


"""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 				A_       ( snake_case__		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	SwinaSRConfig()

			if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
						_UpperCamelCase					:List[str]					     =	4
			elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
						_UpperCamelCase					:Tuple					     =	4
						_UpperCamelCase					:Any					     =	48
						_UpperCamelCase					:str					     =	'''pixelshuffle_aux'''
			elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
						_UpperCamelCase					:List[Any]					     =	[6, 6, 6, 6]
						_UpperCamelCase					:List[str]					     =	60
						_UpperCamelCase					:List[str]					     =	[6, 6, 6, 6]
						_UpperCamelCase					:Union[str, Any]					     =	'''pixelshuffledirect'''
			elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
						_UpperCamelCase					:List[Any]					     =	4
						_UpperCamelCase					:Any					     =	'''nearest+conv'''
			elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
						_UpperCamelCase					:Dict					     =	1
						_UpperCamelCase					:Any					     =	1
						_UpperCamelCase					:Union[str, Any]					     =	1_26
						_UpperCamelCase					:Any					     =	7
						_UpperCamelCase					:Optional[Any]					     =	2_55.0
						_UpperCamelCase					:List[str]					     =	''''''

			return config





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

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

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

			return name





def 				A_       ( snake_case__       ,					snake_case__		)					->  Tuple:
			for key in orig_state_dict.copy().keys():
						_UpperCamelCase					:List[Any]					     =	orig_state_dict.pop(snake_case__		)

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

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

			return orig_state_dict





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:Optional[int]					     =	get_config(snake_case__		)
			_UpperCamelCase					:Union[str, Any]					     =	SwinaSRForImageSuperResolution(snake_case__		)
			model.eval()

			_UpperCamelCase					:Any					     =	torch.hub.load_state_dict_from_url(snake_case__       ,					map_location='''cpu'''		)
			_UpperCamelCase					:int					     =	convert_state_dict(snake_case__       ,					snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:int					     =	model.load_state_dict(snake_case__       ,					strict=snake_case__		)

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

			_UpperCamelCase					:Tuple					     =	1_26 if '''Jpeg''' in checkpoint_url else 2_56
			_UpperCamelCase					:Dict					     =	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]		),
			    ]		)
			_UpperCamelCase					:Dict					     =	transforms(snake_case__		).unsqueeze(0		)

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

			_UpperCamelCase					:List[Any]					     =	model(snake_case__		)

			# assert values
			if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
						_UpperCamelCase					:Tuple					     =	torch.Size([1, 3, 5_12, 5_12]		)
						_UpperCamelCase					:str					     =	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:
						_UpperCamelCase					:List[str]					     =	torch.Size([1, 3, 10_24, 10_24]		)
						_UpperCamelCase					:int					     =	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
						_UpperCamelCase					:Tuple					     =	torch.Size([1, 3, 10_24, 10_24]		)
						_UpperCamelCase					:Optional[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:
						_UpperCamelCase					:List[str]					     =	torch.Size([1, 3, 5_12, 5_12]		)
						_UpperCamelCase					:Any					     =	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:
						_UpperCamelCase					:Tuple					     =	torch.Size([1, 3, 10_24, 10_24]		)
						_UpperCamelCase					:List[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]       ,					snake_case__       ,					atol=1E-3		)
			print('''Looks ok!'''		)

			_UpperCamelCase					: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'''
			    ),
			}
			_UpperCamelCase					:Optional[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(snake_case__		)
						print(f"Saving image processor to {pytorch_dump_folder_path}"		)
						processor.save_pretrained(snake_case__		)

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


if __name__ == "__main__":
					UpperCamelCase__				:int			   =	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__				:Tuple			   =	parser.parse_args()
					convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__				:str			   =	logging.get_logger(__name__)

UpperCamelCase__				:Optional[int]			   =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"markuplm"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3_05_22					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=30_72					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=1E-12					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2_56					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=2_16					,				SCREAMING_SNAKE_CASE__=10_01					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=50					,				SCREAMING_SNAKE_CASE__="absolute"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__(
					    pad_token_id=SCREAMING_SNAKE_CASE__					,				bos_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:Any					     =	vocab_size
					_UpperCamelCase					:Union[str, Any]					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	num_hidden_layers
					_UpperCamelCase					:int					     =	num_attention_heads
					_UpperCamelCase					:Tuple					     =	hidden_act
					_UpperCamelCase					:str					     =	intermediate_size
					_UpperCamelCase					:Optional[int]					     =	hidden_dropout_prob
					_UpperCamelCase					:Any					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Union[str, Any]					     =	max_position_embeddings
					_UpperCamelCase					:Dict					     =	type_vocab_size
					_UpperCamelCase					:int					     =	initializer_range
					_UpperCamelCase					:Optional[int]					     =	layer_norm_eps
					_UpperCamelCase					:Any					     =	position_embedding_type
					_UpperCamelCase					:Any					     =	use_cache
					_UpperCamelCase					:List[str]					     =	classifier_dropout
					# additional properties
					_UpperCamelCase					:Union[str, Any]					     =	max_depth
					_UpperCamelCase					:Union[str, Any]					     =	max_xpath_tag_unit_embeddings
					_UpperCamelCase					:Optional[Any]					     =	max_xpath_subs_unit_embeddings
					_UpperCamelCase					:int					     =	tag_pad_id
					_UpperCamelCase					:str					     =	subs_pad_id
					_UpperCamelCase					:List[str]					     =	xpath_unit_hidden_size

