



'''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			) -> int:
  __lowerCamelCase			:			Tuple       			=					SwinaSRConfig()

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

  return config

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

  if name == "norm.weight":
    __lowerCamelCase			:			int       			=					'layernorm.weight'
  if name == "norm.bias":
    __lowerCamelCase			:			Tuple       			=					'layernorm.bias'

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

  return name

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> Any:
  for key in orig_state_dict.copy().keys():
    __lowerCamelCase			:			str       			=					orig_state_dict.pop(_lowerCAmelCase			)

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

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

  return orig_state_dict

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			List[Any]       			=					get_config(_lowerCAmelCase			)
  __lowerCamelCase			:			Tuple       			=					SwinaSRForImageSuperResolution(_lowerCAmelCase			)
  model.eval()

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

  __lowerCamelCase			:			List[str]       			=					126 if 'Jpeg' in checkpoint_url else 256
  __lowerCamelCase			:			Optional[Any]       			=					Compose(
      [
          Resize((image_size, image_size)			),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]			,std=[0.229, 0.224, 0.225]			),
      ]			)
  __lowerCamelCase			:			str       			=					transforms(_lowerCAmelCase			).unsqueeze(0			)

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

  __lowerCamelCase			:			Dict       			=					model(_lowerCAmelCase			)

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

  __lowerCamelCase			:			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'
      ),
  }
  __lowerCamelCase			:			Union[str, Any]       			=					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											=		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											=		parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''





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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""pegasus"""
  a_				   =["""past_key_values"""]
  a_				   ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




  def __init__(					self :      Any     ,      _a :      int=5_0265     ,      _a :      Tuple=1024     ,      _a :      Optional[Any]=12     ,      _a :      int=4096     ,      _a :      str=16     ,      _a :      Union[str, Any]=12     ,      _a :      Optional[Any]=4096     ,      _a :      Optional[int]=16     ,      _a :      Union[str, Any]=0.0     ,      _a :      str=0.0     ,      _a :      List[Any]=True     ,      _a :      Optional[int]=True     ,      _a :      Tuple="gelu"     ,      _a :      Dict=1024     ,      _a :      int=0.1     ,      _a :      Any=0.0     ,      _a :      Union[str, Any]=0.0     ,      _a :      Any=0.02     ,      _a :      Optional[Any]=0     ,      _a :      str=False     ,      _a :      Tuple=0     ,      _a :      Optional[int]=1     ,      _a :      Union[str, Any]=1     ,      **_a :      Optional[int]     ,      )   ->   Any:
    __lowerCamelCase			:			Union[str, Any]       			=					vocab_size
    __lowerCamelCase			:			int       			=					max_position_embeddings
    __lowerCamelCase			:			Union[str, Any]       			=					d_model
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_ffn_dim
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_layers
    __lowerCamelCase			:			List[Any]       			=					encoder_attention_heads
    __lowerCamelCase			:			Optional[int]       			=					decoder_ffn_dim
    __lowerCamelCase			:			int       			=					decoder_layers
    __lowerCamelCase			:			Tuple       			=					decoder_attention_heads
    __lowerCamelCase			:			Optional[Any]       			=					dropout
    __lowerCamelCase			:			Tuple       			=					attention_dropout
    __lowerCamelCase			:			int       			=					activation_dropout
    __lowerCamelCase			:			Dict       			=					activation_function
    __lowerCamelCase			:			List[Any]       			=					init_std
    __lowerCamelCase			:			List[str]       			=					encoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					decoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					use_cache
    __lowerCamelCase			:			Tuple       			=					encoder_layers
    __lowerCamelCase			:			int       			=					scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=_a     ,      eos_token_id=_a     ,      is_encoder_decoder=_a     ,      decoder_start_token_id=_a     ,      forced_eos_token_id=_a     ,      **_a     ,      )




  @property
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    return self.encoder_attention_heads




  @property
  def 		_lowercase	(					self :      Any  )   ->   int:
    return self.d_model






