





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  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					__A	:						Dict       =				SwinaSRConfig()

					if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
										__A	:						Optional[Any]       =				4
					elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
										__A	:						Optional[int]       =				4
										__A	:						List[Any]       =				48
										__A	:						Optional[Any]       =				'pixelshuffle_aux'
					elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
										__A	:						Any       =				[6, 6, 6, 6]
										__A	:						List[Any]       =				60
										__A	:						Union[str, Any]       =				[6, 6, 6, 6]
										__A	:						str       =				'pixelshuffledirect'
					elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
										__A	:						List[str]       =				4
										__A	:						Union[str, Any]       =				'nearest+conv'
					elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
										__A	:						Any       =				1
										__A	:						List[str]       =				1
										__A	:						Optional[Any]       =				1_26
										__A	:						Dict       =				7
										__A	:						int       =				255.0
										__A	:						Dict       =				''

					return config







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

					if name == "norm.weight":
										__A	:						Optional[int]       =				'layernorm.weight'
					if name == "norm.bias":
										__A	:						Optional[Any]       =				'layernorm.bias'

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

					return name







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      int:
					for key in orig_state_dict.copy().keys():
										__A	:						List[str]       =				orig_state_dict.pop(a  )

										if "qkv" in key:
															__A	:						List[Any]       =				key.split('.'  )
															__A	:						Optional[Any]       =				int(key_split[1]  )
															__A	:						Union[str, Any]       =				int(key_split[4]  )
															__A	:						Union[str, Any]       =				config.embed_dim

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

					return orig_state_dict







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Any:
					__A	:						Optional[int]       =				get_config(a  )
					__A	:						str       =				SwinaSRForImageSuperResolution(a  )
					model.eval()

					__A	:						Optional[Any]       =				torch.hub.load_state_dict_from_url(a					,						map_location='cpu'  )
					__A	:						Any       =				convert_state_dict(a					,						a  )
					__A    ,				__A	:						List[Any]       =				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
					__A	:						Union[str, Any]       =				'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
					__A	:						Dict       =				Image.open(requests.get(a					,						stream=a  ).raw  ).convert('RGB'  )
					__A	:						Tuple       =				SwinaSRImageProcessor()
					# pixel_values = processor(image, return_tensors="pt").pixel_values

					__A	:						Any       =				1_26 if 'Jpeg' in checkpoint_url else 2_56
					__A	:						List[str]       =				Compose(
					    [
					        Resize((image_size, image_size)  ),
					        ToTensor(),
					        Normalize(mean=[0.485, 0.456, 0.406]					,						std=[0.229, 0.224, 0.225]  ),
					    ]  )
					__A	:						Tuple       =				transforms(a  ).unsqueeze(0  )

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

					__A	:						str       =				model(a  )

					# assert values
					if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
										__A	:						List[Any]       =				torch.Size([1, 3, 5_12, 5_12]  )
										__A	:						Optional[int]       =				torch.tensor(
										    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]  )
					elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
										__A	:						List[str]       =				torch.Size([1, 3, 10_24, 10_24]  )
										__A	:						Optional[int]       =				torch.tensor(
										    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]  )
					elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
										# TODO values didn't match exactly here
										__A	:						Union[str, Any]       =				torch.Size([1, 3, 10_24, 10_24]  )
										__A	:						Optional[int]       =				torch.tensor(
										    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]  )
					elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
										__A	:						Optional[Any]       =				torch.Size([1, 3, 5_12, 5_12]  )
										__A	:						Dict       =				torch.tensor(
										    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]  )
					elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
										__A	:						Optional[int]       =				torch.Size([1, 3, 10_24, 10_24]  )
										__A	:						Any       =				torch.tensor(
										    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]  )

					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!'  )

					__A	:						Optional[Any]       =				{
					    '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'
					    ),
					}
					__A	:						List[Any]       =				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__":
				UpperCAmelCase						:   str			  =     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.''')

				UpperCAmelCase						:   Optional[Any]			  =     parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A = None     ):
									if components is None:
														__A	:						int       =				[]
									__A	:						Tuple       =				list(_A     )



				def __len__(			self     ):
									return len(self.__components     )



				def __str__(			self     ):
									return "(" + ",".join(map(_A     ,					self.__components     )     ) + ")"



				def __add__(			self     ,					_A     ):
									__A	:						Optional[int]       =				len(self     )
									if size == len(_A     ):
														__A	:						Any       =				[self.__components[i] + other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:
														raise Exception('must have the same size'     )



				def __sub__(			self     ,					_A     ):
									__A	:						Tuple       =				len(self     )
									if size == len(_A     ):
														__A	:						Union[str, Any]       =				[self.__components[i] - other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:  # error case
														raise Exception('must have the same size'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					(float, int)     ):
														__A	:						str       =				[c * other for c in self.__components]
														return Vector(_A     )
									elif isinstance(_A     ,					_A     ) and len(self     ) == len(_A     ):
														__A	:						Union[str, Any]       =				len(self     )
														__A	:						Dict       =				[self.__components[i] * other.component(_A     ) for i in range(_A     )]
														return sum(_A     )
									else:  # error case
														raise Exception('invalid operand!'     )



				def 					UpperCAmelCase_		(			self     ):
									return Vector(self.__components     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ) and -len(self.__components     ) <= i < len(self.__components     ):
														return self.__components[i]
									else:
														raise Exception('index out of range'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									assert -len(self.__components     ) <= pos < len(self.__components     )
									__A	:						Optional[int]       =				value



				def 					UpperCAmelCase_		(			self     ):
									if len(self.__components     ) == 0:
														raise Exception('Vector is empty'     )
									__A	:						Optional[Any]       =				[c**2 for c in self.__components]
									return math.sqrt(sum(_A     )     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ):
									__A	:						Optional[Any]       =				self * other
									__A	:						Optional[Any]       =				self.euclidean_length() * other.euclidean_length()
									if deg:
														return math.degrees(math.acos(num / den     )     )
									else:
														return math.acos(num / den     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Vector:
					assert isinstance(a					,						a  )
					return Vector([0] * dimension  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Vector:
					assert isinstance(a					,						a  ) and (isinstance(a					,						a  ))
					__A	:						Optional[Any]       =				[0] * dimension
					__A	:						Tuple       =				1
					return Vector(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					assert (
					    isinstance(a					,						a  )
					    and isinstance(a					,						a  )
					    and (isinstance(a					,						(int, float)  ))
					)
					return x * scalar + y







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					random.seed(a  )
					__A	:						str       =				[random.randint(a					,						a  ) for _ in range(a  )]
					return Vector(a  )





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				matrix
									__A	:						Dict       =				w
									__A	:						Optional[int]       =				h



				def __str__(			self     ):
									__A	:						Tuple       =				''
									for i in range(self.__height     ):
														ans += "|"
														for j in range(self.__width     ):
																			if j < self.__width - 1:
																								ans += str(self.__matrix[i][j]     ) + ","
																			else:
																								ans += str(self.__matrix[i][j]     ) + "|\n"
									return ans



				def __add__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Optional[Any]       =				[]
														for i in range(self.__height     ):
																			__A	:						Optional[Any]       =				[
																			    self.__matrix[i][j] + other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrix must have the same dimension!'     )



				def __sub__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Tuple       =				[]
														for i in range(self.__height     ):
																			__A	:						str       =				[
																			    self.__matrix[i][j] - other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrices must have the same dimension!'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ):  # matrix-vector
														if len(_A     ) == self.__width:
																			__A	:						List[Any]       =				zero_vector(self.__height     )
																			for i in range(self.__height     ):
																								__A	:						List[str]       =				[
																								    self.__matrix[i][j] * other.component(_A     )
																								    for j in range(self.__width     )
																								]
																								ans.change_component(_A     ,					sum(_A     )     )
																			return ans
														else:
																			raise Exception(
																			    'vector must have the same size as the '
																			    'number of columns of the matrix!'     )
									elif isinstance(_A     ,					(int, float)     ):  # matrix-scalar
														__A	:						List[str]       =				[
														    [self.__matrix[i][j] * other for j in range(self.__width     )]
														    for i in range(self.__height     )
														]
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									return None



				def 					UpperCAmelCase_		(			self     ):
									return self.__height



				def 					UpperCAmelCase_		(			self     ):
									return self.__width



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														__A	:						int       =				value
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									__A	:						List[str]       =				self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(_A     )     ):
														__A	:						Optional[int]       =				minor[i][:y] + minor[i][y + 1 :]
									return Matrix(_A     ,					self.__width - 1     ,					self.__height - 1     ).determinant()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(_A     ,					_A     )
									else:
														raise Exception('Indices out of bounds'     )





				def 					UpperCAmelCase_		(			self     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if self.__height < 1:
														raise Exception('Matrix has no element'     )
									elif self.__height == 1:
														return self.__matrix[0][0]
									elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
									else:
														__A	:						List[str]       =				[
														    self.__matrix[0][y] * self.cofactor(0     ,					_A     ) for y in range(self.__width     )
														]
														return sum(_A     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Matrix:
					__A	:						list[list[float]]       =				[[0] * n for _ in range(a  )]
					return Matrix(a					,						a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Matrix:
					random.seed(a  )
					__A	:						list[list[float]]       =				[
					    [random.randint(a					,						a  ) for _ in range(a  )] for _ in range(a  )
					]
					return Matrix(a					,						a					,						a  )




