


'''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 lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					SwinaSRConfig()

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

	return config





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







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

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

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

	return name





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		__magic_name__	:   Union[str, Any]					     =					orig_state_dict.pop(lowerCAmelCase__		)

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

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

	return orig_state_dict





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					get_config(lowerCAmelCase__		)
	__magic_name__	:   int					     =					SwinaSRForImageSuperResolution(lowerCAmelCase__		)
	model.eval()

	__magic_name__	:   Tuple					     =					torch.hub.load_state_dict_from_url(lowerCAmelCase__		,		map_location='cpu'		)
	__magic_name__	:   Optional[Any]					     =					convert_state_dict(lowerCAmelCase__		,		lowerCAmelCase__		)
	__magic_name__     ,    __magic_name__	:   Any					     =					model.load_state_dict(lowerCAmelCase__		,		strict=lowerCAmelCase__		)

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

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

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

	__magic_name__	:   str					     =					model(lowerCAmelCase__		)

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

	__magic_name__	:   Dict					     =					{
	    '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'
	    ),
	}
	__magic_name__	:   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(lowerCAmelCase__		)
		print(f'Saving image processor to {pytorch_dump_folder_path}'		)
		processor.save_pretrained(lowerCAmelCase__		)

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


if __name__ == "__main__":
							lowerCAmelCase				: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.''')

							lowerCAmelCase				:List[str]        =      parser.parse_args()
							convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					UNetaDModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					self.dummy_uncond_unet
			__magic_name__	:   str					     =					KarrasVeScheduler()

			__magic_name__	:   List[Any]					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   int					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   str					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2







@slow
@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					'google/ncsnpp-celebahq-256'
			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   int					     =					KarrasVeScheduler()

			__magic_name__	:   str					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe(num_inference_steps=20  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   List[str]					     =					image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			__magic_name__	:   int					     =					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2