Callable[..., U]]:

									'''simple docstring'''







									def cache_decorator_inner(lowercase							:						Callable[[T], U]			) -> Callable[..., U]:
															def cache_decorator_wrapper(*lowercase							:						T			) -> U:
																					if func not in cls.decorator_function_to_instance_map:
																											UpperCamelCase__         = LRUCache(lowercase			)

																					UpperCamelCase__         = cls.decorator_function_to_instance_map[func].get(args[0]			)
																					if result is None:
																											UpperCamelCase__         = func(*lowercase			)
																											cls.decorator_function_to_instance_map[func].put(args[0] ,      lowercase			)
																					return result

															def cache_info() -> LRUCache[T, U]:
																					return cls.decorator_function_to_instance_map[func]

															setattr(lowercase ,      """cache_info""" ,      lowercase			)  # noqa: B010

															return cache_decorator_wrapper

									return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()
