


"""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 		lowercase							(     _snake_case						:      Tuple     )     ->Optional[Any]:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		__snake_case	:						int						=				4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		__snake_case	:						Optional[Any]						=				4
		__snake_case	:						Union[str, Any]						=				48
		__snake_case	:						List[Any]						=				'''pixelshuffle_aux'''
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		__snake_case	:						str						=				[6, 6, 6, 6]
		__snake_case	:						str						=				60
		__snake_case	:						Dict						=				[6, 6, 6, 6]
		__snake_case	:						List[str]						=				'''pixelshuffledirect'''
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		__snake_case	:						int						=				4
		__snake_case	:						List[Any]						=				'''nearest+conv'''
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		__snake_case	:						Optional[Any]						=				1
		__snake_case	:						Union[str, Any]						=				1
		__snake_case	:						Any						=				126
		__snake_case	:						List[Any]						=				7
		__snake_case	:						Tuple						=				255.0
		__snake_case	:						Union[str, Any]						=				''''''

	return config






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Any     )     ->int:
	"""simple docstring"""





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

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

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

	return name






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Dict     )     ->Union[str, Any]:
	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		__snake_case	:						List[Any]						=				orig_state_dict.pop(_snake_case     )

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

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

	return orig_state_dict






def 		lowercase							(     _snake_case						:      Union[str, Any]     ,   _snake_case						:      Dict     ,   _snake_case						:      Union[str, Any]     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				get_config(_snake_case     )
	__snake_case	:						Optional[int]						=				SwinaSRForImageSuperResolution(_snake_case     )
	model.eval()

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

	__snake_case	:						Dict						=				126 if '''Jpeg''' in checkpoint_url else 256
	__snake_case	:						Tuple						=				Compose(
	    [
	        Resize((image_size, image_size)     ),
	        ToTensor(),
	        Normalize(mean=[0.485, 0.456, 0.406]     ,   std=[0.229, 0.224, 0.225]     ),
	    ]     )
	__snake_case	:						Tuple						=				transforms(_snake_case     ).unsqueeze(0     )

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

	__snake_case	:						Dict						=				model(_snake_case     )

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		__snake_case	:						Optional[Any]						=				torch.Size([1, 3, 512, 512]     )
		__snake_case	:						str						=				torch.tensor(
		    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]     )
	elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		__snake_case	:						Tuple						=				torch.Size([1, 3, 1_024, 1_024]     )
		__snake_case	:						int						=				torch.tensor(
		    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]     )
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		# TODO values didn't match exactly here
		__snake_case	:						Optional[Any]						=				torch.Size([1, 3, 1_024, 1_024]     )
		__snake_case	:						Optional[Any]						=				torch.tensor(
		    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]     )
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		__snake_case	:						str						=				torch.Size([1, 3, 512, 512]     )
		__snake_case	:						Tuple						=				torch.tensor(
		    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]     )
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		__snake_case	:						List[Any]						=				torch.Size([1, 3, 1_024, 1_024]     )
		__snake_case	:						List[Any]						=				torch.tensor(
		    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]     )

	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!'''     )

	__snake_case	:						Optional[int]						=				{
	    '''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'''
	    ),
	}
	__snake_case	:						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__":
			SCREAMING_SNAKE_CASE :    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.""")

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









"""simple docstring"""
import math






def 		lowercase							(     _snake_case						:      int     )     ->bool:
	"""simple docstring"""





	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5     ,   int(math.sqrt(_snake_case     ) + 1     )     ,   6     ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True






def 		lowercase							(     _snake_case						:      float = 0.1     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				3
	__snake_case	:						Any						=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1     ,   (j + 2) * (j + 2)     ,   j + 1     ):
			primes += is_prime(_snake_case     )
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()





