






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__ (__lowercase							:Optional[Any]   )		-> Any:
				_A	:				Any   =      SwinaSRConfig()

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

				return config



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

				if name == "norm.weight":
								_A	:				Tuple   =      '''layernorm.weight'''
				if name == "norm.bias":
								_A	:				Dict   =      '''layernorm.bias'''

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

				return name



def  a__ (__lowercase							:List[str]					,  __lowercase							:Dict   )		-> Optional[Any]:
				for key in orig_state_dict.copy().keys():
								_A	:				List[Any]   =      orig_state_dict.pop(__lowercase   )

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

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

				return orig_state_dict



def  a__ (__lowercase							:Any					,  __lowercase							:str					,  __lowercase							:Optional[int]   )		-> List[str]:
				_A	:				int   =      get_config(__lowercase   )
				_A	:				Dict   =      SwinaSRForImageSuperResolution(__lowercase   )
				model.eval()

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

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

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

				_A	:				Union[str, Any]   =      model(__lowercase   )

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

				_A	:				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'''
				    ),
				}
				_A	:				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(__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__":
	_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			:	Any								=parser.parse_args()
	convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











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


_UpperCamelCase			:	Optional[int]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   "canine"







		def __init__(					self       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=16384       ,A__=16       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__=0xE_000       ,A__=0xE_001       ,A__=4       ,A__=4       ,A__=8       ,A__=16384       ,A__=128       ,**A__       ,):
						super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__		)

						_A	:				int   =      max_position_embeddings
						_A	:				Dict   =      hidden_size
						_A	:				List[str]   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Optional[Any]   =      intermediate_size
						_A	:				Optional[Any]   =      hidden_act
						_A	:				str   =      hidden_dropout_prob
						_A	:				Optional[int]   =      attention_probs_dropout_prob
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Dict   =      type_vocab_size
						_A	:				Optional[Any]   =      layer_norm_eps

						# Character config:
						_A	:				Union[str, Any]   =      downsampling_rate
						_A	:				List[str]   =      upsampling_kernel_size
						_A	:				str   =      num_hash_functions
						_A	:				Optional[Any]   =      num_hash_buckets
						_A	:				Optional[Any]   =      local_transformer_stride



