





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_				:      Union[str, 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_				:      Any,     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_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   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_				:      str,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      int     ):
   __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  :   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.')

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








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		int = 6_5_5_3_6 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		str = "fourier" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,  lowerCAmelCase_			:		Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,  lowerCAmelCase_			:		Tuple[str] = "UNetMidBlock1D" ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		Tuple[int] = (3_2, 3_2, 6_4) ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		int = 8 ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		bool = False ,  )							->						Optional[int]:
     super().__init__()
     __lowerCAmelCase							     =     sample_size

     # time
     if time_embedding_type == "fourier":
        __lowerCAmelCase							     =     GaussianFourierProjection(
            embedding_size=8 ,  set_W_to_weight=lowerCAmelCase_ ,  log=lowerCAmelCase_ ,  flip_sin_to_cos=lowerCAmelCase_					)
        __lowerCAmelCase							     =     2 * block_out_channels[0]
     elif time_embedding_type == "positional":
        __lowerCAmelCase							     =     Timesteps(
            block_out_channels[0] ,  flip_sin_to_cos=lowerCAmelCase_ ,  downscale_freq_shift=lowerCAmelCase_					)
        __lowerCAmelCase							     =     block_out_channels[0]

     if use_timestep_embedding:
        __lowerCAmelCase							     =     block_out_channels[0] * 4
        __lowerCAmelCase							     =     TimestepEmbedding(
            in_channels=lowerCAmelCase_ ,  time_embed_dim=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  out_dim=block_out_channels[0] ,  )

     __lowerCAmelCase							     =     nn.ModuleList([]					)
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     nn.ModuleList([]					)
     __lowerCAmelCase							     =     None

     # down
     __lowerCAmelCase							     =     in_channels
     for i, down_block_type in enumerate(lowerCAmelCase_					):
        __lowerCAmelCase							     =     output_channel
        __lowerCAmelCase							     =     block_out_channels[i]

        if i == 0:
           input_channel += extra_in_channels

        __lowerCAmelCase							     =     i == len(lowerCAmelCase_					) - 1

        __lowerCAmelCase							     =     get_down_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_downsample=not is_final_block or downsample_each_block ,  )
        self.down_blocks.append(lowerCAmelCase_					)

     # mid
     __lowerCAmelCase							     =     get_mid_block(
         lowerCAmelCase_ ,  in_channels=block_out_channels[-1] ,  mid_channels=block_out_channels[-1] ,  out_channels=block_out_channels[-1] ,  embed_dim=block_out_channels[0] ,  num_layers=lowerCAmelCase_ ,  add_downsample=lowerCAmelCase_ ,  )

     # up
     __lowerCAmelCase							     =     list(reversed(lowerCAmelCase_					)					)
     __lowerCAmelCase							     =     reversed_block_out_channels[0]
     if out_block_type is None:
        __lowerCAmelCase							     =     out_channels
     else:
        __lowerCAmelCase							     =     block_out_channels[0]

     for i, up_block_type in enumerate(lowerCAmelCase_					):
        __lowerCAmelCase							     =     output_channel
        __lowerCAmelCase							     =     (
            reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase_					) - 1 else final_upsample_channels
        )

        __lowerCAmelCase							     =     i == len(lowerCAmelCase_					) - 1

        __lowerCAmelCase							     =     get_up_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_upsample=not is_final_block ,  )
        self.up_blocks.append(lowerCAmelCase_					)
        __lowerCAmelCase							     =     output_channel

     # out
     __lowerCAmelCase							     =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,  3_2					)
     __lowerCAmelCase							     =     get_out_block(
         out_block_type=lowerCAmelCase_ ,  num_groups_out=lowerCAmelCase_ ,  embed_dim=block_out_channels[0] ,  out_channels=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  fc_dim=block_out_channels[-1] // 4 ,  )






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		torch.FloatTensor ,  lowerCAmelCase_			:		Union[torch.Tensor, float, int] ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[UNetaDOutput, Tuple]:
     __lowerCAmelCase							     =     timestep
     if not torch.is_tensor(lowerCAmelCase_					):
        __lowerCAmelCase							     =     torch.tensor([timesteps] ,  dtype=torch.long ,  device=sample.device					)
     elif torch.is_tensor(lowerCAmelCase_					) and len(timesteps.shape					) == 0:
        __lowerCAmelCase							     =     timesteps[None].to(sample.device					)

     __lowerCAmelCase							     =     self.time_proj(lowerCAmelCase_					)
     if self.config.use_timestep_embedding:
        __lowerCAmelCase							     =     self.time_mlp(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     timestep_embed[..., None]
        __lowerCAmelCase							     =     timestep_embed.repeat([1, 1, sample.shape[2]]					).to(sample.dtype					)
        __lowerCAmelCase							     =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])					)

     # 2. down
     __lowerCAmelCase							     =     ()
     for downsample_block in self.down_blocks:
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     downsample_block(hidden_states=lowerCAmelCase_ ,  temb=lowerCAmelCase_					)
        down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
        __lowerCAmelCase							     =     self.mid_block(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks					):
        __lowerCAmelCase							     =     down_block_res_samples[-1:]
        __lowerCAmelCase							     =     down_block_res_samples[:-1]
        __lowerCAmelCase							     =     upsample_block(lowerCAmelCase_ ,  res_hidden_states_tuple=lowerCAmelCase_ ,  temb=lowerCAmelCase_					)

     # 5. post-process
     if self.out_block:
        __lowerCAmelCase							     =     self.out_block(lowerCAmelCase_ ,  lowerCAmelCase_					)

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase_					)

