"""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_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       lowercase__			:		str       =	SwinaSRConfig()

       if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              lowercase__			:		Any       =	4
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              lowercase__			:		Union[str, Any]       =	4
              lowercase__			:		int       =	48
              lowercase__			:		int       =	'pixelshuffle_aux'
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              lowercase__			:		List[str]       =	[6, 6, 6, 6]
              lowercase__			:		Union[str, Any]       =	60
              lowercase__			:		Union[str, Any]       =	[6, 6, 6, 6]
              lowercase__			:		Tuple       =	'pixelshuffledirect'
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              lowercase__			:		Dict       =	4
              lowercase__			:		Union[str, Any]       =	'nearest+conv'
       elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
              lowercase__			:		Union[str, Any]       =	1
              lowercase__			:		Optional[int]       =	1
              lowercase__			:		Any       =	126
              lowercase__			:		Tuple       =	7
              lowercase__			:		Dict       =	2_5_5.0
              lowercase__			:		Optional[Any]       =	''

       return config





def 				a_     (      _lowerCAmelCase  : Optional[Any]   , _lowerCAmelCase  : Tuple     ):





       '''simple docstring'''

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

       if name == "norm.weight":
              lowercase__			:		Optional[int]       =	'layernorm.weight'
       if name == "norm.bias":
              lowercase__			:		Union[str, Any]       =	'layernorm.bias'

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

       return name





def 				a_     (      _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : List[Any]     ):





       '''simple docstring'''

       for key in orig_state_dict.copy().keys():
              lowercase__			:		List[str]       =	orig_state_dict.pop(_lowerCAmelCase     )

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

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

       return orig_state_dict





def 				a_     (      _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Union[str, Any]     ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	get_config(_lowerCAmelCase     )
       lowercase__			:		Optional[int]       =	SwinaSRForImageSuperResolution(_lowerCAmelCase     )
       model.eval()

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

       lowercase__			:		int       =	126 if 'Jpeg' in checkpoint_url else 256
       lowercase__			:		List[str]       =	Compose(
           [
               Resize((image_size, image_size)     ),
               ToTensor(),
               Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]   , std=[0.2_2_9, 0.2_2_4, 0.2_2_5]     ),
           ]     )
       lowercase__			:		str       =	transforms(_lowerCAmelCase     ).unsqueeze(0     )

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

       lowercase__			:		str       =	model(_lowerCAmelCase     )

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              lowercase__			:		Dict       =	torch.Size([1, 3, 512, 512]     )
              lowercase__			:		Optional[int]       =	torch.tensor(
                  [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]     )
       elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              lowercase__			:		Any       =	torch.Size([1, 3, 1024, 1024]     )
              lowercase__			:		str       =	torch.tensor(
                  [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]     )
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              # TODO values didn't match exactly here
              lowercase__			:		Tuple       =	torch.Size([1, 3, 1024, 1024]     )
              lowercase__			:		str       =	torch.tensor(
                  [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]     )
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              lowercase__			:		Union[str, Any]       =	torch.Size([1, 3, 512, 512]     )
              lowercase__			:		str       =	torch.tensor(
                  [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]     )
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              lowercase__			:		Dict       =	torch.Size([1, 3, 1024, 1024]     )
              lowercase__			:		Any       =	torch.tensor(
                  [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]     )

       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!'     )

       lowercase__			:		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'
           ),
       }
       lowercase__			:		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__":
       _UpperCamelCase   :						Optional[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   :						List[Any]      =    parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
