





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "dpt"

 def __init__(self							:  Optional[int]			,					UpperCAmelCase_							:  Any=768			,					UpperCAmelCase_							:  Optional[Any]=12			,					UpperCAmelCase_							:  Union[str, Any]=12			,					UpperCAmelCase_							:  Tuple=3_072			,					UpperCAmelCase_							:  Tuple="gelu"			,					UpperCAmelCase_							:  Dict=0.0			,					UpperCAmelCase_							:  List[str]=0.0			,					UpperCAmelCase_							:  List[Any]=0.02			,					UpperCAmelCase_							:  Any=1E-1_2			,					UpperCAmelCase_							:  Tuple=384			,					UpperCAmelCase_							:  Dict=16			,					UpperCAmelCase_							:  str=3			,					UpperCAmelCase_							:  Any=False			,					UpperCAmelCase_							:  Dict=True			,					UpperCAmelCase_							:  Dict=[2, 5, 8, 11]			,					UpperCAmelCase_							:  List[str]="project"			,					UpperCAmelCase_							:  Dict=[4, 2, 1, 0.5]			,					UpperCAmelCase_							:  Dict=[96, 192, 384, 768]			,					UpperCAmelCase_							:  Dict=256			,					UpperCAmelCase_							:  List[Any]=-1			,					UpperCAmelCase_							:  List[str]=False			,					UpperCAmelCase_							:  List[Any]=True			,					UpperCAmelCase_							:  List[str]=0.4			,					UpperCAmelCase_							:  str=255			,					UpperCAmelCase_							:  Optional[int]=0.1			,					UpperCAmelCase_							:  int=[1, 1_024, 24, 24]			,					UpperCAmelCase_							:  Dict=[0, 1]			,					UpperCAmelCase_							:  List[str]=None			,					**UpperCAmelCase_							:  Optional[Any]			,					)			->Tuple:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   lowerCamelCase__: Optional[int]         =hidden_size
   lowerCamelCase__: List[str]         =is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("Initializing the config with a `BiT` backbone.")
       lowerCamelCase__: str         ={
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
       }
       lowerCamelCase__: Union[str, Any]         =BitConfig(**UpperCAmelCase_)
     elif isinstance(UpperCAmelCase_			,					UpperCAmelCase_):
       logger.info("Initializing the config with a `BiT` backbone.")
       lowerCamelCase__: Optional[Any]         =BitConfig(**UpperCAmelCase_)
     elif isinstance(UpperCAmelCase_			,					UpperCAmelCase_):
       lowerCamelCase__: int         =backbone_config
     else:
       raise ValueError(
           F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""")

     lowerCamelCase__: Optional[int]         =backbone_featmap_shape
     lowerCamelCase__: Optional[Any]         =neck_ignore_stages

     if readout_type != "project":
       raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode.")
   else:
     lowerCamelCase__: List[Any]         =None
     lowerCamelCase__: Optional[int]         =None
     lowerCamelCase__: Optional[Any]         =[]

   lowerCamelCase__: Union[str, Any]         =num_hidden_layers
   lowerCamelCase__: List[str]         =num_attention_heads
   lowerCamelCase__: Any         =intermediate_size
   lowerCamelCase__: Dict         =hidden_act
   lowerCamelCase__: Optional[int]         =hidden_dropout_prob
   lowerCamelCase__: Dict         =attention_probs_dropout_prob
   lowerCamelCase__: Any         =initializer_range
   lowerCamelCase__: Optional[int]         =layer_norm_eps
   lowerCamelCase__: Dict         =image_size
   lowerCamelCase__: str         =patch_size
   lowerCamelCase__: Any         =num_channels
   lowerCamelCase__: Dict         =qkv_bias
   lowerCamelCase__: Dict         =backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']")
   lowerCamelCase__: int         =readout_type
   lowerCamelCase__: str         =reassemble_factors
   lowerCamelCase__: int         =neck_hidden_sizes
   lowerCamelCase__: Dict         =fusion_hidden_size
   lowerCamelCase__: List[str]         =head_in_index
   lowerCamelCase__: Dict         =use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowerCamelCase__: Dict         =use_auxiliary_head
   lowerCamelCase__: List[str]         =auxiliary_loss_weight
   lowerCamelCase__: Union[str, Any]         =semantic_loss_ignore_index
   lowerCamelCase__: int         =semantic_classifier_dropout







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: List[str]         =copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowerCamelCase__: List[Any]         =self.backbone_config.to_dict()

   lowerCamelCase__: Union[str, Any]         =self.__class__.model_type
   return output



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 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    lowercase__	: List[str]     		=   4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    lowercase__	: Optional[int]     		=   4
    lowercase__	: Optional[Any]     		=   48
    lowercase__	: int     		=   """pixelshuffle_aux"""
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    lowercase__	: List[str]     		=   [6, 6, 6, 6]
    lowercase__	: Any     		=   60
    lowercase__	: Tuple     		=   [6, 6, 6, 6]
    lowercase__	: Dict     		=   """pixelshuffledirect"""
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    lowercase__	: Tuple     		=   4
    lowercase__	: Any     		=   """nearest+conv"""
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    lowercase__	: str     		=   1
    lowercase__	: Optional[int]     		=   1
    lowercase__	: Optional[int]     		=   1_26
    lowercase__	: Any     		=   7
    lowercase__	: int     		=   255.0
    lowercase__	: List[Any]     		=   """"""

  return config

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






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

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

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

  return name

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  for key in orig_state_dict.copy().keys():
    lowercase__	: str     		=   orig_state_dict.pop(lowercase_ )

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

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

  return orig_state_dict

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Dict     		=   get_config(lowercase_ )
  lowercase__	: Any     		=   SwinaSRForImageSuperResolution(lowercase_ )
  model.eval()

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

  lowercase__	: Optional[int]     		=   1_26 if """Jpeg""" in checkpoint_url else 2_56
  lowercase__	: Union[str, Any]     		=   Compose(
      [
          Resize((image_size, image_size) ),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]			,			std=[0.229, 0.224, 0.225] ),
      ] )
  lowercase__	: Dict     		=   transforms(lowercase_ ).unsqueeze(0 )

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

  lowercase__	: Union[str, Any]     		=   model(lowercase_ )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: Optional[Any]     		=   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:
    lowercase__	: List[str]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: 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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: 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!""" )

  lowercase__	: 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"""
      ),
  }
  lowercase__	: str     		=   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__":
     lowerCamelCase__     :		List[str]        = 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.""")

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






