





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_      :			List[Any]	):
   __lowerCAmelCase								=	SwinaSRConfig()

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

   return config

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

   if name == "norm.weight":
      __lowerCAmelCase								=	'layernorm.weight'
   if name == "norm.bias":
      __lowerCAmelCase								=	'layernorm.bias'

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

   return name

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			int	):
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase								=	orig_state_dict.pop(lowerCAmelCase_	)

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

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

   return orig_state_dict

def   a_    ( lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	get_config(lowerCAmelCase_	)
   __lowerCAmelCase								=	SwinaSRForImageSuperResolution(lowerCAmelCase_	)
   model.eval()

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

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

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

   __lowerCAmelCase								=	model(lowerCAmelCase_	)

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

   __lowerCAmelCase								=	{
       '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'
       ),
   }
   __lowerCAmelCase								=	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__":
     _snake_case  :    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.')

     _snake_case  :    str						    =  parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_snake_case  :    Tuple						    =  importlib.util.find_spec('s3fs') is not None

if _has_safs:
     from .safilesystem import SaFileSystem  # noqa: F401

_snake_case  :    List[compression.BaseCompressedFileFileSystem]						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
     if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
          warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
     fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def   a_    ( lowerCAmelCase_      :			str	):
   if "://" in dataset_path:
      __lowerCAmelCase								=	dataset_path.split('://'	)[1]
   return dataset_path

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem	):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	not is_remote_filesystem(lowerCAmelCase_	)
   if is_local:
      # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
      shutil.move(fs._strip_protocol(lowerCAmelCase_	),	fs._strip_protocol(lowerCAmelCase_	)	)
   else:
      fs.mv(lowerCAmelCase_,	lowerCAmelCase_,	recursive=lowerCAmelCase_	)




def   a_    ( ):
   if hasattr(fsspec.asyn,	'reset_lock'	):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	threading.Lock()

