





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_(			A   ):
	UpperCAmelCase_								=     SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		UpperCAmelCase_								=     4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		UpperCAmelCase_								=     4
		UpperCAmelCase_								=     48
		UpperCAmelCase_								=     """pixelshuffle_aux"""
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		UpperCAmelCase_								=     [6, 6, 6, 6]
		UpperCAmelCase_								=     60
		UpperCAmelCase_								=     [6, 6, 6, 6]
		UpperCAmelCase_								=     """pixelshuffledirect"""
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		UpperCAmelCase_								=     4
		UpperCAmelCase_								=     """nearest+conv"""
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		UpperCAmelCase_								=     1
		UpperCAmelCase_								=     1
		UpperCAmelCase_								=     126
		UpperCAmelCase_								=     7
		UpperCAmelCase_								=     255.0
		UpperCAmelCase_								=     """"""

	return config




def    A_(			A				,      A   ):
	if "patch_embed.proj" in name and "layers" not in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.proj"""				,      """embeddings.patch_embeddings.projection"""   )
	if "patch_embed.norm" in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.norm"""				,      """embeddings.patch_embeddings.layernorm"""   )
	if "layers" in name:
		UpperCAmelCase_								=     name.replace("""layers"""				,      """encoder.stages"""   )
	if "residual_group.blocks" in name:
		UpperCAmelCase_								=     name.replace("""residual_group.blocks"""				,      """layers"""   )
	if "attn.proj" in name:
		UpperCAmelCase_								=     name.replace("""attn.proj"""				,      """attention.output.dense"""   )
	if "attn" in name:
		UpperCAmelCase_								=     name.replace("""attn"""				,      """attention.self"""   )
	if "norm1" in name:
		UpperCAmelCase_								=     name.replace("""norm1"""				,      """layernorm_before"""   )
	if "norm2" in name:
		UpperCAmelCase_								=     name.replace("""norm2"""				,      """layernorm_after"""   )
	if "mlp.fc1" in name:
		UpperCAmelCase_								=     name.replace("""mlp.fc1"""				,      """intermediate.dense"""   )
	if "mlp.fc2" in name:
		UpperCAmelCase_								=     name.replace("""mlp.fc2"""				,      """output.dense"""   )
	if "q_bias" in name:
		UpperCAmelCase_								=     name.replace("""q_bias"""				,      """query.bias"""   )
	if "k_bias" in name:
		UpperCAmelCase_								=     name.replace("""k_bias"""				,      """key.bias"""   )
	if "v_bias" in name:
		UpperCAmelCase_								=     name.replace("""v_bias"""				,      """value.bias"""   )
	if "cpb_mlp" in name:
		UpperCAmelCase_								=     name.replace("""cpb_mlp"""				,      """continuous_position_bias_mlp"""   )
	if "patch_embed.proj" in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.proj"""				,      """patch_embed.projection"""   )

	if name == "norm.weight":
		UpperCAmelCase_								=     """layernorm.weight"""
	if name == "norm.bias":
		UpperCAmelCase_								=     """layernorm.bias"""

	if "conv_first" in name:
		UpperCAmelCase_								=     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_								=     name.replace("""conv_last"""				,      """final_convolution"""   )
		if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
			if "conv_before_upsample.0" in name:
				UpperCAmelCase_								=     name.replace("""conv_before_upsample.0"""				,      """conv_before_upsample"""   )
			if "upsample.0" in name:
				UpperCAmelCase_								=     name.replace("""upsample.0"""				,      """upsample.convolution_0"""   )
			if "upsample.2" in name:
				UpperCAmelCase_								=     name.replace("""upsample.2"""				,      """upsample.convolution_1"""   )
			UpperCAmelCase_								=     """upsample.""" + name
		elif config.upsampler == "pixelshuffledirect":
			UpperCAmelCase_								=     name.replace("""upsample.0.weight"""				,      """upsample.conv.weight"""   )
			UpperCAmelCase_								=     name.replace("""upsample.0.bias"""				,      """upsample.conv.bias"""   )
		else:
			pass
	else:
		UpperCAmelCase_								=     """swin2sr.""" + name

	return name




def    A_(			A				,      A   ):
	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_								=     orig_state_dict.pop(A   )

		if "qkv" in key:
			UpperCAmelCase_								=     key.split("""."""   )
			UpperCAmelCase_								=     int(key_split[1]   )
			UpperCAmelCase_								=     int(key_split[4]   )
			UpperCAmelCase_								=     config.embed_dim

			if "weight" in key:
				UpperCAmelCase_								=     val[:dim, :]
				UpperCAmelCase_								=     val[dim : dim * 2, :]
				UpperCAmelCase_								=     val[-dim:, :]
			else:
				UpperCAmelCase_								=     val[:dim]
				UpperCAmelCase_								=     val[dim : dim * 2]
				UpperCAmelCase_								=     val[-dim:]
			pass
		else:
			UpperCAmelCase_								=     val

	return orig_state_dict




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     get_config(A   )
	UpperCAmelCase_								=     SwinaSRForImageSuperResolution(A   )
	model.eval()

	UpperCAmelCase_								=     torch.hub.load_state_dict_from_url(A				,      map_location="""cpu"""   )
	UpperCAmelCase_								=     convert_state_dict(A				,      A   )
	UpperCAmelCase_						,	UpperCAmelCase_								=     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
	UpperCAmelCase_								=     """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
	UpperCAmelCase_								=     Image.open(requests.get(A				,      stream=A   ).raw   ).convert("""RGB"""   )
	UpperCAmelCase_								=     SwinaSRImageProcessor()
	# pixel_values = processor(image, return_tensors="pt").pixel_values

	UpperCAmelCase_								=     126 if """Jpeg""" in checkpoint_url else 256
	UpperCAmelCase_								=     Compose(
	    [
	        Resize((image_size, image_size)   ),
	        ToTensor(),
	        Normalize(mean=[0.485, 0.456, 0.406]				,      std=[0.229, 0.224, 0.225]   ),
	    ]   )
	UpperCAmelCase_								=     transforms(A   ).unsqueeze(0   )

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

	UpperCAmelCase_								=     model(A   )

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		UpperCAmelCase_								=     torch.Size([1, 3, 512, 512]   )
		UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     torch.Size([1, 3, 1024, 1024]   )
		UpperCAmelCase_								=     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
		UpperCAmelCase_								=     torch.Size([1, 3, 1024, 1024]   )
		UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     torch.Size([1, 3, 512, 512]   )
		UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     torch.Size([1, 3, 1024, 1024]   )
		UpperCAmelCase_								=     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!"""   )

	UpperCAmelCase_								=     {
	    """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_								=     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__   : List[Any]					  = 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__   : List[Any]					  = parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









from torch import nn







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     class_size
						UpperCAmelCase_								=     embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.mlp(__lowercase		)
						return logits


