



from typing import Any



class 		lowerCamelCase__      :




					'''simple docstring'''



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



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		data
						_lowercase					:       Union[str, Any]       =		None




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



						'''simple docstring'''





						return F'''Node({self.data})'''






class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       str       =		None




					def __iter__(  self				:	List[Any]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.head
						while node:
							yield node.data
							_lowercase					:       str       =		node.next




					def __len__(  self				:	str					)  ->       int:



						'''simple docstring'''





						return sum(1 for _ in self					)




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



						'''simple docstring'''





						return "->".join([str(UpperCamelCase_					) for item in self]					)




					def __getitem__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	int					)  ->       Any:



						'''simple docstring'''





						if not 0 <= index < len(self					):
							raise ValueError('list index out of range.'					)
						for i, node in enumerate(self					):
							if i == index:
								return node
						return None




					def __setitem__(  self				:	Optional[int]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Any					)  ->       None:



						'''simple docstring'''





						if not 0 <= index < len(self					):
							raise ValueError('list index out of range.'					)
						_lowercase					:       Dict       =		self.head
						for _ in range(UpperCamelCase_					):
							_lowercase					:       List[str]       =		current.next
						_lowercase					:       Optional[int]       =		data




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



						'''simple docstring'''





						self.insert_nth(len(self					)     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Any					)  ->       None:



						'''simple docstring'''





						self.insert_nth(0     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Any					)  ->       None:



						'''simple docstring'''





						if not 0 <= index <= len(self					):
							raise IndexError('list index out of range'					)
						_lowercase					:       List[str]       =		Node(UpperCamelCase_					)
						if self.head is None:
							_lowercase					:       List[Any]       =		new_node
						elif index == 0:
							_lowercase					:       Dict       =		self.head  # link new_node to head
							_lowercase					:       List[Any]       =		new_node
						else:
							_lowercase					:       Tuple       =		self.head
							for _ in range(index - 1					):
								_lowercase					:       Tuple       =		temp.next
							_lowercase					:       Union[str, Any]       =		temp.next
							_lowercase					:       List[Any]       =		new_node




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       None:  # print every node data



						'''simple docstring'''





						print(self					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Any:



						'''simple docstring'''





						return self.delete_nth(0					)




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Any:  # delete from tail



						'''simple docstring'''





						return self.delete_nth(len(self					) - 1					)




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	int = 0					)  ->       Any:



						'''simple docstring'''





						if not 0 <= index <= len(self					) - 1:  # test if index is valid
							raise IndexError('List index out of range.'					)
						_lowercase					:       List[Any]       =		self.head  # default first node
						if index == 0:
							_lowercase					:       Union[str, Any]       =		self.head.next
						else:
							_lowercase					:       Dict       =		self.head
							for _ in range(index - 1					):
								_lowercase					:       str       =		temp.next
							_lowercase					:       Any       =		temp.next
							_lowercase					:       Dict       =		temp.next.next
						return delete_node.data




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       bool:



						'''simple docstring'''





						return self.head is None




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       None:



						'''simple docstring'''





						_lowercase					:       List[str]       =		None
						_lowercase					:       Union[str, Any]       =		self.head

						while current:
							# Store the current node's next node.
							_lowercase					:       List[Any]       =		current.next
							# Make the current node's next point backwards
							_lowercase					:       List[Any]       =		prev
							# Make the previous node be the current node
							_lowercase					:       Optional[Any]       =		current
							# Make the current node the next node (to progress iteration)
							_lowercase					:       Optional[Any]       =		next_node
						# Return prev in order to put the head at the end
						_lowercase					:       Any       =		prev







def 						__UpperCamelCase				(							) ->			None:
	_lowercase					:       Optional[Any]       =		LinkedList()
	assert linked_list.is_empty() is True
	assert str(_lowercase					) == ""

	try:
		linked_list.delete_head()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	try:
		linked_list.delete_tail()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	for i in range(10					):
		assert len(_lowercase					) == i
		linked_list.insert_nth(_lowercase,				i + 1					)
	assert str(_lowercase					) == "->".join(str(_lowercase					) for i in range(1,				11					)					)

	linked_list.insert_head(0					)
	linked_list.insert_tail(11					)
	assert str(_lowercase					) == "->".join(str(_lowercase					) for i in range(0,				12					)					)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_nth(9					) == 10
	assert linked_list.delete_tail() == 11
	assert len(_lowercase					) == 9
	assert str(_lowercase					) == "->".join(str(_lowercase					) for i in range(1,				10					)					)

	assert all(linked_list[i] == i + 1 for i in range(0,				9					)					) is True

	for i in range(0,				9					):
		_lowercase					:       int       =		-i
	assert all(linked_list[i] == -i for i in range(0,				9					)					) is True

	linked_list.reverse()
	assert str(_lowercase					) == "->".join(str(_lowercase					) for i in range(-8,				1					)					)







def 						__UpperCamelCase				(							) ->			None:
	_lowercase					:       List[Any]       =		[
	    -9,
	    100,
	    Node(7734_5112					),
	    'dlrow olleH',
	    7,
	    5555,
	    0,
	    -192.5_5555,
	    'Hello, world!',
	    77.9,
	    Node(10					),
	    None,
	    None,
	    12.20,
	]
	_lowercase					:       int       =		LinkedList()

	for i in test_input:
		linked_list.insert_tail(_lowercase					)

	# Check if it's empty or not
	assert linked_list.is_empty() is False
	assert (
	    str(_lowercase					) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
	    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the head
	_lowercase					:       List[str]       =		linked_list.delete_head()
	assert result == -9
	assert (
	    str(_lowercase					) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the tail
	_lowercase					:       int       =		linked_list.delete_tail()
	assert result == 12.2
	assert (
	    str(_lowercase					) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None"
	)

	# Delete a node in specific location in linked list
	_lowercase					:       Dict       =		linked_list.delete_nth(10					)
	assert result is None
	assert (
	    str(_lowercase					) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None"
	)

	# Add a Node instance to its head
	linked_list.insert_head(Node('Hello again, world!'					)					)
	assert (
	    str(_lowercase					)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
	)

	# Add None to its tail
	linked_list.insert_tail(_lowercase					)
	assert (
	    str(_lowercase					)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
	)

	# Reverse the linked list
	linked_list.reverse()
	assert (
	    str(_lowercase					)
	    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
	    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
	)







def 						__UpperCamelCase				(							) ->			Dict:
	from doctest import testmod

	testmod()

	_lowercase					:       Optional[int]       =		LinkedList()
	linked_list.insert_head(input('Inserting 1st at head '					).strip()					)
	linked_list.insert_head(input('Inserting 2nd at head '					).strip()					)
	print('\nPrint list:'					)
	linked_list.print_list()
	linked_list.insert_tail(input('\nInserting 1st at tail '					).strip()					)
	linked_list.insert_tail(input('Inserting 2nd at tail '					).strip()					)
	print('\nPrint list:'					)
	linked_list.print_list()
	print('\nDelete head'					)
	linked_list.delete_head()
	print('Delete tail'					)
	linked_list.delete_tail()
	print('\nPrint list:'					)
	linked_list.print_list()
	print('\nReverse linked list'					)
	linked_list.reverse()
	print('\nPrint list:'					)
	linked_list.print_list()
	print('\nString representation of linked list:'					)
	print(_lowercase					)
	print('\nReading/changing Node data using indexing:'					)
	print(f'''Element at Position 1: {linked_list[1]}'''					)
	_lowercase					:       List[Any]       =		input('Enter New Value: '					).strip()
	print('New list:'					)
	print(_lowercase					)
	print(f'''length of linked_list is : {len(_lowercase					)}'''					)


if __name__ == "__main__":
		main()





'''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				(							_lowercase					) ->			Tuple:
	_lowercase					:       Tuple       =		SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		_lowercase					:       Optional[Any]       =		4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		_lowercase					:       Tuple       =		4
		_lowercase					:       Union[str, Any]       =		48
		_lowercase					:       Any       =		'pixelshuffle_aux'
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		_lowercase					:       Dict       =		[6, 6, 6, 6]
		_lowercase					:       Optional[int]       =		60
		_lowercase					:       List[str]       =		[6, 6, 6, 6]
		_lowercase					:       Dict       =		'pixelshuffledirect'
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		_lowercase					:       str       =		4
		_lowercase					:       str       =		'nearest+conv'
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		_lowercase					:       str       =		1
		_lowercase					:       Tuple       =		1
		_lowercase					:       Dict       =		126
		_lowercase					:       Optional[int]       =		7
		_lowercase					:       List[Any]       =		2_5_5.0
		_lowercase					:       Tuple       =		''

	return config







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

	if name == "norm.weight":
		_lowercase					:       Union[str, Any]       =		'layernorm.weight'
	if name == "norm.bias":
		_lowercase					:       List[Any]       =		'layernorm.bias'

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

	return name







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

		if "qkv" in key:
			_lowercase					:       Tuple       =		key.split('.'					)
			_lowercase					:       Optional[Any]       =		int(key_split[1]					)
			_lowercase					:       Any       =		int(key_split[4]					)
			_lowercase					:       Optional[Any]       =		config.embed_dim

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

	return orig_state_dict







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Union[str, Any]:
	_lowercase					:       Optional[Any]       =		get_config(_lowercase					)
	_lowercase					:       Union[str, Any]       =		SwinaSRForImageSuperResolution(_lowercase					)
	model.eval()

	_lowercase					:       List[Any]       =		torch.hub.load_state_dict_from_url(_lowercase,				map_location='cpu'					)
	_lowercase					:       Any       =		convert_state_dict(_lowercase,				_lowercase					)
	_lowercase  ,    _lowercase					:       str       =		model.load_state_dict(_lowercase,				strict=_lowercase					)

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

	_lowercase					:       Tuple       =		126 if 'Jpeg' in checkpoint_url else 256
	_lowercase					:       List[str]       =		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]					),
	    ]					)
	_lowercase					:       Optional[Any]       =		transforms(_lowercase					).unsqueeze(0					)

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

	_lowercase					:       Optional[int]       =		model(_lowercase					)

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		_lowercase					:       Any       =		torch.Size([1, 3, 512, 512]					)
		_lowercase					:       Tuple       =		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:
		_lowercase					:       Optional[Any]       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       int       =		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
		_lowercase					:       Optional[int]       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       Dict       =		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:
		_lowercase					:       List[str]       =		torch.Size([1, 3, 512, 512]					)
		_lowercase					:       int       =		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:
		_lowercase					:       Any       =		torch.Size([1, 3, 1024, 1024]					)
		_lowercase					:       Union[str, Any]       =		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],				_lowercase,				atol=1E-3					)
	print('Looks ok!'					)

	_lowercase					:       List[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'
	    ),
	}
	_lowercase					:       int       =		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(_lowercase					)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
		processor.save_pretrained(_lowercase					)

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


if __name__ == "__main__":
		_A				:    Dict     						=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.''')

		_A				:    int     						=parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
